Behavioral Sport Psychology
James K. Luiselli · Derek D. Reed
Editors
Behavioral Sport Psychology
Evidence-Based Approaches
to Performance Enhancement
123
Editors
James K. Luiselli
May Institute
Pacella Park Drive 41
Randolph, MA 02368, USA
Derek D. Reed
Department of Applied Behavioral Science
University of Kansas
Sunnyside Avenue 1000
Lawrence, KS 66045, USA
ISBN 978-1-4614-0069-1 e-ISBN 978-1-4614-0070-7
DOI 10.1007/978-1-4614-0070-7
Springer New York Dordrecht Heidelberg London
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Preface
Sport psychology is a topic of growing interest. Many professionals read journals
such as The International Journal of Sports, Journal of Sport Behavior, Journal
of Applied Sport Psychology, Research Quarterly for Exercise and Sport, and
The Sport Psychologist. Division 47 of the American Psychological Association
is devoted to “the scientific, educational, and clinical foundations of exercise and
sport psychology.” The North American Society for the Psychology of Sport and
Physical Activity (NASPSPA) and the Association for the Advancement of Applied
Sport Psychology (AAASP) convene conferences each year to present scientific
findings and new developments in a rapidly expanding field. The AAASP and other
organizations also qualify professionals as certified sport and exercise psychology
consultants. Finally, a visit to any bookstore will reveal the lay public’s fascina-
tion with sports, as revealed in numerous self-help books and guides to perfecting
athletic performance.
Behavioral psychologists have studied sport psychology for more than three
decades (Martin, Thompson, & Regehr, 2004). Applied behavior analysis (ABA),
in particular, has been an instrumental approach to behavioral coaching in
many sports, including baseball (Osborne, Rudrud, & Zezoney, 1990), basketball
(Kladopoulos & McComas, 2001), figure skating (Ming & Martin, 1996), foot-
ball (Stokes, Luiselli, & Reed, 2010; Stokes, Luiselli, Reed, & Fleming, 2010;
Ward & Carnes, 2002), ice hockey (Rogerson & Hrycaiko, 2002), soccer (Brobst &
Ward, 2002), swimming (Hume & Crossman, 1992), and tennis (Allison & Ayllon,
1980). ABA stresses the application of learning theory principles, objective mea-
surement of athletic skills, controlled outcome evaluation, and socially significant
behavior change. Cognitive behavior therapy, or CBT, also has been a dominant
approach to psychological intervention in sports (Meyers, Whelan, & Murphy,
1996; Weinberg & Comar, 1994). CBT addresses athletic performance through
cognitive-change methods combined with behavioral practice and environmental
modifications. Additionally, there have been many advances in sports-related behav-
ioral, cognitive, and neuropsychological assessment methods (Donahue, Silver,
Dickens, Covassin, & Lancer, 2007; Webbe & Salinas, 2010).
Behavioral Sport Psychology: Evidence-Based Approaches to Performance
Enhancement was written for academic professionals, practicing psychologists and
consultants, and general readers interested in athletics. We focused on several
v
vi Preface
criteria when selecting chapters for the book. First, our objective was to assem-
ble chapters authored by recognized experts in sport psychology and performance
management. We also wanted chapters to reflect the most contemporary clinical and
experimental findings. Most important, the chapters contain many recommendations
for improving behavioral sport psychology applications, advancing research, and
refining the performance of youth, amateur, and elite athletes. A book of this type
cannot cover every relevant topic, but hopefully, we have addressed many of the
dominant areas that make up the sport psychology landscape.
We are, first and foremost, clinical psychologists, but also avid sport enthusiasts.
Dr. Luiselli acknowledges the many coaches who shaped his athletic pursuits in mid-
dle school, high school, and college: James C. Murphy, Michael Donato, Richard
Sterndale, Jerry Splaine, Louis Gnerre, Rocky Carzo, and Herb Erikson. My father,
the late James “Jack the Barber” Luiselli, was my finest coach, always there in t he
stands, consistently positive, and helping me in ways he probably never realized –
I am forever indebted to him. I thank my wife, Dr. Tracy Evans Luiselli, for enduring
my tales of athletic conquests long gone and commiserating with me during Patriots,
Celtics, Bruins, and Red Sox games. Our daughter, Gabrielle Luiselli, has given us
so much pleasure watching her perform on the ice and landing those combination
jumps. And to our son, Thomas Luiselli, your exploits on the hockey rink and the
lacrosse field fill us with pride – you and your sister are true champions!
Dr. Reed acknowledges his father, David Reed, for being a patient trainer, an
understanding coach, and most importantly, an unconditional fan and supporter.
I thank my mentors, Dr. Thomas Critchfield, Dr. Brian Martens, and my co-editor,
Dr. James Luiselli, for supporting my efforts to study the behavioral processes
underlying athletic performance. Finally, I thank my wife, Dr. Florence DiGennaro
Reed, for humoring me when I claim that my playing of football video games is for
the sake of science.
References
Allison, M. G., & Ayllon, T. (1980). Behavioral coaching in the development of skills in football,
gymnastics, and tennis. Journal of Applied Behavior Analysis, 13, 297–314.
Brobst, B., & Ward, P. (2002). Effects of public posting, goal setting, and oral feedback on the
skills of female soccer players. Journal of Applied Behavior Analysis, 35, 247–257.
Donahue, B., Silver, N. C., Dickens, Y., Covassin, T., & Lancer, K. (2007). Development and
initial psychometric evaluation of the sport interference checklist. Behavior Modification, 31,
937–957.
Hume, K. M., & Crossman, J. (1992). Musical reinforcement of practice behaviors among
competitive swimmers. Journal of Applied Behavior Analysis, 25, 665–670.
Kladopoulos, C. N., & McComas, J. J. (2001). The effects of form training on foul-shooting perfor-
mance in members of a women’s college basketball team. Journal of Applied Behavior Analysis,
34, 329–332.
Martin, G. L., Thompson, K., & Regehr, K. (2004). Studies using single-subject designs in sport
psychology: 30 years of research. The Behavior Analyst, 27, 123–140.
Meyers, A., Whelan, J., & Murphy, S. (1996). Cognitive behavioral strategies in athletic perfor-
mance enhancement. Progress in Behavior Modification, 30, 137–164.
Preface vii
Ming, S., & Martin, G. L. (1996). Single-subject evaluation of a self-talk package for improving
figure skating performance. The Sport Psychologist, 10, 227–238.
Osborne, K., Rudrud, E., & Zezoney, F. (1990). Improved curveball hitting through the enhance-
ment of visual cues. Journal of Applied Behavior Analysis, 23, 371–377.
Rogerson, L. J., & Hrycaiko, D. W. (2002). Enhancing competitive performance of ice hockey goal
tenders using centering and self-talk. Journal of Applied Sport Psychology, 14, 14–26.
Stokes, J. V., Luiselli, J. K., & Reed, D. D. (2010). A behavioral intervention for teaching tackling
skills to high school football athletes. Journal of Applied Behavior Analysis, 43, 509–512.
Stokes, J. V., Luiselli, J. K., Reed, D. D., & Fleming, R. K. (2010). Behavioral coaching to improve
offensive line blocking skills of high school football athletes. Journal of Applied Behavior
Analysis, 43, 463–472.
Ward, P., & Carnes, M. (2002). Effects of posting self-set goals on collegiate football players’ skill
execution during practice and games. Journal of Applied Behavior Analysis, 35, 1–12.
Webbe, F. M., & Salinas, C. (2010). Pediatric sport neuropsychology. In A. S. Davis (Ed.),
Handbook of pediatric neuropsychology. New York: Springer.
Weinberg, R., & Comar, W. (1994). The effectiveness of psychological interventions in competitive
sport. Sports Medicine, 18, 406–418.
This is Blank Page Integra viii
Contents
Part I Introduction
1 Overview of Behavioral Sport Psychology .............. 3
Garry L. Martin and Kendra Thomson
Part II Assessment and Measurement
2 Actigraphy: The Ambulatory Measurement of Physical Activity .. 25
Warren W. Tryon
3 Quantitative Analysis of Sports .................... 43
Derek D. Reed
4 Single-Case Evaluation of Behavioral Coaching Interventions ... 61
James K. Luiselli
5 Cognitive Assessment in Behavioral Sport Psychology ....... 79
Bradley Donohue, Yani L. Dickens, and Philip D. Del Vecchio III
Part III Performance Enhancement
6 Goal Setting and Performance Feedback ............... 99
Phillip Ward
7 Cognitive–Behavioral Strategies .................... 113
Jeffrey L. Brown
8 Establishing and Maintaining Physical Exercise ........... 127
Christopher C. Cushing and Ric G. Steele
9 Behavioral Momentum in Sports ................... 143
Henry S. Roane
Part IV Special Topics
10 Developing Fluent, Efficient, and Automatic Repertoires
of Athletic Performance ........................ 159
Brian K. Martens and Scott R. Collier
ix
x Contents
11 Sport Neuropsychology and Cerebral Concussion .......... 177
Frank M. Webbe
12 Aggression in Competitive Sports: Using
Direct Observation to Evaluate Incidence and Prevention
Focused Intervention .......................... 199
Chris J . Gee
13 Behavioral Effects of Sport Nutritional Supplements: Fact
or Fiction? ................................ 211
Stephen Ray Flora
14 Cognitive–Behavioral Coach Training: A Translational
Approach to Theory, Research, and Intervention .......... 227
Ronald E. Smith and Frank L. Smoll
15 Conclusions and Recommendations: Toward
a Comprehensive Framework of Evidenced-Based
Practice with Performers ........................ 249
Gershon Tenenbaum and Lael Gershgoren
Index ..................................... 263
Contributors
Jeffrey L. Brown Harvard Medical School, Boston, MA, USA,
jeffrey_bro[email protected]ard.edu
Scott R. Collier College of Health Sciences, Appalachian State University,
Christopher C. Cushing Clinical Child Psychology Program, University
Philip D. Del Vecchio III Claremont Graduate University, Claremont, CA, USA,
Yani L. Dickens University of Nevada, Reno, NV, USA, ydickens@unr.edu
Bradley Donohue University of Nevada, Las Vegas, NV, USA,
bradley[email protected]
Stephen Ray Flora Youngstown State University, Youngstown, OH, USA,
Chris J. Gee Department of Exercise Sciences, University of Toronto, Toronto,
Lael Gershgoren Florida State University, Tallahassee, FL, USA, [email protected]
James K. Luiselli May Institute, Randolph, MA, USA, [email protected]
Brian K. Martens Department of Psychology, Syracuse University, Syracuse,
NY, USA, bkmarten@syr.edu
Garry L. Martin University of Manitoba, Winnipeg, MB, Canada,
Derek D. Reed Department of Applied Behavioral Science, University of Kansas,
Henry S. Roane Department of Pediatrics and Psychiatry, SUNY Upstate
Ronald E. Smith University of Washington, Seattle, WA, USA, resmith@uw.edu
xi
xii Contributors
Frank L. Smoll University of Washington, Seattle, WA, USA, smoll@uw.edu
Ric G. Steele Clinical Child Psychology Program, University of Kansas,
Gershon Tenenbaum Florida State University, Tallahassee, FL, USA,
Kendra Thomson University of Manitoba, Winnipeg, MB, Canada,
Warren W. Tryon Fordham University, Bronx, NY, USA, [email protected]
Phillip Ward The Ohio State University, Columbus, OH, USA,
Frank M. Webbe Florida Institute of Technology, Melbourne, FL, USA,
webbe@fit.edu
Part I
Introduction
Chapter 1
Overview of Behavioral Sport Psychology
Garry L. Martin and Kendra Thomson
The term behavior analysis refers to the scientific study of laws that govern the
behavior of human beings and other animals (Pear, 2001). Behavioral sport psychol-
ogy involves the use of behavior analysis principles and techniques to enhance the
performance and satisfaction of athletes and others associated with sports (Martin &
Tkachuk, 2000). In this chapter, we trace the early development of the field, high-
light five characteristics that tend to be evident in research and current practice in
behavioral sport psychology, and summarize nine major areas of application in this
field to date.
The Early Development of Behavioral Sport Psychology
The field of sport psychology in general began to acquire status in the 1960s with
the formation of the International Society of Sport Psychology in 1965, The North
American Society for the Psychology of Sport and Physical Activity in 1967, and
The Canadian Society for Psychomotor Learning and Sport Psychology in 1969.
A prominent behaviorally oriented individual in this early history was Brent Rushall.
In 1969, Rushall and Pettinger published a comparison of several different rein-
forcement contingencies on the amount of swimming performed by members of
an age-group swimming team. In 1972, Rushall teamed up with physical educator
Daryl Siedentop to publish The Developmental and Control of Behavior in Sport and
Physical Education. This book was written within an operant conditioning frame-
work, and it contained numerous practical strategies for teaching new sport skills,
motivating sports persons to practice existing skills at a high level, and generaliz-
ing practice skills to competitive settings. In 1974, Thom McKenzie and Rushall
published the first r esearch in the Journal of Applied Behavior Analysis that took
place in a sport setting, and it was the first study in behavioral sport psychology
to use a single-subject design. Their research demonstrated the effectiveness of a
self-monitoring package for improving practice performance of young competitive
swimmers. Two other prominent individuals i n behavioral sport psychology in the
G.L. Martin (B)
University of Manitoba, Winnipeg, MB, Canada
e-mail: [email protected]
3
J.K. Luiselli, D.D. Reed (eds.), Behavioral Sport Psychology,
DOI 10.1007/978-1-4614-0070-7_1,
C
Springer Science+Business Media, LLC 2011
4 G.L. Martin and K. Thomson
1970s were Ron Smith and Frank Smoll at the University of Washington, where they
conducted behavioral assessments and interventions in youth sports (for a review,
see Smith, Smoll, & Christensen, 1996). Rushall, Siedentop, McKenzie, Smith, and
Smoll were the early leaders for behaviorally oriented sport psychologists.
During the late 1970s and the early 1980s, publications in behavioral sport
psychology included the following: (a) single-subject evaluations of strategies to
improve performance of youth athletes in football, gymnastics, tennis, swimming,
soccer, and figure skating, and college athletes in volleyball, baseball, basketball,
and soccer (for a review of these studies, see Martin, Thompson, & Regehr, 2004);
(b) an insightful book that offered a Skinnerian analysis of the contingencies
that deter and promote participation in sports (Dickinson, 1977); (c) articles that
described and examined behavioral strategies for coaches of young athletes (e.g.,
see Martin & Hrycaiko, 1983; Rushall & Smith, 1979; Smith, Smoll, & Curtis,
1979; Smoll, Smith, & Curtis, 1978); and (d) research on cognitive–behavioral
strategies for improving athletic performance of adult athletes (e.g., Desiderato &
Miller, 1979; Gravel, Lemieux, & Ladouceur, 1980; Kirchenbaum, Ordman,
Tomarken, & Holtzbauer, 1982; and Weinberg, Seabourne, & Jackson, 1981). Many
of the early studies were contained in a book of readings by Martin and Hrycaiko
(1983). By the mid-1980s, behavioral sport psychology had a s trong foundation
and a promising future.
Prominent Characteristics of Behavioral Sport Psychology
Preliminary to a discussion of the characteristics of behavioral sport psychology is a
clarification of the meaning of the terms behavior and stimulus. In general, behavior
is anything that a person says or does. Technically, behavior is any muscular, glan-
dular, or electrical activity of an organism (Martin & Pear, 2011). Commonly used
synonyms for behavior include “response,” “action,” “reaction,” “performance,”
and “activity.” Overt behaviors can be easily monitored by others, and examples
are swimming, throwing a basketball, doing a spin in figure skating, yelling at a
teammate, and arguing with a coach. Covert behaviors refer to activities that are
internal and cannot be readily monitored by observers. Examples include a gymnast
thinking, “I hope I don’t fall”; a figure skater feeling nervous (e.g., increased heart
rate, and rapid breathing) just before performing; and a diver mentally rehearsing
a dive just before performing it. (Thinking and feeling are discussed later.) Stimuli
(plural of stimulus) are the physical variables in one’s immediate surroundings that
impinge on one’s sense receptors and that can affect one’s behavior (Martin & Pear,
2011). Examples of external stimuli include the behavior and physical appearance of
the coach and other athletes in the immediate vicinity, the characteristics of the play-
ing field or facility, and the actions and sounds of spectators. One’s private behavior,
such as an athlete’s feelings, self-talk, and imagery (discussed later in this chapter
and in Chapter 8), can also serve as internal stimuli to influence subsequent behav-
ior. When a stimulus precedes and influences a behavior, the stimulus is often called
a “prompt,” “signal,” or “cue.”
1 Overview of Behavioral Sport Psychology 5
The first characteristic of behavioral sport psychology involves identifying target
behaviors of athletes and/or coaches to be improved, defining those behaviors in a
way so that they can be reliably measured, and using changes in the behavioral
measure as the best indicator of the extent to which the recipient of an intervention
is being helped (Martin, 2011). This characteristic is discussed further in Chapters 2,
3, and 5.
A second characteristic is that behavioral psychology treatment procedures and
techniques are based on the principles and procedures of Pavlovian (or respondent)
and operant conditioning and are ways of rearranging the s timuli that occur as
antecedents and consequences of an athlete’s behavior. Pavlovian conditioning is
very important in influencing the physiological components of our emotions that we
describe as our feelings.
Suppose, for example, that a young figure skater experiences several bad falls
while attempting to learn the triple-toe jump, with each fall causing feelings of
fear and considerable pain. The principle of Pavlovian conditioning states that if
a neutral stimulus (practicing the triple-toe jump) is closely followed by an uncon-
ditioned stimulus (a bad fall), which elicits an unconditioned response (feelings of
fear), then the previously neutral stimulus (practicing the triple-toe jump) will also
tend to elicit that response (feelings of fear). The model for Pavlovian condition-
ing is shown in the top half of Fig. 1.1. Fortunately, a conditioned reflex (a CS–CR
sequence), as illustrated in Fig. 1.1, can be eliminated through the process known as
Pavlovian extinction. As illustrated in the bottom half of Fig. 1.1, the Pavlovian
extinction procedure is the presentation of the CS without further pairings with
the US, and the result is that the CS eventually loses the ability to elicit the CR.
Pavlovian procedures for influencing desirable emotional reactions (e.g., calmness
and relaxation) and for eliminating undesirable emotional reactions (e.g., fear
and anxiety) are important intervention strategies in behavioral sport psychology
(Martin, 2011).
Pavlovian conditioning is all about learned and unlearned reflexes – involuntary
responses to prior stimuli. However, much of our behavior is referred to as volun-
tary. Examples of voluntary behavior among athletes include passing a basketball,
performing a figure skating jump, swimming the backstroke, listening to a coach,
and talking to a teammate. Skinner (1953) referred to such activities as operant
behavior – behavior that operates on the environment to produce consequences, and,
in turn, is influenced by those consequences. While Pavlovian conditioning causes
individuals t o involuntarily respond to stimuli due to pairings of antecedent stim-
uli before the response, operant conditioning teaches individuals to emit voluntary
behavior to antecedent stimuli due to consequences for those behaviors. A simplified
sport example is illustrated in Fig. 1.2. Suppose that a golfer is practicing putts of
approximately 6–8 feet in length. At some places on the practice putting green, the
surface around the hole is flat. At another place on the putting green, the surface
around the hole is sloped. In the presence of the cues provided by a flat green, the
golfer’s behavior of aiming and hitting the ball directly at the hole will be positively
reinforced by making the putt. In the presence of the cues when there is a slope on
the green, however, the golfer’s behavior of aiming and hitting the putt directly at
6 G.L. Martin and K. Thomson
Pavlovian Conditioning
Procedure:
Pair neutral stimulus and unconditioned stimulus
NS (approaching take-off position for triple-toe jump)
Many
Pairings
Result: Neutral stimulus acquires ability to elicit response that was elicited by the
unconditioned stimulus.
CS (approaching take-off position for triple-toe jump)
US (bad fall) UR (fear)
CR (fear)
NS = neutral stimulus
US = unconditioned stimulus
UR = unconditioned response
CS = conditioned stimulus
CR = conditioned response
Pavlovian Extinction
Procedure: Present conditioned stimulus repeatedly without further pairings with the
unconditioned stimulus.
CS (approaching take-off position for triple-toe jump) CR (fear)
Result: Conditioned stimulus loses ability to elicit conditioned response
NS ( approaching take-off position for triple-toe jump) (No fear reaction)
Fig. 1.1 An illustration of Pavlovian conditioning of fear while a figure skater is practicing a jump,
and an illustration of Pavlovian extinction of that fear (adapted with permission from illustrations
in Martin, 2011)
the hole will encounter operant extinction in that the behavior will not be reinforced
by making the putt. As illustrated in Fig. 1.2, after several trials, the golfer learns to
putt directly at the hole only when putting at a flat green.
Operant conditioning principles and procedures include methods for teaching
new skills (e.g., shaping and chaining), s trategies for maintaining existing skills and
behaviors at desired levels (e.g., intermittent reinforcement), procedures for bring-
ing new skills under the control of appropriate cues (e.g., stimulus discrimination
training, modeling, and fading), and strategies for decreasing unwanted behaviors
(e.g., operant extinction, response-cost punishment, and reinforcement of desirable
alternative behavior).
Pavlovian and operant conditioning procedures are summarized with many sport
examples in a single chapter in a book by Martin (2011) and are described in
1 Overview of Behavioral Sport Psychology 7
Stimulus Behavior Consequence
Flat green
Aiming and hitting
putt directly at hole
Reinforcer
(making putt)
Several
Trials
Sloped green
Aiming and hitting
putt directly at hole
No reinforcer
(ball rolls away
from hole)
The Resulting
Stimulus Discrimination
Stimulus Behavior
on flat greens
golfer aims and putts directly
at the hole
on sloped greens
golfer does not aim or putt directly
at the hole
Fig. 1.2 An illustration of stimulus discrimination training involving operant behavior of a golfer
(adapted with permission from an illustration in Martin, 2011)
considerable detail in many chapters by Martin and Pear (2011). This second
characteristic is discussed further in Chapters 3, 6, 7, 9, and 10.
A third characteristic of behavioral sport psychology is that many of the
interventions with athletes have been developed by practitioners with a cognitive–
behavioral orientation (e.g., see Smoll & Smith, 2010; Zinsser, Bunker, & Williams,
2006). Cognitive–behavior therapy typically focuses on cognitive processes fre-
quently referred to as believing, thinking, expecting, and perceiving. Over the years,
cognitively oriented sport psychologists have provided considerable evidence that
inappropriate thinking by athletes can lead to poor performance and that appropriate
or positive thinking can lead to good performance (Zinsser et al., 2006). From an
applied behavior analysis (ABA) perspective, cognitive processes are referred to
as covert verbalizations and/or imagery (both of which are discussed later in this
chapter), and it is assumed that the behavioral principles and techniques that apply
to overt behaviors are also applicable to covert behaviors (Martin & Pear, 2011).
Cognitive–behavioral interventions in sports are discussed further in Chapters 5, 8,
and 15.
A fourth characteristic of this approach is that researchers have relied heavily
on the use of single-subject research designs. As expressed by Hrycaiko and Martin
(1996) and Virués-Ortega and Martin (2010), single-subject designs have a number
of features that render them “user-friendly” for practitioners to evaluate interven-
tions in sport settings, including the following: (a) a focus on individual athletic
performance across several practices and/or competitions; (b) acceptability by ath-
letes and coaches because no control group is needed, few participants are needed,
and sooner or later all participants receive the intervention; (c) easy adaptability
to assess a variety of interventions in practices and/or competitions; and (d) effec-
tiveness assessed through direct measures of sport-specific behaviors (e.g., jumps
landed by figure s katers) or outcomes of behaviors (e.g., points scored by basketball
8 G.L. Martin and K. Thomson
players). For a review of research using single-subject designs in sport psychology,
from the initial study by McKenzie and Rushall in 1974 through the next 30 years,
see Martin et al. (2004). Single-subject designs are discussed further in Chapter 4.
A final characteristic of a behavioral approach, whether with athletes and coaches
or with other populations, is that it places high value on accountability for everyone
involved in the design, implementation, and evaluation of an intervention (Martin &
Pear, 2011). In ABA, the term social validation refers to procedures to ensure that
the techniques employed by a practitioner are s elected and applied in the best inter-
ests of the clients. In behavioral sport psychology, social validation requires that
the practitioner constantly seek answers to three questions: (a) What do the ath-
letes (and perhaps the coach and parents) think about the goals of the intervention?
(b) What do they think about the procedures recommended by the practitioner?
(c) What do they think about the results produced by those procedures? Also, behav-
ioral sport psychologists need to be aware of and behave consistently with the set
of ethical principles to guide the actions of sport psychologists published in 1995
by the Association for the Advancement of Applied Sport Psychology, which, in
2006, became the Association for Applied Sport Psychology (AASP). (For the eth-
ical principles of AASP, see the website http://appliedsportpsych.org.) Additional
discussion of topics relevant to this characteristic can be found in Chapters 11, 12,
13, 14, and 15.
Major Areas of Application of Behavioral Sport Psychology
Motivating Practice and Fitness Training
Webster’s unabridged dictionary defines motive as “some inner drive that causes a
person to act in a certain way,” and many people conceptualize motivation as some
“thing” within us that affects behavior. A behavioral approach, on the other hand,
encourages the use of the verb “to motivate” which has the advantage of providing
coaches and athletes with a variety of strategies for motivating practice perfor-
mance and endurance and fitness activities. Martin (2011) described the details of a
behavioral approach to the topic of motivation and athletic performance, and sum-
marized a variety of strategies for arranging antecedents and/or consequences to
motivate athletic behavior in a variety of settings. For example, consider the prob-
lem of motivating speed skaters to work hard in practices. Members of the Manitoba
Provincial Speed Skating Team, ranging in age from 12 to 17 years, were preparing
for the Canada Winter Games. Three of the skaters, however, showed consider-
able off-task behavior and completed only 85% of the skating drills assigned by
the coach. With the help of Connie Wanlin, a master’s student at the University
of Manitoba, the three skaters agreed to participate in a project to improve their
motivation. The skaters agreed to set weekly written goals and daily goals for num-
ber of laps skated and practice drills completed, to record their daily performance
in log books, and to meet with Connie once a week to discuss their progress and
receive feedback. During the intervention, which lasted for several weeks, the three
1 Overview of Behavioral Sport Psychology 9
skaters showed an average of 73% increase in the number of laps skated per practice,
and they completed an average of 98% of the drills assigned by the coach. Racing
times obtained in practices and competitions improved for all three skaters (Wanlin,
Hrycaiko, Martin, & Mahon, 1997). Additional examples in this area include moti-
vating young competitive swimmers (Critchfield & Vargas, 1991), adult novice
rowers (Scott, Scott, Bedic, & Dowd, 1999), and adult recreational athletes per-
forming a gymnasium triathlon (Thelwell & Greenlees, 2003). Detailed discussion
of motivational procedures is presented in Chapters 6, 7, 9, and 10.
Teaching New Sport Skills
During the past 50 years, behavioral researchers have investigated a variety of
behavioral principles and techniques for helping individuals in all walks of life to
learn new skills, develop persistence, and eliminate bad habits. As described by
Martin and Pear (2011), thousands of research reports have demonstrated the value
of these principles and techniques for improving a wide variety of behaviors of
thousands of individuals in diverse settings. It should not be surprising, then, that an
important area of application of behavioral sport psychology is teaching new skills
to athletes and/or coaches. In a review of 30 years of research using single-subject
designs in sport psychology, 72% of the studies focused on improving athletic skills
of athletes in a variety of sports (Martin et al., 2004). Examples include improving
free throw shooting form in basketball ( Kladopoulos & McComas, 2001), improv-
ing the correctness of compulsory figures in figure skating (Ming & Martin, 1996),
improving offensive blocking in youth football (Allison & Ayllon, 1980), increas-
ing arm extension in pole vaulting (Scott & Scott, 1997), teaching golf to beginners
(Simek & O’Brien, 1981), improving positioning and tackling of linebackers in col-
lege football (Ward & Carnes, 2002), increasing correct tags of inline roller speed
skaters (Anderson & Kirkpatrick, 2002), and improving freestyle and backstroke
turns in youth swimming (Hazen, Johnstone, Martin, & Skrikameswaran, 1990).
For some details of an example, consider the problem of teaching novice tennis
players to serve. In the Juniper High School tennis class, Linda Hill had devoted
three classes in a row to instruction on how to serve. After each class, each player
was given the chance to practice while Coach Hill observed and pointed out errors.
With this strategy, the novice players showed little improvement and averaged only
13% correct across service attempts. With the help of Hillary Buzas, a doctoral can-
didate in clinical psychology at Georgia State University, Coach Hill agreed to try
a different strategy. First, the specific components of the serve were listed and dis-
cussed with the players. Next, when the players practiced, Coach Hill watched for
and praised components (from the checklist) that were performed correctly or near
correctly. When an error occurred, the coach did not comment on it in any way.
This approach, which involved the behavioral procedure of shaping, produced an
improvement from 13% during baseline observations to almost 50% correct perfor-
mance in only a few sessions. Moreover, the young players enjoyed it more and
10 G.L. Martin and K. Thomson
were eager to practice their skills (Buzas & Ayllon, 1981). For further discussion of
behavioral procedures for teaching new skills, see Chapters 6, 7, and 11.
Decreasing Persistent Errors in Sport Skills
Even after considerable practice, many young athletes will continue to make errors
in the execution of athletic skills. As described by Martin (2011), there are numer-
ous reasons why errors are repeated. Persistent errors in skills made by beginning
athletes might be due to imitation of other young athletes who are making the same
errors; lack of focus on the appropriate antecedent cues, as a strategy to obtain atten-
tion from the coach; lack of reinforcement for correct performance especially when
the correct performance requires a lot of effort; and accidental positive reinforce-
ment of an error when the young athlete is successful in spite of the error. Regarding
the last point, when a skill results in early success for a young athlete, all of the com-
ponents of that skill are s trengthened, even if one of the components is flawed. For
example, in a youth competitive swim team composed of 9- and 10-year-olds, if the
swimmers swim 500 m of f reestyle during a practice (a common occurrence), an
error in their freestyle stroke will be repeated several hundred times per practice.
Thus, it is not surprising that certain errors are difficult to decrease. Sandra Koop,
a doctoral student in psychology at the University of Manitoba, was contacted by
a coach of the Manitoba Marlins, a youth competitive swim club, to help decrease
repetitive errors in some of the young swimmers. The errors had persisted for several
weeks in spite of the usual coaching techniques. Sandra developed an intervention
package that consisted of identification of errors and correct behaviors, awareness
training regarding the errors and correct behaviors, instruction with key words, mas-
tery criteria, and immediate feedback, and demonstrated the effectiveness of the
package in a multiple-baseline design across participants and swimming strokes
(Koop & Martin, 1983). Other examples of strategies for decreasing errors have
been reported for play execution of the offensive backfield of a youth football team
(Komaki & Barnett, 1977), performance of gymnastic skills with young gymnasts
(Allison & Ayllon, 1980), execution of throw-ins and goal kicks in youth soccer
(Rush & Ayllon, 1984), and performance of volleyball skills by college players
(Landin & Hebert, 1999; McKenzie & Liskevych, 1983).
Decreasing Problem Behaviors of Athletes
in Sport Environments
Sport psychology consultants are sometimes asked for their advice to help coaches
decrease problem behaviors exhibited by athletes. By problem behaviors we mean
a variety of disruptive, non-athletic activities that are likely to interfere with ath-
letic performance and/or create aversiveness for others, such as excessive socializing
during athletic drills, temper tantrums, annoying and disruptive behaviors while the
coach is talking to the team, and so forth. Examples of strategies to decrease problem
1 Overview of Behavioral Sport Psychology 11
behaviors include monitoring and the public posting of such behaviors (Galvan &
Ward, 1998), using self-monitoring and charting to increase desirable alternative
practice behaviors (Hume, Martin, Gonzalez, Cracklen, & Genthon, 1985), using
group music reinforcement for desirable alternative behaviors (Hume & Crossman,
1992), and awareness training, competing response training, and arranging support-
ing contingencies (Allen, 1998).
Managing Emotions to Maximize Athletic Performance
Martin and Pear (2011) suggested that emotions have three important character-
istics: (a) the internal autonomic reaction that one feels during the experiencing
of an emotion (such as the nervous sensations that an athlete feels just before the
start of an important competition), which is influenced by respondent condition-
ing; (b) the way that one learns to express an emotion overtly (such as swearing
and throwing things when angry), which is influenced by operant conditioning;
and (c) the way that one becomes aware of and describes one’s emotions (e.g.,
“I’m a little excited,” as opposed to “I’m really nervous”), which is also influenced
by operant conditioning. With this analysis in mind, two areas that have received
attention from behavioral sport psychologists are as follows: (a) teaching athletes
strategies to decrease excessive nervousness or fear that negatively affects athletic
performance and (b) teaching athletes strategies to overcome excessive anger and
aggression. A related area of research has examined the relationship between phys-
iological arousal and athletic performance. We will briefly comment on all three of
these areas.
Excessive nervousness or anxiety or fear is often i dentified by coaches and ath-
letes to account for poor athletic performance. Goldberg (1998) suggested that “fear
is probably the single biggest cause of choking in sports.” Using the model of
emotions summarized previously, Martin (2011) outlined four main reasons why
excessive feelings of nervousness or fear can interfere with athletic performance.
First, the physiological activity from excessive nervousness consumes energy, which
can be problematic in endurance athletic activities. Second, because of our evolu-
tionary history, experiencing nervousness can cause a narrowing of attention, so
that fearful athletes may miss important external cues. Third, excessive nervous-
ness causes the secretion of adrenaline, which can cause an athlete to rush a skilled
routine and destroy the timing of it. Finally, if an athlete is relatively relaxed at prac-
tices and very nervous at a competition, the excessive nervousness adds additional
stimuli that may interfere with stimulus generalization of a skill from practices to
competitions. Strategies that have been applied by behavioral sport psychologists
to help athletes cope with excessive nervousness or fear include teaching athletes
to (a) recognize and change negative thinking that might cause the fear or nervous-
ness, (b) restructure the environment to “tune out” and prompt relaxing thoughts,
(c) practice a relaxing breathing technique called deep center breathing, (d) practice
progressive muscle relaxation by alternatively tensing and relaxing various muscle
groups and paying close attention to how the muscles feel when they are relaxed
12 G.L. Martin and K. Thomson
versus tense, (e) maintain a sense of humor, and (i) visualize relaxing scenes [these
strategies are described in detail by Martin (2011) and Williams (2010)].
In the model of emotions described by Martin and Pear (2011), anger is caused
by the withdrawal or the withholding of rewards such as a missed shot by a basket-
ball player, a disallowed goal for a soccer player, or a penalty in football that wipes
out a yardage gain. Several studies have described successful anger management
procedures for athletes (Allen, 1998; Brunelle, Janelle, & Tennant, 1999; Connelly,
1988; Jones, 1993; Silva, 1982). Such studies commonly follow a four-step strategy
including (a) helping the athlete to identify anger-causing situations, (b) teaching
the athlete to perform substitute behaviors to compete with the anger, (c) prompt-
ing the athlete to practice the substitute behaviors using imagery and/or simulations
and/or role-playing, and (d) encouraging the athlete to use the coping skills in com-
petitive situations and to receive feedback. For further discussion of aggression in
competitive sports, see Chapter 13.
Regarding the relationship between physiological arousal and athletic perfor-
mance, many studies have suggested an inverted-U relationship between arousal and
performance (Landers & Arent, 2010). To illustrate this relationship, consider the
level of arousal as varying on a continuum from very low to medium to very high.
When the level of physiological arousal is low, athletic performance is likely to be
poor, and the athlete is likely to be described as being indifferent, disinterested, not
being able to “get into the game,” lacking intensity, etc. As the level of physiological
arousal increases to some medium level, athletic performance is likely to increase to
a peak, and the athlete is likely to be described as having lots of energy, having great
anticipation, and being on top of his/her game. As the level of arousal continues to
increase to a high level, athletic performance is likely to decrease, and the athlete is
likely to be described as being excessively nervous, tense, or fearful and will show
a high tendency to “choke.” For discussion of strategies to help athletes achieve an
optimal level of arousal, see Landers and Arent (2010) and Williams (2010).
Using Self-Talk and/or Imagery Training to Improve
Athletic Performance
As indicated previously, applied behavior analysts consider private behavior to
include saying things to oneself (i.e., self-talk) and imagining (e.g., visualizing a
clear blue sky), and assume that behavioral principles and procedures apply to pri-
vate as well public behavior. Regarding self-talk, research has indicated that athletes
can use self-talk to improve performance in a variety of areas, including controlling
their emotions and/or mood, stopping negative thoughts, improving their focusing
or concentration skills, problem solving or planning, and improving skill acqui-
sition and performance (Zinsser et al., 2006). To take just one example, Ziegler
(1987) reported that beginning tennis players practicing backhand shots showed
little progress when simply told to “concentrate.” However, they showed rapid
improvement when they vocalized the word “ready” when the ball machine was
1 Overview of Behavioral Sport Psychology 13
about to present the next ball, the word “ball” when they saw the ball coming toward
them from the machine, the word “bounce” as the ball contacted the surface of the
court, and the word “hit” when they observed the ball contact their racquet while
swinging their backhand.
From a behavioral perspective, Martin (2011) suggested that self-talk might serve
four behavioral functions. First, self-talk can serve as a conditioned stimulus (due to
prior respondent conditioning) to elicit various emotions, such as the gymnast who
thinks “balance, graceful” just before stepping on the balance beam to elicit feelings
of relaxation. Second, self-talk might function as a cue for attending or focusing on
certain stimuli (such as a batter in baseball saying, “watch the ball,” when the ball
leaves the pitcher’s hand). Third, in terms of operant conditioning, specific words
commonly called key words in sports might serve as discriminative stimuli to prompt
particular body positions for motor skills (such as a swimmer thinking “hips” during
the backstroke as a prompt to keep his/her hips high in the water and as flat as
possible). Fourth, self-talk can function as a conditioned reinforcer for desirable
actions (such as a weight lifter thinking, “Good work, keep it up,” after completing
10 repetitions of a particular weight exercise). For further discussion of self-talk t o
improve athletic performance, see Chapters 8 and 15.
Regarding imagery, cognitive–behavioral psychologists have made considerable
use of imagery training to improve the performance of athletes (Vealey & Greenleaf,
2010). From a behavioral perspective, we learn to experience visual imagery through
a process referred to by Skinner (1953) as “conditioned seeing,” in other words,
through respondent conditioning. For example, as we grew up, we experienced
many trials in which the words “blue sky” were paired with actually looking at
and seeing a blue sky. As a result, when we now close our eyes and imagine a
blue sky, the activity elicited in our visual system enables us to experience the
behavior of “seeing” a blue sky. Two behavioral psychologists, Malott and Whaley
(1983), talked more generally about instances of conditioned sensing. Our long
history of associating words with actual sights, sounds, and feelings enable us
to experience inside activity when we imagine seeing, feeling, or hearing some-
thing. In sport psychology, the process of imagining and seeing oneself performing
an activity is referred to as mental rehearsal or mental practice. In a survey of
235 Canadian Olympic athletes, 99% claimed t o use mental rehearsal to enhance
their performance (Orlick & Partington, 1988), and many studies have shown that
various imagery training procedures can enhance athletic performance (Vealey &
Greenleaf, 2010).
Strategies to use mental imagery to enhance practice performance include the
following: (a) scheduling separate imagery sessions to imagine performing a skill
(such as imagery practice to improve a basketball player’s free throw shooting); (b)
using imagery to energize before practices (for example, an athlete imagining that
an important competition is about to start); (c) using imagery at practices before
performing a previously learned skill in order to increase the likelihood of perform-
ing it correctly (such as a figure skater mentally rehearsing a jump at practices just
before attempting it); (d) practicing instant mental replays following a correctly per-
formed skill to help remember the feelings of performing it correctly; and (e) using
14 G.L. Martin and K. Thomson
visualization to simulate the competitive environment to promote stimulus gener-
alization from practices to competitions [these strategies are discussed in detail by
Martin (2011) and Zinsser et al. (2006)]. Strategies to use mental imagery to enhance
competitive performance include the following: (a) use of imagery for emotional
control just before and during competitions (such as an athlete who is excessively
nervous athlete imagining that he/she is relaxing at the beach on a warm summer
day); (b) mental rehearsal of a skill just before performing it such as reported by
Jack Nicklaus before each of his shots when he was an active competitive golfer
(Nicklaus, 1974); and (c) use of imagery to help tune out distracters [details of
these strategies are described by Martin (2011) and Zinsser et al. (2006)]. For fur-
ther discussion of imagery training to improve athletic performance, see Chapters 8
and 15.
Maximizing Confidence and Concentration for Peak
Performance During Competitions
Questionnaire studies with athletes have reported that the factor that most con-
sistently distinguishes highly successful athletes from less successful ones is
“confidence” (Weinberg & Gould, 2007; Zinsser et al., 2006). The ability to con-
centrate effectively has also been identified as a key ingredient of peak athletic
performance (Nideffer & Sagal, 2006). The term peak performance is used to
refer to an outstanding athletic performance, when an athlete “puts it all together”
(Krane & Williams, 2010). How do behavioral psychologists talk about confidence
and concentration? From a behavioral perspective, confidence is a term that is used
to describe athletes who have performed well in recent practices and/or competi-
tions and who show certain behavior patterns that would be described collectively
as illustrating the belief that they will perform well in an upcoming competition
(Martin, 2011). A behavioral interpretation of the term concentration suggests that
two behavioral processes are involved (Martin, 2011). First, concentration includes
behavior commonly referred to as observational, orienting, attending, or focusing –
behavior that puts the individual in contact with important cues for further respond-
ing. For example, a batter in baseball who is “concentrating” is likely to focus on
the pitcher, rather than attending to the first baseman. Second, following appropriate
attending or focusing behavior, concentration refers to the extent to which particular
cues exert effective stimulus control over skilled performance. For example, after a
batter has focused on the pitcher, if the sight of the baseball approaching the strike
zone exerts stimulus control over a solid swing and a hit by the batter, we would say
that the batter has shown good concentration.
Strategies to improve confidence, concentration, and peak performance include
teaching athletes to orient to proper cues (Nideffer & Sagal, 2006; Wilson, Peper, &
Schmid, 2006), influencing athletes to perform well in simulations of competi-
tive cues (Weinberg & Gould, 2007), using imagery to relive best performances
(Orlick & Partington, 1988), encouraging athletes to focus on realistic goals for exe-
cution rather than worrying about outcome (Swain & Jones, 1995; Ward & Carnes,
1 Overview of Behavioral Sport Psychology 15
2002), using facts and reasons to build a case against negative thinking (called
countering; Bell, 1983), and encouraging athletes to prepare and follow specific
competition plans (Rushall, 1979, 1992). For additional discussion on the topics of
this subsection, see Chapters 5, 8, 11, and 15.
Development of User-Friendly Behavioral Assessment
Tools for Athletes
Behavioral assessment has been defined as the collection and analysis of information
and data in order to identify and describe target behaviors, identify possible causes
of the behavior, guide the selection of an appropriate behavioral treatment, and eval-
uate treatment outcome (Martin & Pear, 2011). Behavioral assessment began to
emerge in clinical psychology in the 1970s in response to criticisms by behaviorally
oriented practitioners against traditional diagnostic assumptions and approaches
(Nelson & Hayes, 1979; Nelson, 1983). Behavioral assessment in sport psychol-
ogy typically begins with a behavioral interview to help the athlete identify major
problem areas, select one or two such areas for initial treatment, identify specific
behavioral deficits or excesses within the targeted problem areas, attempt to iden-
tify controlling variables of the problem behavior, and identify some specific target
behaviors for initial treatment (Orlick, 1989; Smith et al., 1996; Tkachuk, Leslie-
Toogood, & Martin, 2003). User-friendly behavioral checklists for athletes have
been developed to facilitate this process. One type of checklist is an across-sport
behavioral checklist, which lists performance aspects of practices and/or competi-
tions that apply to a number of different sports. For example, in the Pre-competition
and Competition Behavior Inventory developed by Rushall (1979), an athlete is
presented with such statements as, “I get nervous and tense before an impor-
tant competition,” “I mentally rehearse my competition plan before contests,” and
“When I am tired during a competition, I concentrate on my technique.” The athlete
is asked to respond to each statement by checking either Always, or Occasionally,
or Never. Other examples of across-sport behavioral checklists include the Post-
competition Evaluation Form (Orlick, 1996), the Psychological Skills Inventory
for Sport (Mahoney, Gabriel, & Perkins, 1987), and the Athletic Coping Skills
Inventory-28 (Smith, Schutz, Smoll, & Ptacek, 1995).
Awithin-sport behavioral checklist lists performance aspects of practices and/or
competitions for a particular sport. Such checklists contain behavioral descrip-
tors and situational examples with terminology specific to a given sport. Martin,
Toogood, and Tkachuk (1997) described within-sport behavioral checklists for
21 different sports. The within-sport checklists were positively reviewed (Smith &
Little, 1998), and research on the checklists for basketball, swimming, running,
volleyball, and figure skating has found them to have high face validity and
high test–retest reliability (Leslie-Toogood & Martin, 2003; Lines, Schwartzman,
Tkachuk, Leslie-Toogood, & Martin, 1999; Martin & Toogood, 1997). In one study,
the within-sport checklists for assessing mental-skills strengths and weaknesses of
athletes were completed by a sample of volleyball players, a sample of track athletes,
16 G.L. Martin and K. Thomson
and their respective coaches. Surprisingly, there was little agreement between vol-
leyball coaches and the athletes that they coached, and between track coaches and
the athletes that they coached, concerning the mental-skills strengths and weak-
nesses of those athletes. In spite of this evidence that the coaches in these samples
did not know the mental skills of their athletes, the coaches showed a high degree of
confidence in their ability to evaluate the mental-skills strengths and weaknesses of
their athletes (Leslie-Toogood & Martin, 2003). Although more research is needed,
results to date indicate that such checklists can facilitate behavioral sport psychology
consulting.
Another type of behavioral assessment tool is the Student–Athlete Relationship
Instrument, or SARI (Donohue, Miller, Crammer, Cross, & Covassin, 2007). The
SARI was developed to assess sport-specific problems in the relationships of ath-
letes with their coaches, teammates, families, and peers. The initial assessment of
the SARI indicates that it has good reliability and validity and that it could be a very
useful tool for assessing an important source of variability in the performance of
athletes. Interestingly, in a study of the SARI with 198 high school and college ath-
letes, the athletes on average reported strongest happiness with family relationships
and least happiness in their relationships with their coaches. For further discussion
of behavioral assessment, see Chapters 2, 3, 4, 5, 11, 14, and 15.
Development of User-Friendly Sport Psychology
Manuals for Athletes
A book such as this is written for advanced college students and sport practitioners.
What about easy-to-use, self-instructional manuals to guide athletes and/or coaches
in the use of sport psychology techniques without the aid of a practitioner? That
is an area that has also received some attention. Some of the early manuals (e.g.,
see Nideffer, 1976; Orlick, 1980, 1986; Tutko & Umberto, 1976), often with “sport
psyching” or “mental training” in the title, were prepared by practitioners with a
cognitive–behavioral orientation and were meant for athletes in general. More recent
versions of such manuals include Goldberg (1998) and Orlick (2008). Other manu-
als have been prepared for athletes in individual sports, such as curling (Martin &
Martin, 2006), dance (Taylor & Taylor, 1995), figure skating (Martin & Thomson,
2010), golfing (Martin & Ingram, 2001), and hockey (Martin, 2010). What is needed
is research evaluating the effectiveness of such manuals.
Summary
Behavioral sport psychology involves the use of behavioral analysis principles and
techniques to enhance the performance and satisfaction of athletes and others asso-
ciated with sports. Behavioral sport psychology developed a firm foundation in the
1970s with early leadership provided by Brent Rushall, Darryl Siedentop, Thom
McKenzie, Ron Smith, and Frank Smoll. Prominent characteristics of behavioral
1 Overview of Behavioral Sport Psychology 17
sport psychology include the following: (a) it identifies target behaviors of athletes
and/or coaches in a way that they can be reliably measured, and it uses changes
in the behavioral measure as the best indicator of the extent to which the interven-
tion has been successful; (b) its treatment procedures and techniques are grounded
in the principles and procedures of Pavlovian and operant conditioning; (c) many
of its interventions have been developed by practitioners who follow a cognitive–
behavioral orientation; (d) many of its researchers have relied heavily on the use
of single-subject research designs; and (e) it places high value on accountability
for everyone involved in the design, implementation, and evaluation of an inter-
vention. Major areas of application of behavioral sport psychology have included
(a) motivating practice and fitness training, (b) teaching new sport skills, (c) decreas-
ing persistent errors in sport skills, (d) decreasing problem behaviors of athletes
in sport environments, (e) managing emotions to maximize athletic performance,
(f) using self-talk and/or imagery to improve athletic performance, (g) maximizing
confidence and concentration for peak performance during competitions, (h) devel-
oping user-friendly behavioral assessment tools for athletes, and (i) developing
user-friendly sport psychology manuals for athletes.
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Part II
Assessment and Measurement
Chapter 2
Actigraphy: The Ambulatory Measurement
of Physical Activity
Warren W. Tryon
Measurement is as fundamental to modern sport as it is to science. The outcomes of
Olympic trials and competitions are sometimes determined by tenths or hundredths
of a second. Athletic training typically entails performance measurement in order to
guide and enhance training methods. Better performance is often its own reward –
that is, observing improvements may strongly motivate competitive athletes.
This chapter has several purposes, and correspondingly two major sections. The
first purpose of this chapter is to introduce readers to actigraphy: what it is, avail-
able instruments, and basic methodological issues. This is accomplished in the first
major section of this chapter. The second purpose of this chapter is to apply this
information to sports. In the second section, I begin with circadian issues, since
actigraphy can track activity level 24 h per day, seven days per week, 365 days per
year. Subsequently, I will discuss nocturnal activity and sleep since athletes need to
be properly rested. Following this, I will consider diurnal activity as training occurs
during waking hours. This discussion will include high-resolution actigraphy to bet-
ter understand behavioral topography in sports such as track and field, bowling, golf,
skating, gymnastics, and skiing.
Methods of Measuring Human Activity
Indirect Methods
Devices such as sensitive touch pads installed in swimming pools or high-speed
video cameras strategically mounted in stadiums and rinks capture human move-
ments for subsequent reviewing, evaluating, informing choices regarding medals,
and reviewing decisions made on the field by referees. While these methods enable
athletes to move freely, measurement can occur only within the confines of a partic-
ular space. Our meaning of ambulatory extends beyond such boundaries. As such,
these measurement methods will not be considered further in this chapter.
W.W. Tr yon (B)
Fordham University, Bronx, NY, USA
e-mail: [email protected]
25
J.K. Luiselli, D.D. Reed (eds.), Behavioral Sport Psychology,
DOI 10.1007/978-1-4614-0070-7_2,
C
Springer Science+Business Media, LLC 2011
26 W.W. Tr yon
Heart Rate
Athletic performances increase heart rate. Montoye, Kemper, Saris, and Washburn
(1996, pp. 98–105) discussed the use of heart rate monitoring to track physical
activity. Searching for “ambulatory heart rate monitors” on the Internet yields a
wide variety of ever-changing equipment models. While it is true that greater activ-
ity levels produce higher heart rates, factors other than activity level can also alter
heart rate. For instance, the excitement of competition can increase heart rate while
the athlete in question observes other athletes prior to their performance. One can-
not separate heart rate increases caused by psychological factors from those due to
increased exertion except by a carefully kept activity diary that identifies the times of
day during which the athlete performed or trained. Because heart rate is an indirect
measure of activity, I will not discuss it further in this chapter.
Core Body Temperature
O’Brien, Hoyt, Buller, Castellani, and Young (1998) introduced a method of mea-
suring core body temperature based on having participants swallow a pill-sized
transducer/transmitter that sends core body temperature to a receiver worn by the
person. Because core body temperature is an indirect measure of activity and
because body temperature changes lag sufficiently behind activity level changes,
I will not discuss it further in this chapter.
Doubly Labeled Water
Montoye et al. (1996, pp. 17–25) discuss a method for measuring energy expendi-
ture in free-living people; in this method, the people drink water laced with stable
isotopes of hydrogen and oxygen. The loss of these isotopes over time as assessed
from saliva, urine, or blood provides an estimate of energy expenditure, considered a
gold standard, that is directly proportional to activity level. Because doubly labeled
water is an indirect measure of activity and because it provides only crude temporal
resolution, I will not discuss it further in this chapter.
Direct Methods
Pedometers/Digital Step Counters
One of the earliest documented forms of direct measurement of athletic-related
behavior comes from Leonardi de Vinci (1452–1519), who invented the pedometer
during the midpoint of his life (Gibbs-Smith, 1978). Thomas Jefferson encoun-
tered the pedometer during his tour as US ambassador to France between 1785
and 1789 and sent it back to America, along with other items (Wilson & Stanton,
1999). The J apanese i ntroduced the first commercial pedometer under the name of
manpo-meter, where “manpo” in Japanese means 10,000 steps. Heel-toe transitions
involved in walking move the hips up and down. Putting one foot in front of the
other displaces the hips left and right. Together, walking moves the attached sensor
2 Actigraphy: The Ambulatory Measurement of Physical Activity 27
in a spiral trajectory. Old-style pedometers contained a pendulum that moved in
response to hip movements associated with walking and related behaviors such as
stooping to pick things up from the floor. A distance indicator was connected to
the pendulum through a series of gears, one of which was a stride-length setting
that could only be crudely set in an effort to make the unit of measure the mile.
Stride length was estimated by counting steps while walking a measured distance
and dividing. Hence, the unit of measure was crudely estimated. Running would
entail a much larger stride than indicated – thus, seriously underestimating distance
traversed. Bending to pick up items entails no forward movement, but is counted
as a stride – thus, underestimating distance walked to an extent that is directly pro-
portional to frequency of this behavior. Modern step counters digitally convert steps
into distance. While a precise stride length can be registered and can be accurately
multiplied by the steps registered, the problem of variable stride lengths remains.
Units of measure. The previous section refers to digital step counters since that
is the primary mechanism of the modern pedometer. Vertical movements of a small
weight increments a digital counter with each movement of the waist activate the
counter. In this regard, the step is the unit of measure. Each person’s step differs
depending upon their height, which makes leg length an important covariate when
comparing steps taken across persons of varying heights. However, I submit that
such variation is a natural part of human activity and not something that one nec-
essarily wants to control for. Consider the following situation where an adult and
a small child cross a street holding hands. The adult strides easily while the child
walks rapidly in an effort to keep up. Have they been equally active because t hey
traversed the same curb-to-curb distance? Or has the child been much more active
than the adult because their much shorter legs required many more steps to traverse
the same distance? I submit that the latter conclusion is valid.
Entering “pedometers” or “step counters” into a search engine will identify many
vendors selling a wide variety of instruments that range from high to low quality.
One probably gets what one pays for. Assessment of instrument reliability and con-
sistency among devices should be done under laboratory conditions, using some
form of bench test (such as a shaker) on which several devices can be mounted
simultaneously. Data from devices that over-count or under-count can be adjusted
using a conversion factor derived from such a test. For example, if after 100 back
and forth movements, Device A counts 115 and Device B counts 95, the conver-
sion factor for Device A would be 100/115 = 0.8696, with the conversion factor for
Device B being 100/95 = 1.0526.
We shall see below that studies frequently report steps per day. This metric does
not control for waking hours and activities for which the pedometer cannot be worn
such as swimming. Someone who sleeps late and goes to bed early may get a low
step count that day despite being rather active. If that person also went swimming
for an hour or two during such a day, their step count for that day would be even
lower. However, dividing the steps per day by the minutes that the pedometer was
worn might more correctly reflect the person’s average activity level. The method
I have been using for over 19 continuous years studying my own activity level is
as follows. Upon dressing in the morning, I record the date and time I attached my
28 W.W. Tr yon
waist-worn step counter on one line of a 3 × 5 index card. Upon undressing at night,
I record the time I took my step counter off using military 24-h designation. I enter
a one- or two-word note if I did something unusual that day and then record the step
count. If I went swimming that day, then I would record the time off and back on as
well. This enables me to record 10 days of data per card. At the time of this writing,
I have been using this procedure for almost 24 years. Thus, my personal example
demonstrates that longitudinal data collection is both possible and feasible. The
point of recording time on and off is to determine the minutes of wearing time so
that I can divide steps taken by minutes of wearing time in order to control for time
worn as longer wearing times can lead to higher step counts even if activity level
is lower. Montoye et al. (1996, pp. 72–75) also discussed the use of pedometers to
measure activity level.
An important limitation of standard step counters is that someone must read and
record the data at specified intervals. Several models of Actigraph LLC actigraphs
have software that converts actigraph data into step counts to simulate pedometer
functionality (www.theactigraph.com). This function enables one to change the unit
of measure from milli-g to steps. New Lifestyles (http://new-lifestyles.com/) retails
five models of accelerometer-based step counters with memories in addition to four
models of coiled spring pedometers and two models of hair spring pedometers
(http://www.thepedometercompany.com/pedometers.html).
Actigraphs
Actigraphs are small lightweight computerized accelerometer-based devices worn
typically at the waist, wrist, and/or ankle that rapidly and simultaneously digitize
movement in one, two, or three dimensions every 15, 30, or 60 s continuously 24 h
a day for as many days as memory allows (which typically spans 7–28 days). Further
information regarding the term “actigraph” can be found at http://en.wikipedia.org/
wiki/Actigraph. A list of actigraph vendors and links to most of their products is
provided in Table 2.1.
Units of measure. There are no standard units of measurement across actigraphs
by various vendors, despite the fact that all of these devices use accelerometers to
measure activity level. Velocity, typically understood as speed, is defined as the dis-
tance covered per unit time (e.g., miles per hour or meters per second). Acceleration
Table 2.1 Actigraph vendors and devices
Vendor Device/URL
Actigraph LLC
http://www.theactigraph.com
GT1M series
http://www.theactigraph.com/productsGT1M.asp
ActiTrainer
http://www.theactigraph.com/index.php?option=
com_virtuemart&page=shop.product_details&flypage=
flypage.tpl&product_id=7&26Itemid=87
2 Actigraphy: The Ambulatory Measurement of Physical Activity 29
Table 2.1 (continued)
Vendor Device/URL
Ambulatory Monitoring, Inc.
http://www.ambulatory-
monitoring.com/
MotionLogger
http://www.ambulatory-monitoring.com/motionlogger.html
MicroMini-Motionlogger and family of sensors
http://www.ambulatory-monitoring.com/micro_sensors.html
Motionlogger Buzz Bee
http://www.ambulatory-monitoring.com/buzz_bee.html
Body Media, Inc. SenseWear
http://www.sensewear.com/
SenseWear BMS
http://www.sensewear.com/BMS/solutions_bms.php
Cambridge neurotechology
limited
http://www.camntech.com/
Actiwatch
http://www.camntech.com/
IM Systems
http://www.imsystems.net/
ActiTrac
http://www.imsystems.net/actitrac/actitrac.htm
DigiTrac (12 bit A/D Triaxial, 40 Hz)
http://www.imsystems.net/actitrac/actitrac.htm
BioTrainer-Pro
http://www.imsystems.net/btpro/btpro.htm
BioTrainer II
http://www.imsystems.net/bt2/bt2.htm
BedMate
http://www.imsystems.net/bedmate/bedmate.htm
SleepCheck
http://www.imsystems.net/sleepcheck/sleepcheck.htm
Mini mitter
http://www.minimitter.com/
Actiwatch
http://ribn.respironics.com/
New Lifestyles
http://new-lifestyles.com/
Pedometers
http://new-lifestyles.com/content.php?_p_=100
http://www.thepedometercompany.com/pedometers.html
Nokia
http://www.nokiausa.com/
Cellphone actigraphy
N95, N95 8GB, N82, N93i, 6210, N79, N85, N96 Phones
http://betalabs.nokia.com/apps/nokia-step-counter
PAL Technologies Ltd
http://www.paltechnologies.
com/
ActivPAL
http://www.paltech.plus.com/products.htm#activpal
Philips directlife activity
monitor
http://exercise.about.com/od/
productreviews/fr/
philipsactivitymonitor.htm
Activity monitor
http://exercise.about.com/gi/o.htm?zi=1/XJ&zTi=1&sdn=
exercise&cdn=health&tm=91&gps=123_265_1020_517&f=
10&su=p284.9.336.ip_p674.7.336.ip_&tt=6&bt=1&bts=
1&st=24&zu=http%3A//www.directlife.philips.com/
how_it_works/advanced_activity_monitor/
Polar
http://www.polarusa.com/us-
en
Activity monitors
http://www.polarusa.com/us-en/products/get_active
Polar AW200 activity monitor
http://www.dickssportinggoods.com/product/index.jsp?
productId=2715949&CAWELAID=110638143
Stayhealthy
http://www.stayhealthy.com/
en_us/main/
RT3
http://www.stayhealthy.com/en_us/main/research_activity_
monitor
30 W.W. Tr yon
is defined as the change in velocity per unit time; typically meters/second/second.
Acceleration is frequently measured in units of “g” for gravity, where 1 g equals the
rate with which bodies freely fall to Earth, which is 9.80616 m/s/second at sea level
at 45
◦
latitude and mean sea level.
The problem begins with the material used to make the accelerometer.
Piezoceramic accelerometers emit a voltage only while they are undergoing positive
or negative acceleration. The charge bleeds off to zero when acceleration is constant,
which includes the absence of movement. This is understandable as no movement
should be measured as zero, and the fact that limbs are jointed and attached to
the torso means that they cannot accelerate at a constant rate in a single direction
for very long. All changes in direction produce acceleration. This rate of angular
movement, measured in Hertz, modifies the actual acceleration value. Because acti-
graphs do not measure these frequencies, it is impossible to accurately report in
units of g. However, because most actigraphs bandpass filter movement frequency
from approximately 0.1–3.6 Hz in order to preclude artifact from being recorded
as human movement, one could use the midpoint of this frequency range to con-
vert accelerometer voltages to g units. The technical specifications of each vendor
should be consulted regarding this issue.
Some actigraphs contain analog-to-digital (A/D) converters that divide a g-force
range into parts. For example, one actigraph used an 8-bot A/D converter to divide a
–2.13 g to +2.13 g maximum range into 2
8
= 256 levels of acceleration (cf. Tryon &
Williams, 1996). Dividing 4.26 g by 256 gives 0.01664 g/s/count on the A/D
converter. This actigraph makes 10 measurements per second resulting in a sam-
pling period of 0.1 s. Integrating over this interval corresponds to multiplying
0.01664 g/s/count by 0.1 s resulting in 0.001664 g/count = 1.664 milli-g/count.
Other actigraphs integrate the area under the curve created by the time varying
voltage from the accelerometer. The resulting volt-second units are not standard.
Methodological Issues
The reader should be aware of the following four methodological issues when con-
sidering actigraphy: (1) site of attachment, (2) instrument reliability, (3) clinical
repeatability, and (4) instrument validity. The following three s ections address these
issues.
Site of Attachment
Activity level is commonly considered to be something akin to a personality trait
(i.e., a rather stable feature of the person). This conceptualization is incorrect in two
major ways. First, activity is measured by placing a sensor on a body part such as
the wrist, waist, or ankle. The instrument actually responds to its own movement,
which corresponds to movements of the site of attachment only. Placing instruments
at multiple body sites provides information about those sites, but only about those
sites. While walking and running move all body sites, these behaviors move them
differently. Sitting in a chair reading a magazine immobilizes the waist and ankles,
2 Actigraphy: The Ambulatory Measurement of Physical Activity 31
but not the arms and hands, which are involved in turning pages and perhaps writ-
ing notes. Securely attaching actigraphs to a belt or waist band places it relatively
close to the body’s center of gravity. Vertical movements of this site are directly
proportional to energy expenditure.
One should therefore think in terms of the site of attachment that most reflects
the behavior of interest. For example, the wrist is the most active site in waking
people and, therefore, the site of interest when assessing sleep. All computer sleep-
scoring algorithms assume that the data are from the wrist. It does not seem to matter
whether one measures the left or right wrist in left- or right-handed people.
The second problem is that activity level is not constant over time, but varies
in two substantial and important ways. Autocorrelation is the first characteristic of
how activity level varies over time when behavior is measured in one-min or 30-s
epochs. If one is walking through a given minute, it is rather probable that one will
also be walking during the next minute as it usually takes more than one min to walk
anywhere. If one is sitting through a given minute, it is rather probable that one
will be sitting during the next minute also. Autocorrelation violates the common
assumption of independence made by most statistical methods such as t-tests and
analysis of variance (ANOVA), thus precluding their use. Aggregating activity level
over sufficiently long blocks of time such as 15, 30, 45, or 60 min tends to reduce
autocorrelation. The second characteristic of repeated activity measurements is that
at the one-min epoch of temporal resolution, activity level tends to form a Poisson
distribution where the standard deviation equals the mean. Most observations lie
within 1 standard deviation of the Poisson mean versus ±3 standard deviations in a
normal distribution. A graph of activity vs. time using one-min epochs reveals that
the magnitude of activity level changes radically from one min to the next, thereby
creating enormous variability. Attempting to normalize this integral feature of activ-
ity level is not recommended for at least two reasons. The first, and perhaps the most
persuasive, reason is that one is ignoring a central feature of activity level. The sec-
ond reason is that all transformations complicate interpretation. For example, how
should one interpret the square root or the logarithm (natural or base 10) of activity
level or, worse yet, the square root of the logarithm of activity level?
Instrument Reliability
Physical instruments and psychological tests differ in fundamental ways that influ-
ence how one assesses their reliability and validity. Psychological tests must be
given to people in order to obtain data from which to compute reliability and validity
coefficients. The sample studied can markedly influence the obtained results, which
is why informed psychometricians understand that tests are reliable and valid only
for some samples and some purposes, but not others. In short, reliability and validity
are not entirely about the test, per se. This limitation does not pertain to instruments
whose f unctional properties can be studied in the laboratory with machines capable
of accurately reproducing specific movements.
The concept of reliability requires that the same phenomenon be measured at
least twice, and preferably multiple times, to see if the same value is returned. The
32 W.W. Tr yon
source of movement used to study the reliability of an instrument should vary as
little as possible; preferably variation should be negligible so that it can be assumed
to be effectively zero. Then, all observed variation over repeated measurements can
be entirely attributed to the unreliability of the device. However, more commonly,
investigators have people repeatedly perform the same behavior and attribute all
observed variability to the unreliability of the device. This assumes that the people
have precisely repeated the requested behaviors – I submit that this is rarely, if ever,
true. For example, participants are asked to repeatedly walk a measured distance or
repeatedly climb a set of stairs, or repeatedly perform a task such as hammering a
nail. Attributing all observed variation to measurement unreliability assumes that
human variability is negligible, when in fact it is both measurable and substantial.
Hence, the variability of instruments should always be established under laboratory
conditions, and never with people performing specific behaviors as this concerns
clinical repeatability, which is discussed below.
The standard methods by which psychometricians calculate the reliability of psy-
chological tests are inappropriate when measuring the reliability of instruments for
methodological and statistical reasons. The typical method for psychological tests is
to administer them to a group of people on one occasion and to compute Cronbach’s
alpha using commercially available software to determine the test’s reliability,
which is a measure of internal consistency. When possible, psychologists admin-
ister the test to a group of people on two occasions to determine test–retest temporal
stability. Here the reliability coefficient is the correlation coefficient between the test
and retest scores. The time interval must be carefully chosen: long enough so that
participants do not clearly recall their prior answers but short enough so that real
change does not occur. Both methods assume substantial variation across people.
Instruments are typically constructed to a physical standard to minimize inter-
device variability and then calibrated to remove as much remaining interdevice
variability as possible. The resulting homogeneity artificially reduces traditional
psychometric indices of reliability in direct proportion to the extent to which devices
perform the same way. This is the reverse of what one wants. A solution I have
recommended is to compute the coefficient of variation (CV) on a set of repeated
measurements taken from a machine such as a pendulum or shaker (cf. Tryon &
Williams, 1996; Tryon, 2005). This is done by dividing the standard deviation (SD)
of the repeated measurements for a single device by the mean of those measurements
and multiplying by 100 to yield a percentage. The more close one measurement is
to another, the smaller is its SD and CV. This method enables one to determine a
reliability coefficient for each device.
When an investigator has multiple devices, they may observe that the means used
to compute the CAs are not identical. One can compute a correction coefficient
for each device as follows. Compute the grand mean, the mean of all the means
across devices. The correction coefficient for each device is the difference between
its mean and grand mean, i.e., the number that must be added to or subtracted from
the instrument’s mean in order to obtain the grand mean; i.e., some correction values
are negative and others positive. This correction constant is then added to every mea-
surement made with that instrument. This will minimize any systematic differences
2 Actigraphy: The Ambulatory Measurement of Physical Activity 33
across instruments. This issue is avoided for individuals when the same instrument
is used at the same body site for the same person across time. However, this issue
occurs when two or more instruments are used to compare the behavior of two or
more individuals.
Pedometers. Bassett et al. (1996) reported that the “manpo” pedometers initially
introduced by the Japanese were subject to large measurement errors, but that the
next generation of electronic pedometers (i.e., step counters) is reasonably accu-
rate for assessing walking-related activities. Modern pedometers, especially those
using the “KS10 and JW200 pedometer engines, are quite accurate. Vincent and
Sidman (2003) tested 24 Yamax MLS-2000 digital pedometers using a shake test.
The average deviation over 100 shakes was 0.39 steps ± 0.29, before what they
characterize as heavy use in a large study, and 0.60 ± 0.62 steps after the study was
completed. All pedometers were within 5% of nominal values, i.e., within 5 steps
of the programmed 100 shakes. The authors also reported results for a standard
walking where the mean was 2.26 and the standard deviation was 0.80 before the
study. The walking test was repeated after the study ended, when the mean was
1.71 and the standard deviation was 0.88. The authors reported that the walking test
produced significantly more error (F(1, 46) = 109.04, p <0.01). Note that the walk-
ing test mean of 2.26 is 5.79 times as large as the shake test mean of 0.39 before
the study began, and the mean of 1.71 was 2.85 times as large as the shake test of
0.60 after the study ended. Hence, we can conclude that walking tests overestimate
pedometer error from approximately 300 to 600% – thus supporting the recommen-
dation made above to restrict assessments of the reliability of activity monitors to
laboratory studies.
Actigraphs. The reliability and validity of actigraphs has also been studied under
laboratory conditions where known physical forces can be repeatedly applied with
considerable precision. Tryon and Williams (1996) studied the reliability and valid-
ity of 40 CSA Model 7164 actigraphs using a spinner and a 5 feet 7 i nch pendulum.
They reported reliability coefficients between 97.5 and 99.4%. Validity was estab-
lished by comparing the observed performance with expectation during pendulum
decay and spinning. Tryon (2005) repeatedly tests four MotionLogger
TM
and four
BuzzBee
TM
actigraphs 10 times on a precision pendulum. He reported reliability
coefficients of 0.98 and validity coefficients of 0.99.
Clinical Repeatability
It is important to know how much variability is associated with efforts that people
make to reproduce behaviors in the same way, because this level of variability limits
our ability to detect change such as improvements due to training. All instrumented
measures of human activity level in applied contexts such as sports necessarily
confound instrument unreliability with human biomechanical, neural, and psy-
chological limits and will necessarily be more variable than instrument reliability
suggests. It is important for trainers and athletes to repeatedly measure performances
that they feel are the same and compare them with measurements of behaviors that
they feel are different.
34 W.W. Tr yon
Aggregates of behavior are more repeatable than are single measurements of
behavior. Hourly measures are more repeatable than are minute-by-minute mea-
sures. Weekly measures are more repeatable than are daily measures. Epstein (1979,
1980, 1983, 1986) clearly demonstrated that aggregation improves t est–retest reli-
ability including good agreement. He also demonstrated that the Spearman–Brown
prophecy formula enables one to accurately estimate reliability from the number of
repeated measurements taken.
Instrument Validity
Instruments are designed and constructed to measure specific quantities. For exam-
ple, the accelerometers in modern actigraphs measure acceleration, and little else,
as long as they are operated within specified temperature extremes and not dropped,
i.e., exposed to extreme accelerations that might damage them. Nevertheless, it is
important to establish their operating characteristics, which is best done under labo-
ratory conditions for all of the reasons discussed above concerning reliability. Tryon
and Williams (1996) used both a large 5 foot 7 inch pendulum and a spinner device
to assess both reliability and validity.
Application to Sports
I now turn to the question of what can be done to enhance sport performance
with pedometers and actigraphs. I separate this discussion into two parts because
the possible applications differ by virtue of the different technical capabilities of
pedometers and actigraphs.
Pedometers
General Fitness Using Pedometers
Athletes must be generally fit in order to benefit from specialized training. The
President’s Council on Physical Fitness and Sports (2001) identified physical inac-
tivity as important to a healthy life style and made recommendations for using
pedometers to improve general fitness.
Normative data. Bohannon (2007) reported a meta-analysis of the average and
95% confidence interval for the number of steps taken by 6,199 participants in
42 studies. The overall average was 9,448 with a 95% confidence interval of 8,899–
9,996 steps. Participants below the age of 65 took an average of 9,797 steps per
day with a 95% confidence interval of 9,216–10,377 steps. Participants 65 and
older took an average of 6,565 steps per day with a 95% confidence interval of
4,897–8,233 steps.
Tudor-Locke and Myers (2001) compiled normative results from 32 studies and
reported that activity level decreases with age; they also noted a sex effect. Healthy
8–10-year-old children take between 12,000 and 16,000 steps per day, boys being
2 Actigraphy: The Ambulatory Measurement of Physical Activity 35
more active than girls. Vincent and Pangrazi (2002) studied more than 700 children
aged 6–12 years old and reported that boys took between 12,300 and 13,989 steps
per day whereas girls took between 10,479 and 11,274 steps per day. Wilde (2002)
studied more than 600 adolescents aged 14–16 years old and reported between
11,000 and 12,000 steps per day, boys being more active than girls. Rowlands,
Eston, and Ingledew (1999) reported that 8–10-year-old UK children take between
12,000 and 16,000 steps per day.
Tudor-Locke and Myers (2001) reported that healthy young adults take between
7,000 and 13,000 steps per day, men being more active than women, whereas healthy
older adults take between 6,000 and 8,500 steps per day with men again being more
active than women. Individuals living with disabilities and chronic diseases can be
expected to take between 3,500 and 5,500 steps per day.
Suzuki et al. (1991) reported that children and adults aged 3–22 years took an
average of 14,500 steps per day if they were intellectually disabled, 12,700 steps
per day if blind, 17,400 steps per day if deaf, and 8,050 steps per day if physically
handicapped.
How active should we be? Initial recommendations by Japanese investigators
were for 10,000 steps per day to achieve general fitness (Hatano, 1993; Yamanouchi
et al., 1995), which corresponds to approximately 300–400 calories per day (Hatano,
1997) depending upon walking speed, sex, age, and body size. This is at least double
the amount of activity (150 kcal/day) recommended by the U.S. Surgeon General
(U.S. Department of Health and Human Services, 1996). The American College of
Sports Medicine (ACSM: www.acsm.org) has endorsed the following categorization
of activity levels. Taking less than 5,000 steps per day is termed “Sedentary.” Taking
between 5,000 and 7,499 steps per day is termed “Low Active.” Taking between
7,500 and 9,999 steps per day is termed “Somewhat Active.” Taking between 10,000
and 12,500 steps per day is termed “Active.” Taking more than 12,500 steps per
day is termed “Highly Active.” The President’s Council on Physical Fitness and
Sports (2001) recommended that children take at least 11,000 steps per day, at least
five days per week, as a standard healthy base. The more recent ACSM recom-
mended daily activity level for children is between 12,000 and 16,000 steps per day.
Leermakers, Dunn, and Blair (2000) suggested that at least 15,000 steps per day
may be necessary to achieve weight loss goals.
Effects of body composition. Overweight adults take fewer steps than do normal-
weight adults (McClung, Zahiri, Higa, Amstutz, & Schmalzried, 2000; Tryon,
Pinto, & Morrison, 1991; Tudor-Locke & Myers, 2001; Tudor-Locke, Jones, Myers,
Paterson, & Ecclestone, 2002). The same relationship holds for children (Rowlands
et al., 1999). Tudor-Locke and Myers ( 2001) have shown that people who take
more than 9,000 steps per day frequently have a normal body mass index (BMI),
whereas individuals who take less than 5,000 steps per day frequently are considered
obese by BMI standards. However, physicists define work as mass times distance.
Multiplying body weight by steps taken frequently reveals that overweight people
expend more energy than do normal weight people (Tudor-Locke & Myers, 2001).
Using pedometers to promote activity.Sidman(2002) has reported at book length
about promoting activity in sedentary women using pedometers. However, I am
36 W.W. Tr yon
aware of no studies that used pedometers to enhance athletic performance in healthy
people even in such likely journals as Sports Medicine, Research Quarterly for
Exercise and Sport, and Medicine and Science in Sports and Exercise. The focus
of research with pedometers is on sedentary people and/or sedentary people with
health issues such as obesity, diabetes, and hypertension.
Richardson et al. ( 2008) reported a meta-analysis of studies designed to pro-
mote activity level. Their inclusion criteria were extensive. Each study had to use
pedometers as a motivational tool including setting a step-count goal. The study was
a controlled trial or had a pre-post design. The study did not use concurrent dietary
intervention. Participants were both sedentary at baseline and overweight or obese.
Intervention lasted at least four weeks. The following databases were searched:
CINAHL, EMBASE, MEDLINE, PsycINFO, SportDiscus, and the Web of Science.
The authors identified 9 studies covering 307 participants in programs lasting from
four weeks to one year. Results indicated that participants’ average activity level
increased by 3,656 steps.
Bravata et al. (2007) also conducted a meta-analysis of pedometer-based activ-
ity promoting programs. They searched for all English-language articles in the
MEDLINE, EMBASE, Sport Discus, PsychINFO, Cochrane Library, Thompson
Scientific (previously known as Thompson ISI), and ERIC databases from 1966
through 2007, and retrieved 2,246 citations. Of these 26 studies, 8 randomized
controlled trials (RCTs) and 18 observational studies met the following inclusion
criteria. Studies had to include more than five participants; studied participants in
naturalistic settings; counseled participants relative to activity goals; measured BMI,
glycemic control, serum lipid levels, and blood pressure; and expressed baseline
activity as steps per day using a pedometer. As noted above, the steps per day met-
ric fails to control for length of waking day and for activities such as swimming
during which the pedometer would be removed. Participants were 49 years old on
average (SD = 9), although five studies concerned people whose age was greater
than 60 years on average. Nine studies exclusively enrolled women. Overall, men
accounted for just 15% of the samples. When race and ethnicity were reported, 93%
were white, on average. Most participants were obese by BMI standards, but had
relatively normal serum lipid levels.
Interventions took from 3 to 104 weeks, with an average and standard deviation
of 18 and 24, respectively. Five of the interventions took place at work. Counseling
sessions ranged from 0 to 104 with a mean and standard deviation of 7 and 19,
respectively. The average steps per day during baseline were 7,473 with a standard
deviation of 1,385 and a range of 2,140–12,371 steps per day. The 155 participants
across the 8 RCTs that actively used pedometers to increase activity took an aver-
age of 2,491 more steps per day than did the 122 control participants. The 95%
confidence interval ranged from 1,098 to 3,885 steps per day. Participants in the 18
observational studies who used pedometers to increase activity level took an aver-
age of 2,183 more steps per day. The 95% confidence interval ranged from 1,571
to 2,796 steps per day. When combined, using a pedometer with an activity goal
such as 10,000 steps per day resulted in an average activity increase of 26.9%.
This change was associated with an average BMI reduction of 0.38 with a 95%
2 Actigraphy: The Ambulatory Measurement of Physical Activity 37
confidence interval ranging from –0.05 to –0.72. Systolic blood pressure decreased
by 3.8 mmHg with the 95% confidence interval ranging from –1.7 to –5.9 mmHg.
These changes were more pronounced in participants who had a higher initial blood
pressure and who took more steps per day suggesting a dose–response relationship
between activity and health benefits.
Conclusions. Pedometers provide numerical evidence of activity level that can
be incorporated into a planned regimen favoring greater activity and corresponding
improvements in fitness. The existing literature shows that even unathletic people
can increase their activity level and improve fitness. It therefore seems reason-
able that athletes should be able to do as well or better at achieving similar goals.
However, pedometers cannot be used to measure activity while swimming, and
while pedometers can be worn while bike riding and weight training, they will not
accurately measure caloric expenditure during these activities. Hence, pedometers
pertain to general fitness that derives from ambulation.
Actigraphs
Actigraphs offer two important advantages over even the best accelerometer-
based pedometers: (1) proportionality and (2) time-locked repeated measurements.
Whereas the earliest actigraphs registered presence of movement but not intensity,
modern actigraphs respond in direct proportion to the intensity of activity. While this
information can be reduced to “steps,” much information is lost in doing so because
actigraphs measure activity level at least 10 times per second, and then average over
a user-defined epoch (typically one min, i.e., 600 measurements/min). Measuring
activity level so intensively and reporting and storing results at one-min time slices
provide a much more detailed record of activity level – this therefore enables more
applications than pedometers.
Sleep
It is important that athletes get proper sleep in order to give their best perfor-
mance. Polysomnography (PSG) is the gold standard for measuring sleep, but
requires one to sleep in a sleep lab, which would be prohibitively expensive for
continuous use. Home PSG is possible, but its use is also restricted to diag-
nosing sleep disorders rather than as a training resource for athletes. Actigraphy
provides an alternative instrumented method for studying sleep. The American
Academy of Sleep Medicine updated its Practice Parameters for the use of actig-
raphy to assess sleep (Morgenthaler et al., 2007). These Practice Parameters are
also available on the American Academy of Sleep Medicine Web page (http://
www.aasmnet.org/PracticeParameters.aspx?cid=-1) and on the National guideline
Clearinghouse Web page (http://www.ngc.gov/summary/summary.aspx?doc_id=
10779). A PubMed search for “actigraphy, sleep” returned 720 articles on April
18, 2010, indicating that a substantial body of research supports these guidelines.
Actigraphy and PSG differ with regard to sleep measures for primarily two rea-
sons (cf. Tryon, 2004): (1) PSG is a multichannel physiological (e.g., EEG, EMG,
38 W.W. Tr yon
respiration) monitoring system; actigraphy is a single-channel behavior-monitoring
system, and (2) PSG and actigraphy key on different parts of the sleep-onset spec-
trum. PSG sleep scoring continues to be based on the Rechtschaffen and Kales
(1968) sleep scoring criteria for humans that score wake and then various stages
of sleep. However, it is now clear that sleep onset entails several systematic changes
and is not a discrete event. Tryon (2004) described the following three phases
of sleep onset: (1) People become i nactive for a period of time that actigraphy
considers characteristic of sleep onset. People with insomnia are noted for their
ability to remain awake but motionless. Good sleepers complete this phase of sleep
onset much more rapidly. (2) The beginning of the second stage of sleep onset is
marked by muscle relaxation resulting in dropping handheld objects. This was the
gold standard criterion of sleep onset that was used to validate the onset of PSG-
based sleep and consequently continues to mark the point when PSG marks sleep
onset. An empty spool of thread held between the thumb and the forefinger was
typically used to determine this point of muscle relaxation. (3) The beginning of
the third stage of sleep onset is marked by an elevation of the auditory threshold,
i.e., when awareness of one’s surroundings is lost. This is the point of subjective
sleep onset and corresponds to sleep onset times recorded in sleep logs. Waking
up rapidly reverses the three stages of sleep onset. Actigraphy cannot measure all
that PSG can, but Tryon (1996) has shown that actigraphic measures of four sleep
parameters are highly correlated with PSG measures of the same parameters. For
example, validity correlations for total sleep time ranged from r(19) = 0.72 to r(3)
= 0.98. Validity correlations for percent sleep ranged from r(19) = 0.82 to r(2) =
0.96. Validity correlations for sleep efficiency ranged from r(23) = 0.63 to r(11) =
0.91. Validity correlations for wake after sleep onset ranged from r(37) = 0.56 to
r(67) = 0.87.
Athletes sometimes fly long distances before competing, and that can result in jet
lag, which can interfere with athletic performance. Montaruli, Roveda, Calogiuri,
La Torre, and Carandente (2009) reported results from an actigraph-based study
demonstrating how jet lag can be minimized and sleep can be improved. They used
actigraphs to measure the sleep of 18 athletes who flew from Milan to New York,
where 12 of them ran the 2007 New York City Marathon. They divided these 12 ath-
letes into two groups of six: a morning training group (MTG) who trained in Milan
from 7 a.m. until 9 a.m. for one month and an evening training group (ETG) who
trained in Milan from 7 p.m. to 9 p.m. for one month. The remaining six athletes
served as a control group (CG); they did not train and did not run the marathon. In
New York, the MTG and ETG groups both trained in the morning from 7 a.m. to
9 a.m. Their results showed that the transatlantic flight fragmented the sleep of the
ETG and CG significantly more than that of the MTG and that morning workouts
repaired this problem.
Circadian Applications
Circadian rhythms are those biological functions that wax and wane over an approxi-
mately 24-h period. Fit individuals typically have robust circadian rhythms. Activity
2 Actigraphy: The Ambulatory Measurement of Physical Activity 39
level is normally characterized by a circadian rhythm in that it should be much
higher during waking hours than during sleep. While it is possible to assess circadian
rhythm over a single 24-h period, a much more accurate assessment results when
evaluated over a week or month. The average activity level over a 24-h period or
multiple periods is called the measor. The time at which peak activity level occurs is
called acrophase. Cosinor software can be used to determine the best-fitting cosine
wave and deduce acrophase from its peak. Amplitude refers to the height of the fitted
cosine function. The suprachiasmatic nucleus (SCN) is believed to be the biological
clock in mammals that regulates circadian rhythms. Zeitgebers are environmental
cues that entrain, i.e., regulate, circadian rhythms. The solar light/dark cycle is a
prominent zeitgeber that can be disrupted by long jet flights where the light/dark
cycle of the destination differs substantially from that of the point of departure. The
study reviewed above by Montaruli et al. (2009) s hows how physical training during
morning hours can help return circadian rhythms to normal.
Improving Sleep
Monitoring sleep during training is practical and feasible using sleep logs and actig-
raphy. Sleep logs provide a rough estimate of sleep as they primarily contain the
time the person started trying to sleep and the time that they awoke. Actigraphy
can provide additional objective information, such as the time that the person went
to sleep and the time that they awoke with an accuracy of one-min. The athlete
needs to wear only a wristwatch-size actigraph to bed every night. Data need to be
downloaded only once each week and sleep scoring has been simplified to a menu
selection. The Zeo is a new sleep-monitoring system that can be used to monitor
sleep (http://www.myzeo.com/). Data are archived on a website in order to keep
track of sleep quality over time. Bedtime, alcohol consumption, and diet can all be
modified as necessary to keep sleep scores up.
Diurnal Activity
Low resolution. Actigraphs normally measure from 10 to 30 times per second and
average over one-min epochs. This level of temporal resolution is much more
detailed than pedometer data and frequently adequately characterizes the dura-
tion and intensity of ambulation-based workouts that include running or walking.
The Fitbit system (http://www.fitbit.com/) uses a triaxial accelerometer to track
activity level throughout the day. The Nike+ system (http://www.apple.com/ipod/
nike/, http://nikerunning.nike.com/nikeos/p/nikeplus/en_US/) creates a personal fit-
ness trainer by wirelessly connecting a sensor placed in the heel of running shoes
with an iPhone or iPod to collect data while exercising that is then sent to a server
for further processing.
High resolution. Sometimes greater temporal resolution is helpful in quantify-
ing athletic performance. The RT3 sold by StayHealthy has a one-second recording
epoch, which gives 60 times better temporal resolution than a one-min record-
ing epoch. The ActiTrainer Solution Package sold by Actigraph LLC measures
and records activity level at 30 Hz (30 times per second), and their GT3X model
40 W.W. Tr yon
can sample and record at 80 Hz, which should be sufficient to track a golf swing
or a bowling approach. I could not find published applications of high-resolution
actigraphs for these purposes.
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Chapter 3
Quantitative Analysis of Sports
Derek D. Reed
In 2003, Michael C. Lewis published the book Moneyball: The Art of Winning an
Unfair Game, detailing Billy Beane’s (the general manager of the Oakland Athletics
Major League Baseball [MLB] team) contemporary use of advanced statistical
methods to draft or select players and to devise strategic approaches to game play
in hope of launching the team into the competitive echelon of the MLB, despite the
numerous odds against them. To the novice reader or sports enthusiast, this sim-
ple description seems an endearing tale of an underdog’s success, and rightly so –
the Oakland Athletic’s, with a salary budget of only $41 million, competed against
teams with much higher salaries, such as the New York Yankees with $200 million
to spend on its players. Transcending beyond this “triumph over adversity” tale,
however, Lewis’s Moneyball has become a panacea for analytically maximizing out-
comes from an economic approach. Perhaps more importantly, Moneyball has both
glamorized and popularized the previously obscure and seemingly excessively aca-
demic use of advanced statistics of sabermetrics in measuring within-game/season
performance to judge success to predict future outcomes. As David Grabiner (n.d.)
describes in The Sabermetric Manifesto,
The most common uses of statistics are to evaluate past performance (such as to determine
who should win the MVP award) and to predict future performance (such as to evaluate
a trade that was just made). In both cases, [sabermetricians] are interested in measuring
contribution to games won and lost.
Since the publication of Moneyball, this use of sabermetrics – termed after the
Society for American Baseball Research (see http://www.sabr.org) – has been
adopted by many MLB teams, who now employ full-time sabermetricians or
statisticians as part of their staff (Heller, 2010).
The argument to abandon simple statistics and to utilize advanced quantitative
measures in elite sports was first publicly and seriously advanced with Cook’s
1964 publication of Percentage Baseball (Schwarz, 2005). Cook, a metallurgist and
D.D. Reed (B)
Department of Applied Behavioral Science, University of Kansas, Lawrence, KS 66045, USA
e-mail: [email protected]
43
J.K. Luiselli, D.D. Reed (eds.), Behavioral Sport Psychology,
DOI 10.1007/978-1-4614-0070-7_3,
C
Springer Science+Business Media, LLC 2011
44 D.D. Reed
consultant on the Manhattan Project whose only real baseball experience was in
college, embarked on his pursuit to refine baseball analyses using probability theory
simply in an effort to prove that Ty Cobb was indeed a better batter than Babe Ruth
(Schwarz, 2005). What resulted from Cook’s analyses of professional baseball was
a litany of findings, suggesting gross overuse of the sacrifice bunt, inadequate uti-
lization of relief pitchers, problems with traditional batter order arrangements, and
many other findings shaking the foundation of baseball lore concerning strategy and
play. As such, in a review of Cook’s findings, Frank Deford of Sports Illustrated
(1964) titled his editorial piece “Baseball I s Played All Wrong.”
Despite the fact that contemporary quantitative analyses of athletics emerged
from the sport of baseball, interesting findings abound away from the ballpark.
In 2010, Kevin Kelley, the head coach of the Pulaski Academy High School
American-rules football team (hereafter, American-rules football will be simply
termed “football”) in Little Rock, Arkansas, was a panel presenter at the MIT Sloan
Sports Analytics Conference (Baxamusa, 2010). How a high school football coach
found himself presenting at an elitist sports conference is explained through his
novel approach to offensive play calling – he does not punt on fourth down. After
reading statistical accounts akin to those employed in sabermetrics, Kelley learned
that, statistically, the offense converts over 75% of fourth down plays to first down
(see Easterbrook, 2007). Moreover, with a 33% chance of an offensive play series
resulting in scoring, calling a rush or pass on fourth down increases scoring oppor-
tunities and subsequently decreases opportunities for the opposing team to score.
Since Kelley has adopted this offensive strategy, he has led his varsity football team
to multiple state titles.
Like the example of Kevin Kelley and football, the translation of sabermetrics to
basketball also has its success stories. Mark Cuban, a brash entrepreneur, worked
his way up from selling garbage bags door-to-door in New Jersey to starting and
selling companies that yielded enough profit to finance his purchase of the Dallas
Mavericks organization of the National Basketball Association (NBA) in 2000
(D’Angelo, 2006). Prior to Cuban’s purchase and management of the Mavericks, the
team’s average winning percentage during the 1990s barely broke 30%. During his
first year of ownership, Cuban, customarily sitting in the seats among fans during
Mavericks games, encountered Wayne Winston, Cuban’s former statistics teacher
(Hruby, 2004). Cuban and Winston subsequently discussed ways to improve the
team and, in doing so, decided to follow the lessons from the pages of Moneyball
and employ more precise and analytic quantitative approaches to management.
Consequently, Winston became a statistical consultant for the Mavericks and has
since written a widely acclaimed book on quantitative approaches to sports (see
Winston, 2009). Similar to Beane’s transformation of the Oakland Athletics, fol-
lowing Cuban and Winston’s statistical approach guiding player and play selections,
the Mavericks have become a competitive organization in the NBA, with nearly 70%
winning record under their tutelage.
With sabermetrics gaining national press and serious consideration in baseball
(Neyer, 2002), and the sports of basketball and football slowly translating this
3 Quantitative Analysis of Sports 45
science to practice in their respective arenas
1
, a group of sabermetricians banded
together to form the Journal of Quantitative Analysis in Sports (http://www.bepress.
com/jqas/) t o provide a singular outlet for researchers interested in the subject –
previous to this, such researchers had to find publication homes in multidisci-
plinary academic journals or had to simply compile their findings and thoughts
for books and book chapters (see Alamar, 2005). Given the aforementioned exam-
ples, it is evident that quantitative analyses of sport are (a) gaining interest in pop
culture (e.g., Moneyball, 2003), (b) becoming increasingly used in sports man-
agement (e.g., Mark Cuban), and (c) growing as a legitimized subfield of science
and sport (e.g., Journal of Quantitative Analysis in Sports). As such, the behav-
ioral approach to sport psychology may benefit from adopting and expanding upon
these techniques. Moreover, given the success of quantitative analyses of operant
behaviors in the lab and field (e.g., Marr, 1989; Mazur, 2006; McDowell, 1988;
Nevin, 2008; Shull, 1991), behavioral sport psychology researchers may offer the
traditional sabermetricians additional variables to consider in their practice.
The difference between a behavior analytic approach and quantitative analy-
sis from the sports examples provided above, however, lies in the derivation and
application of the mathematical models themselves. In particular, when sabermetri-
cians report on a quantitative model of behavior, what they are actually reporting is
novel means of data analysis. To quote J. C. Bradbury, the author of The Baseball
Economist: The Real Game Exposed (2007), sabermetrics is a science that “[eval-
uates] players based on a few readily available statistics” (p. 150). To be more
specific, such quantitative analyses typically rely upon multiple regression mod-
els where a smorgasbord of variables are entered into a program to see which are
most related to a sport statistic (e.g., batting average in baseball) of interest. While
this quantification of sport is useful for scouting reports and managerial decisions,
it does little in describing processes underlying sport behavior. The quantitative
analyses employed by behavior analysts, however, are used to evaluate whether a
particular hypothesis of a behavioral phenomenon – one based upon repeated mea-
sures of steady-state responding in well-controlled experiments and organized into
an equation – better accounts for both laboratory and field observations of behav-
ior than other hypotheses (i.e., other quantitative models of behavior). To quote
James Mazur (2006), quantitative models of behavior are advantageous in that
“[t]ranslating a verbal hypothesis into a mathematical model forces a theorist to
be precise and unambiguous, and this can point to ways of testing competing theo-
ries that sound as if they make similar predictions when they are stated in words”
(p. 287). In essence, quantitative analyses of behavior are used to both describe and
predict patterns of behavior, with the goal to elucidate a behavioral process central
to the observations. These models then compete in the peer-reviewed literature to
1
The application of advanced statistical analyses in basketball has been termed APBRmetrics,
after the Association for Professional Basketball Researcher (see http://www.apbr.org). In football,
analysts simply borrow the term sabermetrics (e.g., Campos & Chait, 2004), despite its origin from
baseball.
46 D.D. Reed
find one quantitative model that best accounts for the observations shared among
behavior analytic labs. Thus, while the application of quantitative analysis differs
substantially between sabermetricians and behavior analysts, both parties agree that
much may be gained through the use of equations to describe a behavior of interest,
not just relying on verbal accounts by savvy commentators or well-versed academic
writers.
Quantitative Analyses of Behavior
In order to understand sport performance within the auspices of a quantitative model
of behavior, one must accept the notion that sport performance is indeed under the
control of the environment and, thus, susceptible to operant principles. Undoubtedly,
the chapters of the present volume provide an abundance of support for this concep-
tion. Nevertheless, recall Grabiner’s (n.d.) quote from The Sabermetric Manifesto
in the opening paragraph of this chapter, which states the focus of sabermetrics is in
understanding past data to predict future performance in an effort to improve sport
behaviors (e.g., batting orders in baseball). Compare this notion with B. F. Skinner’s
(the father of operant behaviorism) quote from his 1953 treatise on what it means to
have a behavioral analytic science of behavior:
Science not only describes, it predicts. It deals not only with the past but with the future. Nor
is prediction the last word: to the extent that relevant conditions can be altered, or otherwise
controlled, the future can be controlled. If we are to use the methods of science in the field
of human affairs, we must assume that behavior is lawful and determined. (p. 6)
Given the similar interests driving sabermetricians and behavior analysts, it is of no
surprise that behavioral researchers have recently begun applying their quantitative
analyses to athletic performance. That behavior analysts turn to quantitative models
should be expected, given the benefits of doing so described above and in the liter-
ature (see Critchfield & Mazur, 2006; Reed, 2009). For example, framing behavior
within a quantitative model provides the most parsimonious explanation for how
and why the behavior is controlled by the environment. Second, the quantitative
model itself provides a succinct description of a behavioral phenomenon for pre-
diction by organizing the phenomenon into a core functional relation in the form of
an equation. Third, the equation itself organizes the environment-behavior relations
using parameters – both constant and free – as predictors and descriptors of behav-
ior. Finally, the model is tolerant to deviations from the core relation and factors in
modulating variables that govern these behavioral variants.
Numerous applications of quantitative models have been made in behavioral
approaches to human behavior (e.g., Davison & McCarthy, 1988; Herrnstein,
Rachlin, & Laibson, 1996; Hull, 1943; Hursh, Raslear, Shurtleff, Bauman, &
Simmons, 1988; Madden & Bickel, 2010;Nevin,1988, 2008). However, the two
quantitative models most relevant and directly translatable to the study of sport
behavior are the matching law and discounting. Thus, the remainder of this chapter
will specifically focus on (a) the research behind these models, (b) how these mod-
els fit into the framework of quantitative analysis of behavior as outlined above, and
3 Quantitative Analysis of Sports 47
(c) specific research questions related to sport that have been addressed, or may be
addressed, by these behavioral models.
Matching
Arguably the most influential quantitative model of behavior, the matching law
(Herrnstein, 1961), originates from the basic experimental analysis of pigeons’
key pecking in a well-controlled experimental study. Herrnstein’s seminal study
describes a simple concurrent operants arrangement in which pigeons could earn
access to grain by pecking on one of two keys. Both keys featured concurrent
variable interval schedules of reinforcement, such that pecking on a key after a
specific amount of time had passed since the previous peck would result in access
to reinforcement. Since both keys were concurrently available, pecking one key
did not influence the schedule on its alternative. What Herrnstein found was that
when he manipulated the schedules, the pigeon’s rate of pecking on the keys pro-
portionally “matched” the reinforcement schedule. As such, Herrnstein’s matching
equation simply states that relative rates of behavior will “match” relative rates of
reinforcement. In mathematical form,
B
1
(
B
1
+ B
2
)
=
R
1
(
R
1
+ R
2
)
(1)
the matching law equation states that the proportion of a particular behavior (B
1
)
to the total amount of behavior emitted (B
1
+ B
2
) is equivalent to the proportion
of reinforcement obtained for that behavior (R
1
) to the total amount of reinforce-
ment obtained (R
1
+ R
2
). This relatively simple equation dramatically shifted the
analysis of basic operant principles and has become a mainstay in experimental anal-
yses of behavior. Moreover, this basic notion of matching has accounted for many
real-world behaviors such as teen pregnancy (Bulow & Meller, 1998), academic
behaviors (Billington & DiTommaso, 2003; Reed & Martens, 2008), and attend-
ing to conversational peers (Borrero et al., 2007; Conger & Killeen, 1974). Most
germane to this discussion, however, is the recent translation of the matching law
to sports (e.g., Alferink, Critchfield, Hitt, & Higgins, 2009; Reed, Critchfield, &
Martens, 2006; Romanowich, Bourret, & Vollmer, 2007; Stilling & Critchfield,
2010; Vollmer & Bourret, 2000).
In the aforementioned extensions of the matching law to real-world behaviors,
these translational researchers utilized a derivation of Herrnstein’s 1961 equation,
which permits and quantifies deviation away from strict matching using the gener-
alized matching equation (Baum, 1974). The generalized matching equation states,
log
B
1
B
2
= a
R
1
R
2
+ log b (2)
where the logarithmically transformed behavior (B
1
/B
2
) and reinforcement (R
1
/R
2
)
ratios derive from Equation (1), such that unit changes in one ratio are proportional
48 D.D. Reed
to unit changes in the other. The a parameter represents the slope of the best-fit line
of the correlation between the two ratios, with log b representing the y-intercept of
that line. In a behavior analytic sense, a represents the sensitivity of an organism’s
behavior to changes in the reinforcement ratio. Moreover, log b may be considered
the bias that the organism has for some response alternative (i.e., preference for an
alternative that cannot be accounted for by the reinforcement ratio alone).
In the graphic format, the core relation is viewed by behavior analysts as how
close the best-fit line resembles strict matching in the sense of the line’s slope near
1 and bias of zero ( see left panel of bottom row of Fig. 3.1). Strict conformance
would translate to a perfect relation between the behavior and reinforcement ratios,
Behavior Ratio
log (B
1
/B
2
)
Reinforcement Ratio
log (R
1
/R
2
)
log b = Bias
ΔX
ΔY
ΔX
ΔY
= a =
Sensitivity to
Reinforcement
Behavior Ratio
log (B
1
/B
2
)
Reinforcement Ratio
log (R
1
/R
2
)
log b > 0.00
Bias for
Behavior 1
Bias for
Behavior 2
log b < 0.00
Overmatching
a > 1.00
a < 1.00
Undermatching
Sensitivity (a) = 1.00
Bias (log b) = 0.00
Examination of:
Conformance to
Matching Law
Sensitivity to
Reinforcement
Preference for
Response Alternative
Fig. 3.1 Top panel depicts how a matching analysis is plotted on a coordinate plane, with specifi-
cation of how the matching analysis is derived from the slope and y-intercept of the least-squares
linear regression best-fit line. Bottom panel depicts three analyses that can be done using the gen-
eralized matching equation (Equation 2), with associated examples of data and their interpretation
3 Quantitative Analysis of Sports 49
as is expected in Equation (1). When behavior analysts look to understand the
influence of an environmental event (i.e., a modulating factor) on an organism’s
sensitivity to reinforcement, they examine how deviant the slope of the best-fit line
is away from 1 (see middle panel of bottom row of Fig. 3.1). In examining sensitiv-
ity modulation, when the observed slope is greater than 1, the organism’s behavior is
termed as “overmatching,” as the organism’s behavior has overmatched proportional
changes in reinforcement – undermatching thus implies insensitivity in the organ-
ism’s behavior to changes in reinforcement. Finally, bias modulation (see right panel
of bottom row in Fig. 3.1) is understood by behavior analysts through examination
of the y-intercept’s relation to the scatter plot’s origin. Positive y-intercepts imply a
bias for Behavior 1, with negative y-intercepts implying a bias for Behavior 2.
In the first translation of the matching law to sport behavior, Vollmer and
Bourret (2000) analyzed the allocation of two- and three-point shots by 13 male
and 13 female National Collegiate Athletic Association (NCAA) Division I bas-
ketball players. In NCAA basketball, an arc designated with a painted line extends
from the center of the hoop with a radius of 6.02 m (19 ft 9 in). When a player
makes a shot from beyond this line (i.e., greater than 6.02 m from the hoop), that
player’s team is awarded three points. Shots (not counting free throws) made within
the line are rewarded only with two points. Thus, at any given point during game
play, a player with the ball has the choice to take a three-point shot, or advance
closer to the basket for a two-point shot. Vollmer and Bourret viewed this arrange-
ment as a natural extension of Herrnstein’s (1961) choice paradigm with pigeons.
As such, these researchers sought to examine a real-world sports example of match-
ing by analyzing the proportion of two- and three-point shots and comparing this
against the proportion of the number of points obtained for each shot type using
Equation (2). As predicted by the matching law, the proportion of shots taken nearly
perfectly matched the proportion of reinforcement the players obtained for making
those shots (see top panel of Fig. 3.2).
In addition to simply capturing molar shot selection–reinforcement relations (that
is, summarizing large amounts of data collectively, rather than looking at game-to-
game performance), Vollmer and Bourret (2000) also sought to determine whether
they could predict future shot selections. Toward this end, Vollmer and Bourret cal-
culated the running aggregate allocation of shots from all previous games following
each game to make a prediction about the allocation of shots for the next game
(see bottom panel of Fig. 3.2). These researchers found their predictions became
more and more accurate across the course of the season. Thus, not only does ana-
lyzing data at the molar level (i.e., analyzing data at the end of the season) within a
matching framework describe shot selection as an operant behavior, but this analytic
approach may also be translated to game-by-game data to predict future behaviors.
Following Vollmer and Bourret’s (2000) lead, Reed et al. (2006) sought to repli-
cate the finding that sport behavior is explainable using quantitative models of
operant learning. In traditional football, the offense (the team with the ball) has
four chances (i.e., downs) to advance the ball 10 yards. Upon advancement of 10
(or more) yards, the offense is allotted an additional four down to advance 10 addi-
tional yards. The goal of advancing the ball down the field is to cross the ball over the
50 D.D. Reed
Vollmer & Bourret, 2000
Basketball
2pt vs. 3pt Shots
Men Women
Slope = .91
Bias
= –.02
Slope
= 1.05
Bias
= –.07
Behavior Ratio
Reinforcement Ratio
Predicted using
weighted equation
Actual
Games (Across Season)
Proportion of Shots
Take From 3 pt Range
0
100
Fig. 3.2 Top panel depicts
two generalized matching
equation analyses
(Equation 2)ofVollmerand
Bourret’s (2000) study on 2
vs. 3 point shots as operant
choice. Bottom panel depicts
Vollmer and Bourret’s
concatenated analyses to
examine the ability of the
matching relations to predict
future allocations to 2 vs. 3
point shots
threshold of the opposing team’s end zone. Doing so awards the offense six points
and an extra point opportunity. To advance the ball, the offense has two options: (1)
pass the ball (throwing to a receiving player) or (2) rush the ball (handing the ball
to a player to run upfield). Given this simple two-choice arrangement, as well as the
clear identification and quantification of reinforcement (i.e., yards gained), Reed and
colleagues posited that offensive play calling could be explained using Equation (2).
In particular, Reed et al. examined the offensive play calling of elite football teams
to determine if the relative proportion of passing to rushing plays approximated the
relative proportion of yards gained passing to yards gained rushing. Indeed, these
researchers found that Equation (2) did an excellent job in explaining offensive
play calling across numerous elite football leagues (e.g., National Football League
[NFL], Arena Football League, National Women’s Football Association [NWFA],
several large NCAA conferences, etc.; see top panel of Fig. 3.3).
Similar to Vollmer and Bourret’s (2000) analysis, Reed et al.’s (2006) analyses
went beyond simply describing the relationship between responses and reinforce-
ment. In one analysis, Reed and colleagues posited that the bias parameter (log b)
of Equation (2) could be used to analyze preferences for play types across downs
during game play in the NFL. Specifically, Equation (2) was applied to passes and
rushes as described above and was analyzed across first, second, and third down
play situations. As armchair quarterbacks are aware, traditional football lore sug-
gests that teams run the ball on first down and throw the ball on third down. The
findings from Reed et al.’s study suggest that this belief is supported using match-
ing law analyses (see bottom panel of Fig. 3.3). In their interpretation of these data,
these researchers put forth the notion that the risk of turning over the ball in either
3 Quantitative Analysis of Sports 51
Reed et al., 2006
American Rules Football
Pass vs. Rush
Slope = .73
Bias
= –.13
Slope
= .55
Bias
= –.12
NFL NWFA
Behavior Ratio
Reinforcement Ratio
+.5
–.5
0
Bias (log b)
First Second
Down
Third
Bias for Rushing
Bias for Passing
Fig. 3.3 Top panel depicts
two generalized matching
equation analyses
(Equation 2) of Reed et al.’s
(2006) study on passing
versus rushing as operant
choice. Bottom panel depicts
Reed et al.’s analysis of
teams’ bias for passing versus
rushing as a function of
down, using the bias
parameter (log b)ofthe
generalized matching
equation (Equation 2)
play call would increase t he bias toward the alternative. To examine this, Reed and
colleagues created a turnover rate index for NFL teams, which was derived via log-
arithmic transformation of the ratio of fumbles per rushing play to interceptions per
passing play. The researchers found a significant correlation between turnover rate
and the bias parameter from the matching analyses (obtained using Equation 2),
corroborating their hypothesis. Finally, in an effort to provide external validity to
the operant account of offensive play calling, the researchers correlated the vari-
ance accounted for by Equation (2) with winning percentage across all NFL teams.
Data indicated that the degree to which NFL teams conformed to the matching law
(i.e., variance accounted for by Equation (2)) was significantly correlated with win-
ning percentage – that is, teams that “matched” relatively better according to the
matching law won more games than teams that did not.
Following the field sabermetrics’ example of progressing from simple quantita-
tive descriptions of sport phenomena, contemporary matching law research in sports
has shifted its focus to explaining and predicting factors affecting play. Such pro-
gression is noteworthy, as understanding such data advances the field’s ability to
apply this science to inform coaches and athletes of the kinds of variables and statis-
tics they should consider when making decisions affecting strategy. This level of
scientific translation echoes the notion put forth by Baer, Wolf, and Risley (1968), as
well as Van Houten et al. (1988), that behavior analytic services should provide the
consumer with the most conceptually systematic and effective means of changing
socially important behaviors.
52 D.D. Reed
Division III
Variance Accounted For
(Equation 2)
0
20
40
60
80
100
Sensivity to Reinforcement
(Equation 2)
0.0
0.2
0.4
0.6
0.8
1.0
Variance Accounted For
Sensitivity to Reinforcement
Alferink et al., 2009
Division I
Division II
Fig. 3.4 Bar graph depicting
Alferink et al. (2009)dataon
generalized matching
equation (Equation 2)
variables across Division I, II,
and III college basketball
teams
In a major extension of matching theory to understand factors affecting shot
selection in basketball, Alferink et al. (2009) sought to determine the extent to
which matching law (using Equation (2)) accounted for the variance in 320 Division
I college basketball teams. From these results, Alferink et al. demonstrated that
their large sample resembled s hot selection patterns similar to those reported by
Vollmer and Bourret (2000) and Hitt, Alferink, Critchfield, & Wagman (2007), fur-
ther suggesting that matching theory is a robust phenomenon in basketball. Alferink
and colleagues then investigated the difference in matching between Division I, II,
and III teams. As Fig. 3.4 indicates, more elite teams (i.e., Division I or II) con-
formed to matching theory to a greater extent than less elite teams (i.e., Division
III). Moreover, Alferink and colleagues then compared regulars and substitutes
from these teams, and found that regulars better conformed to matching theory than
did substitutes. In these examples, Alferink et al. demonstrate that a relationship
between matching and success exists – that is, there appears to be advantages to
conforming to matching expectations. Nevertheless, it remains unclear whether bet-
ter teams select players who conform to matching, or whether matching itself makes
a team successful.
In an effort to better explain football offensive play calling, Stilling and
Critchfield (2010) provide a plethora of analyses to complement those utilized
by Reed et al.’s (2006) study. Specifically, Stilling and Critchfield examined sen-
sitivity to reinforcement, bias, and variance accounted for across numerous NFL
play-calling situations. Generally speaking, their analyses indicate that sensitivity
to reinforcement remains stable across downs, yards to the goal line, and score
(i.e., whether winning, losing, or tied). However, their analyses revealed that teams
became more sensitive to reinforcement as the end of the half approached and
decreased as the number of yards needed for a first down decreased. Note that
none of these differences were statistically significant, although they suggested
that a team’s sensitivity to reinforcement remains relatively constant across game
situations.
3 Quantitative Analysis of Sports 53
In addition to evaluating how sensitivity to reinforcement changed through-
out differing situations, Stilling and Critchfield (2010) also examined teams’
preferences for play types using the bias parameter of Equation (2)(they-intercept
of the best-fit line). In each game situation evaluated, the change in bias parameter
was statistically significant ( p ≤ .0002). In particular, bias increased toward passing
across downs (first to third), as the half came closer to a close, as the chances of
losing increased, and as the number of yards needed for both a first down and a goal
increased. While any armchair quarterback would predict these data, the fact that
the matching equation lends some validity to football lore provides evidence beyond
Reed et al.’s (2006) assertion that football play calling is an operant behavior.
Other Quantitative Models of Behavior
Apart from matching analyses, the extent to which quantitative models from the
experimental analysis of behavior can account for sports remains unknown. In fact,
the only other quantitative model from behavior analysis that has been researched
for sports is the behavioral momentum principle (see Chapter 9;Nevin,1988).
However, these sports applications have focused only on theoretical accounts of
momentum – they have failed to actually fit sports data to the quantitative model of
momentum (Mace, Lalli, Shea, & Nevin, 1992; Roane, Kelley, Trosclair, & Hauer,
2004). A third quantitative model – with no applications to sport, to date – comes
from the basic experimental literature on self-control. In particular, the notion of dis-
counting (i.e., the relative devaluation of rewards as a function of increasing effort,
risk, or delay) has a rich history of successfully quantifying organisms’ impulsive
and irrational decision-making (see Madden & Bickel, 2010). It is logical, then, that
this robust quantitative model would also account for decision-making in sports.
For example, consider the reasoning that coaches make as the probability of a suc-
cessful play decreases, as the distance from a goal increases, or as the time left in
a game decreases. In each of these circumstances, a coach’s decision to execute a
particular play or utilize some strategy will change as a function of such variables.
Moreover, it is likely the case that a quantitative model of discounting would account
for these behaviors and decisions. For a discussion of such models, see Madden
and Johnson (2010), Myerson and Green (1995), as well as Myerson, Green, and
Warusawitharana (2001).
Translating Quantitative Analyses to Sports Applications
Given the evidence above for the matching law’s relevance to sports, as well as
the suppositions regarding momentum and discounting, the utility of quantitative
models seems relevant for improving performance. Unfortunately, the research on
these subjects has not yet been extended beyond the ivory tower – that is, what
we know is purely academic. What is lacking in the extant literature on quantita-
tive models of sport behavior is evidence of its utility on the playing field. This is
54 D.D. Reed
difficult for several reasons. First, teams are reluctant, for likely good reasons, to
share non-public data on strategies and statistics. Thus, the extent of our analyses
is limited to basic statistics that do not factor in situational factors (an exception, of
course, is to approach analyses in a manner such as that described by Stilling and
Critchfield (2010); nevertheless, this is labor intensive and requires an extensive
database). Second, when researchers find relations between behavioral phenomena
and athletic success, the logical next phase of translation is to examine functional
relations between the operant process and success. This requires, however, directly
intervening on play calling. As one might expect, it is incredibly hard to recruit
actual athletes/teams/coaches to change their strategies for experimental purposes.
Thus, the external validity of our scholarly pursuits remains untested. If researchers
remain on the sidelines (no pun intended) with their findings, athletes and coaches
will remain skeptical of what these analyses have to offer.
Despite the bleak outlook conveyed in the preceding paragraph regarding barriers
to translation, there are numerous ways in which behavior analytic quantitative mod-
els may contribute to improved performance. First, given that these models seem to
correlate with positive outcomes, it is possible that simply educating coaches and
athletes about these findings will prompt them to hone in on the relevant statistics
that comprise the quantitative analyses. For instance, showing a coach the team’s
sensitivity to reinforcement (and explaining what this metric means and how it is
calculated) may clue him/her into how efficiently their play calls yield success-
ful outcomes. Providing this feedback and reviewing these statistics after every
game may improve subsequent play-calling profiles; this will hopefully result in
more points/goals/wins/etc. Second, the mere demonstration that sport behavior
conforms to a quantitative model of the same operant principles that govern other
human behavior lends credibility to the notion that a behavioral approach to sports
performance improvement is a worthwhile venture.
Data Considerations
The premise of this chapter is to introduce the quantitative analysis of behavior
as a precise means to assess the relevancy of behavioral processes to sport, while
remaining conceptually systematic to the behavioral orientation to sports psychol-
ogy. As discussed throughout this chapter, quantitative models provide an efficient
means to organizing one’s research questions while objectively and quantitatively
assessing differing behavioral models’ abilities to explain the sport phenomena of
interest. Prior to such investigations, interested researchers – however novice or
experienced – should take precautions to retain the fidelity of their quantitative
analyses.
Like many translational approaches to science, behavior analytic data collection
outside of the laboratory is fraught with possible confounds. Sport is no different.
Analyses of sport, however, are somewhat more direct as the data of interest are typ-
ically readily available on team websites or can be easily gleaned from published
records and statistics. Moreover, the sport variables of interest to behavior analytic
3 Quantitative Analysis of Sports 55
researchers are often directly assessed within the game or sporting event itself (e.g.,
field goals attempted and made in basketball, passing plays called in football, home
runs in baseball). Fortunately, the third-variable confounds (i.e., some existing vari-
able that moderates the relation between two variables of interest) are often just as
easily accessible. Having these kinds of data also in hand permits more interesting –
and perhaps more meaningful – analyses of behavior. Controlling for these kinds of
variables is paramount for assuring that one is truly examining the model’s ability
to explain the relation between the variables of interest.
Sources of Data
Once the researcher has finalized his/her research question – and has hypothesized
ways to analyze the research question while best reducing confounds – he/she must
find a data source to complete the analysis. As described above, the logical first step
in this process is to simply consult team or league websites (e.g., http://www.nba.
com, http://www.nfl.com, http://mlb.com). Most team or league websites publish the
kinds of statistics that are reported in box scores or described in game/event sum-
maries (i.e., the data most recognized and understood by novice sports fans and the
lay public). Moreover, television channels devoted to sports often post statistics on
their sites (e.g., http://espn.com, http://msn.foxsports.com). While many basic anal-
yses can be appropriately conducted using these kinds of data, situational data are
often much more meaningful to the research questions of interest to teams or aca-
demic communities. Situational data are those that are parsed by sport situations –
that is, sport behavior in context (e.g., football play calling on fourth down in the red
zone, baseball pitch s election on a 3–0 count against a power hitter when no runners
are on base, probability of calling a full-court press on an in-bounds pass following
a goal when the score is tied in basketball).
Obtaining situational sport data may be difficult if one does not have propri-
etary rights to the data sets – unless, of course, one wishes to watch and score a
sporting event in real time, coding for situational variables along the way time left
in the game, score, location on the court/field, etc. Fortunately, many useful data
sets are available online for basic situational analyses. For example, the website
http://82games.com offers detailed NBA statistics, such as teams’ shot selections,
shooting data by position, shot clock usage, and others. For MLB enthusiasts,
the website http://www.retrosheet.org offers play-by-play and box score data for
most MLB games since 1952 (box score data are available for games since 1920).
Unfortunately, few free access sites exist for researchers interested in NFL data –
perhaps due to the rising popularity of fantasy football leagues. However, for serious
researchers, premium websites such as http://www.twominutewarning.com or http://
www.footballoutsiders.com offer advanced statistics and detailed play summaries
for a fee. Perhaps more important, many of these premium websites – particularly
the two referenced – are maintained by expert sabermetricians.
Researchers, coaches, or devoted sport aficionados interested in coding their own
sporting events f or eventual quantitative analysis have several options available to
56 D.D. Reed
assist such pursuits. For the reader with an interest in football, CompuSports, Inc.
(http://www.compusports.com) offers several products that aid in data organization
and analysis. For instance, both Easy-Scout XP Plus
©
and Easy-Scout XP
Professional
©
offer a data entry system in which all aspects (e.g., hash side, down,
distance, field position, yards gained) of an offensive or defensive play can be
entered. With such data, the user can create reports that generate statistics cate-
gorized by variable (e.g., percentage of passing plays with a gain in yards when the
game situation is a third down with more than seven yards to go). Moreover, the
Easy-Scout XP Professional
©
software integrates video coding and editing so that
the user needs only to use videos of games or plays to generate data. Reducing the
need for real-time data coding during the live game permits more time for actual
coaching or spectating. Also available from CompuSports
©
are statistical software
packages that integrate with Personal Desktop Assistants [PDAs] to allow for real-
time recording of player statistics that synchronize with the statistics database for
eventual data analyses.
Similar to the football software described above, TurboStats Software Company
©
(http://www.turbostats.com) offers the ScoreKeeper
©
program for baseball/softball
and basketball that uses PDAs to record real-time events into a statistics database.
This company also offers a TurboStats
©
program, which uses these data to generate
advanced analyses based upon situational events. For example, interested users can
generate baseball reports that delineate batting statistics by pitch count, pitch type,
or pitch location. Basketball reports can provide the percentage of shots made by
location on the court or statistics broken down by home/away games.
Analyzing Data
Quantitative analyses of behavior (i.e., sport or otherwise) necessitate the use
of advanced software to statistically analyze data and to fit quantitative mod-
els to the data sets. While Microsoft Office Excel
R
is the spreadsheet program
perhaps the most widely used by the lay public, many of the relevant analyses
cannot be handled with this software alone. Nevertheless, for those readers with
access to Excel
R
, rest assured that some of the basic quantitative analyses could
indeed be conducted within this program. For example, to conduct matching law
analyses, one need only to use the regression function available in the Analysis
ToolPak add-in, which comes standard in Excel
R
. For more information on using
Excel
R
for matching analyses, the reader should consult Reed (2009). Moreover,
if one is interested in obtaining area-under-the-curve estimates for discounting
plots, Excel
R
can easily be programmed to compute this metric using the trape-
zoidal rule (a simple Web search of the term “trapezoidal rule in Excel” [without
quotation marks] will yield a plethora of examples of how to do this). With rela-
tively simple programming, Excel
R
can aid in many quantitative research pursuits.
Such programming requires extensive knowledge of Excel
R
functions and codes,
as well as an intimate understanding of the quantitative models relevant to the
research question. However, with other software packages available that require
3 Quantitative Analysis of Sports 57
little to no additional programming for quantitative models, the serious researcher
might invest in products such as IBM SPSS Statistics Base
R
(http://www-01.ibm.
com/software/analytics/spss/products/statistics/base/), SigmaPlot
R
(http://www.
sigmaplot.com), and/or GraphPad Prism
R
(http://www.graphpad.com/prism/prism.
htm). These advanced programs are much more user friendly concerning nonlin-
ear regression models, as well as selecting appropriate statistical tests. Moreover,
these products generate professional-quality graphs and data displays, which require
much less postanalysis editing than Excel.
Summary
In many ways, the current state of the behavior analytic approach to the quantitative
analysis of sport is akin to that of the sabermetrician’s in the mid-1960s when Cook
first published Percentage Baseball (1964). That is, similar to Cook’s analyses pub-
lished in 1964, behavior analysts have translated quantitative analyses to sport, and
have happened upon interesting findings. Moreover, much like early sabermetrics,
behavior analysis has yet to apply these findings to promote meaningful improve-
ments in sport performance. As further refinement of sabermetrics enhances sport,
more athletes and coaches will begin to take notice of and adopt these analyses into
everyday practice.
Through volumes such as this book, coaches and athletes may begin to under-
stand the value of behavior analysis in sport. Recognition of both the analytica
approach and the utility of behavioral principles by coaches and athletes may be
guided by the practice of behavioral researchers. For instance, a common complaint
regarding behavioral studies is the extensive use of jargon. As Lindsley proposes
(1991), behavioral researchers – in the present case, those interested in sport –
should write to the lay public to better translate their wares to the needs of their
athletic consumers. Another way to broaden the impact of quantitative analyses on
actual coaches and athletes is to present findings from such quantitative analyses
at sports conferences or through submission to sports psychology journals. Far too
often, laboratory-based quantitative analyses are marketed to researchers, not con-
sumers likely to benefit from understanding these findings. Finally, it is important
for quantitative researchers to acknowledge that when their studies are reported, it
would benefit the consumer to hear the researchers’ thoughts about how these anal-
yses may be applied in the “real world” of sport psychology. And also, researchers
should offer direct suggestions for how to test these models in either simulated or
actual sporting events.
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Chapter 4
Single-Case Evaluation of Behavioral
Coaching Interventions
James K. Luiselli
Behavioral coaching involves intervening with athletes to improve their skills and
competitive success. As presented in this book, intervention procedures are most
effective when they are evidence based and empirically supported. Whatever the
coaching venue, behavioral sport psychology professionals such as clinicians and
consultants should be responsible for not only implementing the most potentially
effective procedures but also carefully evaluating the effects of those procedures.
This chapter concerns the application of single-case evaluation designs within
behavioral coaching. Specifically, my intent is to show how behavioral sport psy-
chology professionals can incorporate single-case methodology for assessing the
impact of instructional and training interventions on athletic performance. The chap-
ter briefly considers principles of single-case evaluation designs and then describes
four designs that have practical application within behavioral coaching. I also review
research studies that illustrate design adaptations within several sports.
Single-case evaluation designs are sometimes referred to as single-case research
designs and N = 1 research, but neither label is precise. For example, although
single-case designs have a rich tradition in clinical and applied research (Kazdin,
2011), they have utility beyond a formal research investigation. As indicated, the
premise of this chapter is that behavioral sport psychology professionals should
incorporate single-case methodology to evaluate their coaching and training rec-
ommendations. Note, too, that “single-case” does not mean that only one person is
evaluated at a time or that groups of individuals are excluded. In actuality, single-
case research designs can examine an intervention across several people and have
included large groups. Accordingly, for this chapter I chose the term single-case
evaluation designs (a) to highlight the emphasis on person-specific performance and
(b) to avoid the perception that the designs are used solely for research.
Before describing basic principles of single-case evaluation designs, it is instruc-
tive to contrast them with more traditional group, or “large N,” methods. Typically,
group research recruits multiple participants (subjects) from either normative or
J.K. Luiselli (B)
May Institute, Randolph, MA 023681, USA
e-mail: [email protected]
61
J.K. Luiselli, D.D. Reed (eds.), Behavioral Sport Psychology,
DOI 10.1007/978-1-4614-0070-7_4,
C
Springer Science+Business Media, LLC 2011
62 J.K. Luiselli
clinical samples, or the population at large. Participants are matched on inclusion
criteria and then assigned to control and treatment groups. Most often, the depen-
dent variables in group research are measures acquired from standardized tests and
rating instruments. These data are averaged per group, and treatment efficacy and
effectiveness are determined by between-group statistical analyses (e.g., chi-square,
t-tests, ANOVA).
With single-case evaluation designs, each person serves as her/his control. The
effects of a treatment or intervention are ascertained by repeatedly measuring one or
more relevant behaviors. Measurement is usually conducted through direct obser-
vation in real time or from video recordings. Visual inspection of measurement
data determines whether a person’s behavior changes in response to the presence
or absence of intervention.
Keeping with the practitioner focus of this chapter, single-case evaluation designs
enable a behavioral sport psychology professional to target performance-specific
measures vital to the success of an individual athlete. In essence, these measures
are the skills needed for peak performance during practice and competitive events.
Stated succinctly by Freeman and Lim (2010), single-case methodology is “an idio-
graphic approach for describing, examining, and comparing the performance of
an individual against his or her own performance at different points in time or in
different settings” (p. 397).
Principles and Operations
Although this chapter is not an exhaustive and detailed presentation of single-case
evaluation designs, it is, nevertheless, important to grasp basic operational logic
and underlying principles. Readers seeking more in-depth coverage of single-case
methodology should consult exemplary texts by Kazdin (2011) and Barlow, Nock,
and Hersen (2008).
Measurement. Direct measurement of critical behaviors is at the heart of single-
case evaluation designs. In the realm of sport psychology, athletic performance rep-
resents desirable behaviors that should occur frequently and undesirable behaviors
that should occur infrequently or not at all. To illustrate, the measure of improved
free-throw shooting by a basketball player would be percentage of successful
shots. A behavior-reduction measure would be fewer penalties incurred by an ice
hockey team. Importantly, performance measures must be defined in behavior-
specific terms so that they can be recorded accurately and are not influenced by
observer bias.
Many single-case evaluations rely on event recording, either behavior frequency
or rate. Frequency data are obtained by counting the number of times a behavior
occurs; dividing the frequency count by some unit of time yields a rate measure.
Other measurement methods are duration, the length of time that behavior is exhib-
ited, and interval recording, documenting whether behavior did or did not occur
during a specific period of time. There are many considerations when selecting
a measurement method, including, but not limited to, the physical characteristics
4 Single-Case Evaluation of Behavioral Coaching Interventions 63
(topography) of behavior, the performance setting, and the expected outcome from
intervention. A comprehensive review of behavioral assessment and measurement
specific to sport psychology can be found in Tkachuk, Leslie-Toogood, and Martin
(2003). Readers interested in more i nformation about behavioral measurement the-
ory and general application should consult Cooper, Heron, and Heward (2007) and
Martin and Pear (2011).
Baseline evaluation. All single-case evaluation designs begin with a baseline
phase. One purpose of conducting baseline measurement is to document a person’s
behavior in the absence of intervention. Furthermore, the data recorded at base-
line provide a standard by which intervention effectiveness can be judged. Some
single-case evaluation designs as described subsequently, include not only an initial
baseline phase but also one or more replications of baseline conditions.
The length of a baseline phase depends on the trend and stability of the behav-
iors being recorded. The top graph in Fig. 4.1 shows a stable trend across five
hypothetical baseline data points. These results would mean that it is reasonable to
introduce intervention because a positive or negative effect from it could be isolated
(i.e., behaviors during intervention improve or worsen relative to the steady-state
responding in baseline). Conversely, the second and third graphs shown in Fig. 4.1
illustrate unstable trends at baseline. In the second graph, the hypothetical data rep-
resent a behavior to be increased with intervention. The five data points labeled
“decreasing trend” would justify intervention because the person’s performance is
worsening overtime. The five data points labeled “increasing trend” argue against
intervention because the person’s performance is improving. The same concerns
about baseline trends also apply to the third graph. Here, the behavior to be reduced
is increasing and intervention would be needed. When an undesirable behavior is
decreasing during baseline, intervention should be delayed.
Intervention. The type of trend analysis through visual inspection of graphed
data in a baseline phase also extends to intervention. Behaviors selected for inter-
vention also must change in a desirable direction, with minimal variability, and at
a level that is clinically significant. One of the standard guidelines when conduct-
ing a single-case evaluation design is to change only one independent variable at a
time per intervention phase. This convention enables the evaluator to isolate the
controlling influence of single procedures. Of course, there are many situations
in which intervention is comprised of more than one procedure. In such cases,
it is acceptable to evaluate the intervention relative to baseline and, if warranted,
strategically withdraw and reintroduce procedures to determine which ones are
required.
Internal and external validity. Kazdin (2011) has written extensively about
internal and external validity in the context of single-case evaluation designs.
Briefly, internal validity refers to changes in behavior that can be attributed to
the independent variable and not extraneous (uncontrolled) influences or alterna-
tive explanations. Major threats to internal validity include maturation, history, and
features of the measurement methods. External validity refers to the extent that the
results of a single-case evaluation can be generalized to other people and condi-
tions. Some of the major threats to external validity are person-specific attributes, the
64 J.K. Luiselli
0
2
4
6
8
10
Days
Behavior (Frequency)
To Be Increased or Decreased
12345
0
2
4
6
8
10
Behavior (Frequency)
To be Increased
Days
12345
Decreasing Trend:
Intervention Warranted
Increasing Trend
0
2
4
6
8
10
Days
12345
Behavior (Frequency)
To be Decreased
Increasing Trend:
Intervention Warranted
Decreasing Trend
Fig. 4.1 Examples of
behavior trends during
baseline measurement
situations that constituted the evaluation, and reactivity to measurement. For behav-
ioral sport psychology professionals, internal and external validity have practical
significance, most notably (a) the ability to judge confidently that a coaching
intervention improved an athlete’s performance (internal validity) and (b) that the
coaching intervention should be recommended for other athletes in the same sport
(external validity).
4 Single-Case Evaluation of Behavioral Coaching Interventions 65
Description of Single-Case Evaluation Designs
In this section I describe four single-case evaluation designs, how they are
implemented, and methodological variations that are sometimes indicated. Where
applicable, I highlight behavioral sport psychology research examples of each
design.
A-B-A-B Design
The A-B-A-B design, sometimes called a reversal design, begins with a baseline (A)
phase. During baseline, intervention is not in effect. Once baseline data are stable,
intervention (B) is introduced, followed by a second baseline (A) phase and then a
second intervention (B) phase. This systematic alteration of phases is intended to
show that a person’s performance improves with intervention and is less proficient
when intervention is not implemented.
Figure 4.2 plots hypothetical data in an A-B-A-B design. For the purpose of illus-
tration, imagine that the performance measure is the number of steps in a 10-step
skill sequence that an athlete executes accurately. These data demonstrate that inter-
vention effectively increased the number of accurate steps relative to the baseline
phases. Because the desired effect was replicated convincingly, the results would
predict future levels of performance if intervention was maintained.
Sometimes an A-B-A-B design verifies a modest change in behavior and the
need for additional intervention. Figure 4.3 shows an A-B-A-B evaluation where a
second intervention procedure, “C,” was added to “B.” When this combined inter-
vention boosted performance, it was removed briefly during a reversal-to-baseline
0
2
4
6
8
10
123456789101112
Days
Number of Steps in 10-Step
Skill Sequence Executed Accurately
ABAB
Fig. 4.2 Example of the A-B-A-B single-case evaluation design
66 J.K. Luiselli
0
2
4
6
8
10
123456789101112131415161718192021
Days
Number of Steps in 10-Step Skill
Sequence Executed Accurately
AABBB + CA B + C
Fig. 4.3 Example of the A-B-A-B-B+C-A-B+C single-case evaluation design
(A) phase in which the athlete performed less accurately. The “B+C” intervention
was implemented again with good success. So conducted, this sequence would be
labeled an A-B-A-B-B+C-A-B+C design.
The logic of the A-B-A-B design can also be applied for evaluating questions
about the intensity of intervention. Referencing Fig. 4.4, the hypothetical data
0
2
4
6
8
10
123456789101112131415161718192021
Days
Number of Steps in 10-Step Skill
Sequence Executed Accurately
AAB + CB + CB A B
Fig. 4.4 Example of the A-B+C-A-B+C-B-A-B single-case evaluation design
4 Single-Case Evaluation of Behavioral Coaching Interventions 67
0
2
4
6
8
10
1234567891011121314
Days
Number of Steps in 10-Step Skill
Sequence Executed Accurately
AAABBB
Fig. 4.5 Example of one-day reversal “probes” within the A-B-A-B single-case evaluation design
are plotted in an A-B+C-A-B+C-B-A-B design. The “B+C” intervention clearly
improved performance. Whether both “B” and “C” procedures of intervention were
required was addressed by withdrawing “C,” reversing to baseline (A), and reinstat-
ing “C.” The interpretation of these data would be that the “B” procedure alone was
as effective as implementing it with “C.”
Finally, one-day reversal “probes,” depicted in Fig. 4.5, are an A-B-A-B design
variant for practical application. With this design, the reversal-to-baseline phases
last a single day, inserted between more lengthy intervention phases. Although
a solitary, one-day reversal effect by itself is not convincing, demonstrating the
effect several times is a quick way to make confident recommendations about an
intervention.
Research examples. Allison and Ayllon (1980) used A-B-A-B designs to study
a behavioral coaching intervention with athletes (ages 11–35 years) participat-
ing in football, gymnastics, and tennis. The performance measures were blocking
(football), backward walkovers, front hand-springs, reverse hips (gymnastics), and
forehand, backhand, and service strokes (tennis). During baseline phases for each
sport, the respective coaches carried out their usual procedures with the athletes. The
behavioral coaching intervention was comprised of systematic verbal instructions,
performance feedback, positive and negative reinforcement, modeling, and imita-
tion. Compared to the baseline phases, all of the athletes had a higher percentage
of skill execution when the coaches implemented the behavioral protocols. Keep in
mind that as conducted, the A-B-A-B design in Allison and Ayllon (1980) could not
isolate the controlling effects from the different procedures comprising the superior
behavioral coaching intervention.
As noted previously, the prototype A-B-A-B design has many variations. In a
study with three wide receivers (ages not specified) on a Division II college football
68 J.K. Luiselli
team, Smith and Ward (2006) evaluated several coaching procedures in what would
be described as an A-B-A-C-A-B+C design. The performance measures were the
percentage of blocks, pass routes, and releases from the line of scrimmage, each
wide receiver executed correctly during practices and games. In baseline (A), the
coach reviewed expectations with the players, gave them verbal feedback, and cor-
rected errors. The three intervention phases were public posting of performance
(B), goal setting (C), and public posting of performance with goal setting (B+C).
The t hree coaching interventions were equally effective with the players and bet-
ter than baseline. Note, however, that although the baseline phase in the study was
replicated twice, the three coaching interventions were not repeated beyond a single
application.
Summary. The A-B-A-B design and its options are adaptable to many behavioral
coaching contexts. Yet the design has two constraints. One limiting factor is having
to temporarily withdraw a seemingly effective intervention. That is, if a performance
deficit is revealed during an initial baseline evaluation and subsequent intervention
is implemented with positive result, is it reasonable (some would argue ethical) to
stop the intervention in a reversal-to-baseline phase? Remember that the baseline
reversal phase does not need to be lengthy when conducting an A-B-A-B single-
case evaluation design. Nevertheless, selecting this design must be tempered by the
possible negative outcome of briefly terminating an intervention on both an athlete’s
performance and his/her attitude.
The second concern about the A-B-A-B design is that some behaviors are not
“reversible.” Think of a baseball pitcher who has been taught a cognitive control
strategy that helps him prepare to face batters and throw more strikes. It is unlikely
that the pitcher would abandon the strategy when he is told to do so during a
reversal-to-baseline phase. Another reason that a behavior may not reverse is that
with intervention, the behavior possibly contacts other reinforcing consequences.
For example, visual markers such as colorful tape sometimes are applied to the
sticks of beginning hockey players to teach them proper hand position. After learn-
ing to pass and shoot the puck more accurately through many hours of practice, we
should not expect players to perform less fluently when the markers are removed. In
effect, their skills are no longer dependent on the intervention–as such, withdrawing
it would not be associated with lesser performance.
Multiple Baseline Design
The multiple baseline design (MBD) comes in three forms. In a MBD across behav-
iors, two or more performance measures are targeted for one person. With a MBD
across settings, a performance measure is recorded for one person in two or more
locations. The third MBD format, the MBD across individuals, focuses on the same
or similar performance measures in two or more people.
Figures 4.6, 4.7, and 4.8 show hypothetical data for the three MBDs. Each fig-
ure has two panels, the minimal number required to demonstrate an intervention
effect. Figure 4.6 is a MBD across behaviors, showing an athlete’s execution of
4 Single-Case Evaluation of Behavioral Coaching Interventions 69
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Baseline Intervention
Skill #1
Skill #2
Number of Steps in 10-Step Skill Sequence Executed Accurately
Fig. 4.6 Example of the multiple baseline across behaviors single-case evaluation design
two, 10-step skill sequences. Figure 4.7, a MBD across settings, i s the skill sequence
performance of one athlete during practice sessions and games. In Fig. 4.8, the per-
formance measure is plotted for two athletes. Each of these MBDs is intended to
demonstrate desirable changes in baseline performance following the sequential
introduction of intervention. That is, the designs simultaneously monitor multiple
performance measures under baseline conditions so that the effect of intervention
can be confirmed each time it is applied to them.
Research examples. The MBD across individuals has been the most popular
MBD in behavioral sport psychology research. Ziegler (1987) conducted a study
with 24 beginning tennis players (19–31 years old), establishing three groups,
each comprised of eight participants. The performance measures were executing
70 J.K. Luiselli
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Baseline Intervention
Games
Practice
Number of Steps in 10-Step Skill Sequence Executed Accurately
Fig. 4.7 Example of the multiple baseline across settings single-case evaluation design
forehand and backhand return strokes during weekly lessons. Intervention consisted
of a stimulus cueing technique: having the players focus on and quietly vocalize
“ball tracking” responses for each stroke. The intervention was evaluated by intro-
ducing it sequentially across the three groups of players. Of note, the study by
Ziegler (1987) is a good example of adapting single-case evaluation designs, in this
case an MBD, to groups of athletes and not just one person.
Another example of the MBD across individuals is a study by Kladopoulos and
McComas (2001). Three players (ages 19–20 years) on a women’s NCAA Division
II basketball team participated in the study. The intervention, termed “form train-
ing,” was intended to increase the percentage of successful foul shots and the
percentage of foul shots taken with correct form. Following customary baseline
4 Single-Case Evaluation of Behavioral Coaching Interventions 71
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Baseline Intervention
Athlete #1
Athlete #2
Number of Steps in 10-Step Skill Sequence Executed Accurately
Fig. 4.8 Example of the multiple baseline across individuals single-case evaluation design
measurement, intervention comprised of pre-practice review, social reinforcement
(praise), and performance feedback was implemented successfully with each player.
This MBD illustrates another design option in that two performance measures, foul
shooting success and form, were recorded within baseline and intervention phases.
Be aware that the capacity to collect data on more than one performance measure is
not exclusive to MBDs but is a feature of all of the single-case evaluation designs
described in this chapter.
Finally, Stokes, Luiselli, Reed, and Fleming (2010) designed a study that evalu-
ated three behavioral coaching interventions within a MBD across individuals. The
participants were five offensive linemen (ages 15–17 years) on a varsity high school
football team. The offensive line coach recorded the percentage of steps the play-
ers executed correctly according to a 10-step, task analyzed blocking sequence.
72 J.K. Luiselli
Dan
DFBL DF+
VF
TAG GAME BL
VF7
DF+ GAME
Steve
Russ
Matt
Logan
SEASON 1 SESSION SEASON 2 SESSION
SEASON 1 SEASON 2
PERCENTAGE OF STEPS COMPLETED CORRECTLY
0
20
40
60
80
100
0
20
40
60
80
100
0
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TAG
TAG GAME
135
5
5
67 7
7 9 11 13 15 17
1357911131517
19 21 23 25 27 29 31 33 35 37 39
Fig. 4.9 Evaluation of three coaching interventions in a multiple baseline design across individ-
uals. The shaded horizontal lines represent the normative pass blocking performance of starting
varsity linemen. From Stokes et al. (2010)
Figure 4.9 shows that the different interventions were implemented in staggered
fashion with each player: (a) descriptive (nonverbal) feedback (DF), (b) descriptive
and verbal feedback (DF + VF), and (c) teaching with acoustical guidance (TAG).
The results revealed that the players responded similarly to the intervention pro-
cedures, that in-game performance improved following intervention, and that the
intervention procedures had to be reinstated to support performance during a second
season.
The MBD across behaviors has also been represented in behavioral sport psy-
chology research. Brobst and Ward (2002) evaluated a combined intervention of
public posting, goal setting, and verbal feedback with three high school soccer play-
ers (ages 15–17 years). The same performance measures were selected for each
player: (a) keeping and maintaining ball possession, (b) moving to an open position
during a game restart, and (c) moving to an open position after passing the ball. For
each player, the multi-procedural intervention was implemented sequentially with
4 Single-Case Evaluation of Behavioral Coaching Interventions 73
each of the performance measures. Another example of the MBD across behav-
iors is a study by Boyer, Miltenberger, Batsche, and Fogel (2009) on the effects
of video modeling and video feedback with four competitive youth gymnasts (ages
7–10 years). The intervention was implemented first for performing a backward
giant circle to hand stand, then a kip cast, and then a clear hip circle (all uneven
bar maneuvers). The controlling effects of intervention were demonstrated when
each performance measure improved in response to intervention. Finally, Harding,
Wacker, Berg, Rick, and Lee (2004) conducted a MBD across behaviors with two
adults (ages 33 and 40 years) participating in martial arts training. The objec-
tive of intervention was to improve their punching and kicking techniques during
drill and sparring sessions. Differential reinforcement of technique execution was
implemented first for punching, followed by kicking, and was effective with both
adults.
Summary. Compared to the A-B-A-B single-case evaluation design, the MBD
does not require a reversal-to-baseline phase in order to verify intervention-induced
changes in behavior. Another advantage of the MBD is that it enables the behavioral
sport psychology professional to address “real world” questions about generaliza-
tion. For example, the MBD across behaviors can answer the question, “Does a
coaching intervention implemented for one performance measure extend to mea-
sures not selected for intervention?” Similarly, the MBD across settings could
discern whether intervention conducted in practice changes performance favorably
during competition.
A relative disadvantage of the MBD is that at least two performance measures,
be they the same behavior of several people or one person’s behavior in more than
one setting, may be difficult to arrange in some situations. However, behavioral
coaching usually considers more than a single athletic skill. As for team sports, the
MBD across individuals would be a reasonable choice for evaluating intervention
with several players.
Changing Criterion Design
After the baseline phase, intervention in a changing criterion design is intro-
duced at a predetermined standard (the “criterion”), usually a performance measure
that results in positive reinforcement. When the performance measure consistently
matches the criterion, thereby showing improvement, the criterion is increased
slightly, and so on, until a terminal performance indicator is achieved. The influence
of intervention is demonstrated by showing that the performance measure changes
desirably with each step-wise increase in the criterion.
Figure 4.10 presents hypothetical data in a changing criterion design. The base-
line phase reveals below 30% accurate execution of a 10-step skill sequence. With
intervention, the athlete gains access to something desirable when she/he matches
or exceeds each advanced criterion three times consecutively (denoted by “C” in the
figure). Such results would confirm that the athlete performed better as a function
of intervention.
74 J.K. Luiselli
Days
0
1
1
23
4
5
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9
10 11 12 13 14 15 16 17 18 19
2
3
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5
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7
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9
10
Number of Steps in 10-Step Skill Sequence
Executed Accurately
Baseline Intervention
C
= 10 Steps
C
= 8 Steps
C
= 6 Steps
C
= 4 Steps
Fig. 4.10 Example of the changing criterion single-case evaluation design
There are no hard-and-fast rules for adjusting criterion in a changing crite-
rion design. Most behavioral sport psychology professionals would agree that the
best approach is to advance criteria in steps small enough to ensure success. This
guideline is especially important when many steps make up the skill sequence. An
additional stipulation for evaluating a potentially effective intervention is that a crite-
rion should increase following several successful (usually consecutive) performance
opportunities at that criterion.
Research examples. Fitterling, Martin, Gramling, Cole, and Milan (1988)useda
changing criterion design to evaluate a behavioral intervention for aerobic exercise
training with five adults (ages 33–56 years) who suffered from vascular headache.
Although the participants in this study were not athletes and the focus was not
sport psychology, exercise is certainly a performance measure consistent with the
theme of this book (see Chapter 8). In Fitterling et al. (1988), the adults were given
a personalized exercise program based on their age and activity preferences. The
program was based on Cooper (1977) in which “points” are awarded for exercise
adherence. Following baseline, the adults set performance goals, starting low, and
then increased the goals gradually in criterion steps. Contingent on meeting their
goals, the adults earned back portions of a $100 deposit, received positive perfor-
mance feedback, and were praised. Results indicated that exercise frequency and
fitness increased progressively as the intervention “demands,” the imposed criteria,
became more stringent.
In a changing criterion design study specifically centered on sport psychology,
Scott, Scott, and Goldwater (1997) evaluated a prompting and shaping intervention
with a 21-year-old university pole vaulter. The performance concern preceding inter-
vention was that on planting his pole, the vaulter did not extend his arms completely
prior to take-off. His average hand height during baseline measurement was 2.25 m
4 Single-Case Evaluation of Behavioral Coaching Interventions 75
relative to a maximum arm extension height of 2.54 m. During intervention the
desired height started at 2.30 m, the vaulter was prompted to “reach” as he ran down
the runway, and an audible tone (a conditioned positive reinforcer) sounded when
he broke a photoelectric beam that had been set at the height marker. Contingent on
his success, the specified height was increased in 0.5 m steps to a terminal height of
2.52 m. The changing criterion design revealed that the vaulter extended his arms
correctly each time the desired height was advanced. As illustrated in the previous
research example, the changing criterion design used by Scott et al. (1997) made it
possible to measure performance in direct relation to gradual intervention changes.
Summary. Like the MBD, intervention evaluation in a changing criterion design
does not require a reversal-to-baseline phase. The design is also well suited to
the types of coaching interventions designed by behavioral sport psychology pro-
fessionals. For example, many interventions have the objective of breaking down
a performance skill into its composite steps, teaching each step until it is per-
fected, and tying the steps together so that the athlete performs them fluently (see
Chapter 10, this volume). The steps correspond to progressively increasing criteria
that are linked to the intervention.
The changing criterion design usually targets positive reinforcement interven-
tions such as “rewarding” an athlete when she/he achieves a performance standard.
However, the intervention can be relatively simple as teaching a beginning track and
field runner to clear hurdles. In s uch a case, the intervention could begin by setting
the height of the hurdles low to the ground, reinforcing the runner upon clearing
each hurdle with proper form, and then making the hurdles higher through gradual
height adjustments. Another example would be building physical endurance of ath-
letes in any number of sports by systematically increasing training demands (e.g.,
more weight training repetitions, running longer distances). Of course, it would be
expected that some type of positive reinforcement and feedback would be given to
the athletes for meeting the performance objectives. Whatever the intervention, the
basis for the changing criterion design is evaluating procedures that incrementally
shape skills.
Alternating Treatments Design
The alternating treatments design (ATD) is used principally to compare the
behavior-altering effects of two or more interventions. After baseline data are col-
lected, the interventions are introduced at different times in the same day or one
time on randomly selected, consecutive days. This comparative evaluation continues
until performance differentiates between or among the interventions. Keeping with
the examples presented throughout the chapter, Fig. 4.11 is an ATD in which an ath-
lete’s accurate execution of a 10-step skill sequence is measured in response to two
interventions. Compared to baseline performance, the athlete improved slightly with
intervention #1. By contrast, performance was better on days in which intervention
#2 was implemented. The third phase of Fig. 4.11, customary when conducting an
ATD, verifies the performance-enhancing effect of the most successful intervention.
76 J.K. Luiselli
Days
0
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Number of Steps in 10-Step Skill Sequence
Executed Accurately
Baseline First Intervention Phase
Intervention #1
Intervention #2
Intervention #2
Second
Intervention
Phase
Fig. 4.11 Example of the alternating treatments single-case evaluation design
One reason the ATD can reasonably compare two or more interventions is that it
controls for sequence effects (Barlow & Hayes, 1979). Using an A-B-A-B design,
for example, a behavioral sport psychology professional might try to compare two
interventions by implementing one intervention during the first “B” phase and the
second intervention during the second “B” phase. If an athlete performed more skill-
fully in the second intervention phase, the result could have occurred because she/he
was exposed to an earlier intervention, not necessarily because the second interven-
tion was superior to the first intervention. In an ATD, the influence of intervention
sequence is minimized by rapidly and randomly altering conditions.
Research examples. Osborne, Rudrud, and Zezoney (1990) studied curveball hit-
ting proficiency of five college baseball players (ages not specified) under baseline
and two intervention conditions in an ATD. Before intervention, the players prac-
ticed hitting against a pitching machine that was adjusted to simulate a curveball
thrown at a standard speed. The interventions consisted of marking the seams of
baseballs with either
1
/
4
inch or 1/8 inch orange stripes. Each of these marked-ball
conditions was compared to the unmarked-baseball condition during two batting
practice sessions each day. The ATD showed that curveball hitting proficiency
improved with the marked-ball interventions. This study is a good example of how
“treatments” in an ATD can actually be any number of conditions, contexts, or
procedures that can be manipulated to compare the one(s) that is/are optimal.
Summary. The ATD gives the behavioral sport psychology professional an eval-
uation tactic that addresses a common coaching objective, namely comparing
different methods with individual athletes. The design does not require a reversal-to-
baseline phase and, as noted, is the only single-case evaluation design that controls
for sequence effects. From a research perspective, there is concern about multiple
4 Single-Case Evaluation of Behavioral Coaching Interventions 77
treatment interference – whether the results of intervention in an ATD would be the
same if they were the only intervention that was evaluated (Kazdin, 2011). From
an applied perspective, the possibility of multiple treatment interference is a minor
limitation when considering how rapidly two or more interventions can be evaluated
using an ATD.
Summary and Conclusions
Single-case evaluation designs are useful for measuring the effects of behavioral
coaching interventions on athletic performance objectives. This chapter reviewed
four designs, the principles of each design, and research examples within behav-
ioral s port psychology. Some professionals may conclude that single-case evaluation
designs are not practical and do not translate easily to practice and competitive set-
tings. However, as I have emphasized, single-case evaluation of behavioral coaching
interventions are desirable because they (a) concentrate on the individual athlete,
(b) include direct measurement of performance, (c) are intended to isolate the most
effective procedures, and (d) can be implemented in a relatively brief period of time.
Thus, the designs are compatible with best practices in applied sport psychology
intervention and consultation (Martin, 2011).
What can behavioral sport psychology professionals do to learn more about
single-case evaluation designs? One recommendation is to read noteworthy texts
by Kazdin (2011), Barlow et al. (2008), and Cooper et al. (2007). Peer-reviewed
journals such as the Journal of Applied Behavior Analysis, Journal of Clinical Sport
Psychology, and Journal of Sport Behavior also feature single-case research pub-
lications. Attending annual conferences sponsored by organizations such as the
Association for Applied Sport Psychology is another way to gain knowledge. And
seeking advice and direction from an experienced colleague can advance one’s
understanding of single-case design methodologies and their application to the
athletic arena.
References
Allison, M. G., & Ayllon, T. (1980). Behavioral coaching in the development of skills in football,
gymnastics, and tennis. Journal of Applied Behavior Analysis, 13, 297–314.
Barlow, D. H., & Hayes, S. C. (1979). Alternating treatments design: One strategy for comparing
the effects of two treatments in a single subject. Journal of Applied Behavior Analysis, 12,
199–210.
Barlow, D. H., Nock, M. K., & Hersen, M. (2008). Single-case experimental designs: Strategies
for studying behavior change (3rd ed.). Boston: Allyn & Bacon.
Boyer, E., Miltenberger, R. G., Batsche, C., & Fogel, V. (2009). Video modeling by experts
with video feedback to enhance gymnastics skills. Journal of Applied Behavior Analysis, 42,
855–860.
Brobst, B., & Ward, P. (2002). Effects of public posting, goal setting, and oral feedback on the
skills of female soccer players. Journal of Applied Behavior Analysis, 35, 247–257.
Cooper, K. H. (1977). The aerobics way. New York: Bantam Books.
78 J.K. Luiselli
Cooper, J. O., Heron, T. E., & Heward, W. L. (2007). Applied behavior analysis (2nd ed.). Upper
Saddle River, NJ: Merrill Prentice Hall.
Fitterling, J. M., Martin, J. E., Gramling, S., Cole, P., & Milan, M. A. (1988). Behavioral manage-
ment of exercise training in vascular headache patients: An investigation of exercise adherence
and headache activity. Journal of Applied Behavior Analysis, 21, 9–19.
Freeman, K. A., & Lim, M. (2010). Single subject research. In J. Thomas & M. Hersen (Eds.),
Handbook of clinical psychology competencies (pp. 397–423). New York: Springer.
Harding, J. W., Wacker, D. P., Berg, W. K., Rick, G., & Lee, J. F. (2004). Promoting response
variability and stimulus generalization in martial arts training. Journal of Applied Behavior
Analysis, 37, 185–195.
Kazdin, A. E. (2011). Single-case research designs: Methods for clinical and applied settings (2nd
ed.). New York: Oxford University Press.
Kladopoulos, C. N., & McComas, J. J. (2001). The effects of form training on foul-shooting per-
formance in members of a women’s college basketball team. Journal of Applied Behavior
Analysis, 34, 329–332.
Martin, G. L. (2011). Applied sport psychology: Practical guidelines from behavior analysis (4th
ed.). Winnipeg, MB: Sport Science Press.
Martin, G. L., & Pear, J. J. (2011). Behavior modification: What it is and how to do it (9th ed.).
Upper Saddle River, NJ: Pearson-Prentice Hall.
Osborne, K., Rudrud, E., & Zezoney, F. (1990). Improved curveball hitting through the enhance-
ment of visual cues. Journal of Applied Behavior Analysis, 23, 371–377.
Scott, D., Scott, L. M., & Goldwater, B. (1997). A performance improvement program for an
international-level track and field athlete. Journal of Applied Behavior Analysis, 30, 573–575.
Smith, S.L., & Ward, P. (2006). Behavioral interventions to improve performance in collegiate
football. Journal of Applied Behavior Analysis, 39, 385–391.
Stokes, J. V., Luiselli, J. K., Reed, D. D., & Fleming, R. K. (2010). Behavioral coaching to improve
offensive line pass blocking skills of high school football athletes. Journal of Applied Behavior
Analysis, 43, 463–472.
Tkachuk, G., Leslie-Toogood, A., & Martin, G. L. (2003). Behavioral assessment in sport
psychology. The Sport Psychologist, 17, 104–117.
Ziegler, S. G. (1987). Effects of stimulus cueing on the acquisition of ground strokes by beginning
tennis players. Journal of Applied Behavior Analysis, 20, 405–411.
Chapter 5
Cognitive Assessment in Behavioral Sport
Psychology
Bradley Donohue, Yani L. Dickens, and Philip D. Del Vecchio III
The contribution of thoughts, emotions, and images to athletic training and competi-
tion has long been acknowledged. As Yogi Berra once said about baseball, “90% of
this game is half mental” (Baseball Almanac, 2010). Less understood is the manner
by which these processes can be formally assessed to guide optimum implementa-
tion of evidence-based behavioral intervention. Indeed, as emphasized throughout
this book, evidence-based interventions are becoming increasingly utilized by sport
psychologists. However, there is often a poor fit between cognitive assessment
strategies and performance-enhancing behavioral interventions (Meyers, Whelan, &
Murphy, 1996). It is important that students and professionals practicing within the
field of sport psychology are familiar with the psychometric support and conceptual
basis underlying cognitive assessment methods. Indeed, one of the challenges for
professionals who work with athletes and performers is to develop an efficient and
evidence-supported method for assessing cognitive constructs that have been histor-
ically difficult to fully understand. This chapter, therefore, provides a practical and
evidence-based guide that may be used when conducting cognitive assessment in
sport psychology consultation. We begin by underscoring commonly used cognitive
strategies that have been identified to facilitate optimum sport performance, such as
self-talk, imagery, and arousal management. We then review environmental factors
that have been found to influence the attitudes and motivational sets of athletes.
Lastly, we review our evidence-based approach to cognitive assessment in athletes.
Common Factors Affecting Cognitive Assessment
Self-Talk
Almost three decades ago, Albert Ellis (1982) acknowledged the pervasiveness of
irrational thinking in sports. He reported that irrational negative thoughts lead to per-
formance difficulties and suggested that thoughts could be restructured to enhance
B. Donohue (B)
University of Nevada, Las Vegas, NV, USA
e-mail: bradley[email protected]
79
J.K. Luiselli, D.D. Reed (eds.), Behavioral Sport Psychology,
DOI 10.1007/978-1-4614-0070-7_5,
C
Springer Science+Business Media, LLC 2011
80 B. Donohue et al.
performance. About this time, positive self-talk in sports was thought to improve
physiological preparation ( Rushall, 1982, 1984), motivation (Kirschenbaum & Bale,
1980; Weinberg, Jackson, & Smith, 1984), and instructional support (Rushall, 1975)
in competitive situations. These processes were later demonstrated in outcome stud-
ies to enhance performance (e.g., Bunker, Williams, & Zinsser, 1993; Theodorakis,
Weinberg, Natsis, Douma, & Kazakas, 2000; Zinnser, Bunker, & Williams, 1998).
When used within the context of sport psychology, self-talk usually refers to
thoughts, beliefs, and attributions that are internally reviewed by athletes dur-
ing practice and competition. Interestingly, however, self-talk contributes to sport
performance outside athletic activities, as well. For instance, athletes may talk them-
selves into attending a late-night party the night before a championship game, or
may perform poorly in a game due to self-deprecating thoughts that are influenced
by a depressive disorder. Therefore, it is important to assess nonverbal speech in
both athletic and non-athletic contexts.
Assessing the specific content of self-talk is important. Indeed, one word can
have dire implications to performance in sport exercises (Van Raalte et al., 1995).
Along these lines, instructions and ongoing self-assessments are best stated neu-
trally and without bias (e.g., “I’m running with my hands relaxed and open,” “My
split times are 2 s slower than usual”), whereas motivationally based thoughts should
be positive, uplifting, and self-serving (e.g., “I feel like I can run all day today”).
Attributional statements usually occur after sport-related behaviors are performed
to assist athletes in understanding performance. We emphasize that the literature
on attribution-related thinking patterns is complicated and must be interpreted with
caution. As Hardy and colleagues (1996) point out, “precisely what constitutes self-
talk” (p. 37) is problematic because “past researchers have been too ‘loose’ in their
operationalization of self-talk” (pp. 37, 38). For example, it may be appropriate
for novice athletes with poor self-efficacy to initially attribute poor performance to
external factors. Such attributions are likely to protect against problems associated
with embarrassment, shame, or threats to self-esteem. However, accurate perfor-
mance attributions facilitate improvement in sports, particularly as athletes become
more experienced (Gordon, 2008).
There is some evidence to suggest athletes should avoid self-statements that indi-
cate they “need” or “should” do certain behaviors, as these thought patterns set up
comparisons with others rather than facilitate accurate s elf-evaluation. Comparisons
with others, whether downward (comparison with athletes who demonstrate lower
skill) or upward (comparison with athletes who demonstrate higher skill), restrict
opportunities to accurately become aware of one’s true ability or skill level (Hewitt,
2009). As an alternative, athletes are more likely to do well when they get excited
about the competition, preparing themselves with thoughts about opportunities
they are looking forward to taking, and to focus on their own motivational and
instructional sets (Donohue et al., 2006).
The timing of self-statements is critical for enhancing sport performance. For
example, Donohue, Barnhart, Covassin, Carpin, and Corb (2001) found cross-
country runners often voiced derogatory and self-defeating statements immediately
prior to their competitions (e.g., “what if I fall in the loose sand”), but not during
5 Cognitive Assessment in Behavioral Sport Psychology 81
practice or hours before their competitions. When these athletes were provided task-
relevant instructions immediately prior to running, however, their performance was
enhanced relative to control conditions and equally effective as being told motiva-
tional statements. We have also found derogatory self-talk in athletes can manifest
itself differently in training and competitive situations (Donohue, Covassin, et al.,
2004). Therefore, sport psychologists need to assess self-talk within both of these
contexts.
Imagery
Imagery is another cognitive skill set that is often overlooked in the assessment
of athletes. Specifically, imagery involves the ability of athletes and performers to
imagine themselves in performance-related tasks. Athletes often utilize imagery
prior to performance as a preparatory skill. It is common for athletes to watch
themselves in a sport activity much like a movie (external imagery), or view them-
selves in a participatory manner as if they are performing the athletic endeavor in
real time (internal imagery; Orlick & Partington, 1988; Williams & Krane, 2006).
The literature indicates it is probably better to utilize participatory imagery, as
the image can be experienced as if the event is happening with full utilization of
all senses and emotions, including kinesthetic movement (Callow & Hardy, 1997;
Louis, Guillot, Maton, Doyon, & Collet, 2008). The latter imagery strategy permits
memory of important associations that are likely to occur during actual sport-related
tasks. Although all senses may be imagined, it is appropriate for some senses (i.e.,
vision, touch) to be imagined more so than others (Gregg, Hall, & Nederhof, 2005).
Moreover, images should focus on positive experiences that approximate optimum
performance (Anderson, 2000). Positive images are also incompatible with anxiety
and are thus likely to assist in arousal management during participation in ath-
letic activities (Maddux, 2009). Other important facilitating factors associated with
imagery include making the task simple, optimizing the timing of events, and being
intellectually capable of vividly imagining the events with appropriate perspective
(see Wright & Smith, 2009).
Arousal Management
A thorough understanding of arousal management is essential in cognitive assess-
ment, as this skill set is s trongly associated with optimum sport performance
(Meyers et al., 1996; Parfitt & Hardy, 1994; Patrick & Hrycaiko, 1998; Robazza,
Bortoli & Nougier, 1998). As first hypothesized by Yerkes and Dodson (see
Taylor & Wilson, 2002; Weinberg, 1990), task performance is typically poor when
physiological arousal is low and improves as arousal increases to some relative
extent. However, performance gets worse when arousal is relatively high. Of course,
this relationship is grossly influenced by various factors, including the type of task,
task difficulty, task familiarity, past experience, confidence with the task, and so on
82 B. Donohue et al.
(see Gould & Udry, 1994). Optimum arousal is often associated with “flow state,”
which is a psycho-emotional state of mind in which athletes are completely engaged
and intrinsically motivated, and often reference a distorted sense of time during peak
performance (Nakamura & Csikszentmihalyi, 2009). During the flow experience,
performers often report their perceived s kill sets are capable of meeting task-relevant
challenges that are perceived to be important (Abuhamdeh & Csikszentmihalyi,
2009).
Several studies have demonstrated relationships between anxiety, arousal, and
self-confidence (Hardy, 1996; Hardy, Woodman, & Carrington, 2004). Bois,
Sarrazin, Southon, and Boiché (2009) compared golfers who made a qualifying
cut to those who did not. They found the golfers who made the cut demonstrated
more cognitive and somatic anxiety, but also utilized relatively more relaxation
strategies and emotional control. This supports Meyer’s notion t hat how athletes
respond to anxiety is probably more important than whether or not they experi-
ence anxiety. Interventions have been empirically developed to help athletes manage
anxiety and arousal (Chapter 7, this volume; Donohue et al., 2006; Miller &
Donohue, 2003). These interventions are generally aimed at modifying irrational
and negative thoughts to be more neutral with perhaps a positive bent (Gould &
Udry, 1994). Therefore, researchers appear to recognize the importance of cognitive
assessment in arousal management and emphasize that employment of validated
measures of anxiety are a necessary first step of intervention planning (Raglin &
Hanin, 2000).
Cognitions and Relationships
A primary focus of cognitive assessment is understanding how people influence
athletes to think in sport-related activities. This can become quite complicated
because these factors are dynamic and highly interactive in determining sport per-
formance (see Zourbanos, Theodorakis, & Hatzigeorgiadis, 2006). There does,
however, appear to be substantial evidence that frequent rewarding, social sup-
port, and democratic style in decision making lead to greater satisfaction in athletes
(Weiss & Friedrichs, 1986) and professional performers (Quested & Duda, 2010).
Team cohesion and sport performance appear to be interwoven (Carron, Colman,
Wheeler, & Stevens, 2002). Of course, these findings suggest professionals who
practice sport psychology need to assess the extent to which athletes perceive they
are being supported by both their coaches and teammates. However, they also need
to assess athletes’ perceptions of familial relationships. Stress appears to exacer-
bate interactions between athletes and their parents (Brustad & Partridge, 2002;
Weiss & Fretwell, 2005) and may limit support and encouragement from family
(Brustad, 1993; Hellstedt, 1988; Martin & Dodder, 1991; Woolger & Power, 1993).
Along these lines, athletes have reported that their family members contribute most
to their sport performance, as compared with their coaches, teammates, and friends
(Donohue, Miller, Crammer, Cross, & Covassin, 2007).
5 Cognitive Assessment in Behavioral Sport Psychology 83
Attitudes Toward Sport Psychology
Even when athletes experience cognitive, emotional, and environmental problems
that interfere with sport performance, they may be unmotivated to pursue assistance
from sport psychology consultants due to negative stigma associated with sport psy-
chology (Donohue, Dickens, et al., 2004; Martin, 1998; Martin, Wrisberg, Beitel, &
Lounsbury, 1997). Upon visiting sport psychologists, these beliefs may continue to
exist, leading to skepticism about perceived effectiveness of consultation (Martin
et al., 1997). Thus, in addition to assessment of cognitive factors that may inter-
fere with sport performance, attitudes about help-seeking and help follow-through
should be assessed prior to intervention planning and implementation.
Description of an Empirically Guided Method
in Cognitive Assessment
In the following sections, we highlight our method of understanding cognitive func-
tioning in athletes and performers. We begin by describing the basic structure and
format of our cognitive assessment sessions and progress to “hands-on” methods
involved in this evidence-supported process, such as behavioral interviewing, uti-
lization of psychometrically validated self-report measures, behavioral observation,
and self-monitoring (see Gardner & Moore, 2006).
Setting. We typically conduct our initial assessment in the office with the identi-
fied client and subsequently examine our functional hypotheses in field settings. The
office facilitates a quiet environment and assures privacy. This setting also permits
us to quickly locate cognitive assessment measures that are often spontaneously
administered during the interview process. Case information gathered during the
initial meeting is utilized to determine when it is optimal to conduct on-site cogni-
tive assessments during practice and competition, which offers distinct advantages,
such as timely queries about cognitions that may have occurred when behaviors
were observed during key activities. Indeed, we sometimes conduct our preliminary
meetings in places where clients practice or compete, but do so only during off-hours
when others are likely to be absent. In vivo cognitive assessment may be particu-
larly useful in reducing stigma associated with the referral and often makes it easier
for clients to be comfortable and better remember cognitions that may be triggered
in specific locations where performance is expected to occur (i.e., state-dependent
memory; Anderson, 2000). In vivo assessment also permits ongoing examination of
cognitive restructuring exercises that are often assigned during intervention phases.
When in vivo cognitive assessment is not possible, we encourage clients to bring
videos of their performance to the office and subsequently instruct them to report
cognitions they remember having during key events and activities. Note that it is
important to review videos soon after they are recorded to minimize distortions in
memory that occur with the passage of time.
Collateral informants. Clients are the chief source in assessing their own cogni-
tions. However, collateral sources of information can be helpful in substantiating
84 B. Donohue et al.
self-report data and offering unique insights relevant to the presenting problem.
Informants often include coaches, teammates, trainers, team doctors, and family
members. When the identified client is a minor, we always include legal guardians
in the initial process of assessment, whereas adult clients are encouraged to invite
one or two persons to the initial assessment meetings who they feel could shed light
on the presenting problem. Later, others are involved in the assessment process as
appropriate.
Psychometrically validated scales to guide interviewing. Our initial meeting
begins with a general discussion of the presenting problem, including the reasons
that led to the referral and perspectives of the presenting problem. We use a semi-
structured behavioral interview format (Grills-Taquechel & Ollendick, 2008). The
general areas of focus in the interview adjust based on our initial discussion of the
presenting problem and the athlete’s responses to standardized measures that are
administered immediately prior to the behavioral interview. In general, however, we
emphasize cognitive domains that have been empirically identified to influence per-
formance in athletic activities (see review by Donohue, Silver, Dickens, Covassin, &
Lancer, 2007). Potential cognitive domains include the following: (1) motivation to
obtain professional assistance, (2) motivation to pursue the respective sport activi-
ties, (3) injury management, (4) relationships, (5) academic and professional issues,
(6) dysfunctional thoughts, (7) stress, and (8) attitude (i.e., confidence, support from
others).
To assist in determining which domains to emphasize in our cognitive assess-
ment, we always first administer the Sport Interference Checklist (SIC; Donohue,
Silver, et al., 2007) and the Student–Athlete Relationship Instrument (SARI;
Donohue et al., 2007) at the start of our first meeting. The SIC was developed
to assess a wide range of problems that have been identified to interfere with
sport performance, including cognitive, emotional, motivational, and environmen-
tally based problems. Developed with 141 NCAA and high school athletes, the SIC
is unique to other measures in its ability to assess cognitive and behavioral prob-
lems that have been identified to commonly occur during both training (Problems
in Sport Training Scale, PSTS) and competition (Problems in Sport Competition
Scale, PSCS). In completing these scales, athletes are asked to indicate how often
each of the 26 problem cognitions and behaviors interferes with their training, and
separately competition, utilizing a seven-point frequency scale (see Table 5.1). For
each of the cognitive or behavioral stems, athletes are asked to indicate whether
they would go to a sport psychologist if possible (Desire for Sport Psychology
Scale, DSPS). Factor analysis of PSCS items indicate six factors (Dysfunctional
Thoughts and Stress, Academic and Adjustment Problems, Injury Concerns, Lack
of Motivation, Overly Confident/Critical, and Pain Intolerance), whereas PSTS and
DSPS items evidenced four factors ( Dysfunctional Thoughts and Stress, Academic
Problems, Injury Concerns, and Poor Team Relationships). The psychometric prop-
erties of this instrument, including its face validity, internal consistency, convergent
and discriminative validity, are excellent (see Donohue, Silver, et al., 2007).
We prefer the SIC for use in cognitive assessment because many of its scales are
specific to a wide r ange of commonly identified thought processes that have been
5 Cognitive Assessment in Behavioral Sport Psychology 85
Table 5.1 Sport Interference Checklist (SIC) Sample Items. Sample directions: “Below is a list
of things that sometimes occur with athletes during their training or during their competition.
Please circle the number that represents how often each of these things interfere with your perfor-
mance during training, and separately, your performance during competition (1 = Never, 2 = Ve ry
Seldom, 3 = Seldom, 4 = Sometimes, 5 = Often, 6 = Ver y O f t e n , 7 = Always). Then circle either
“yes” or “no” to indicate if you would see a sport psychologist if this happened to you, and if a
good sport psychologist were available to you.”
How often does this
interfere with your
performance during
training?
How often does this
interfere with your
performance during
competition?
Would you go to a
sport psychologist
for this, if possible?
1 Negative thoughts
about personal
performance
1234567 1234567 YesNo
2 Being too critical of
myself
1234567 1234567 YesNo
3 Being too critical of
teammates
1234567 1234567 YesNo
4 Distracted (or upset)
by people who
observe me
1234567 1234567 YesNo
Note. The Sport Interference Checklist is freely available in Donohue, Silver, et al. (2007).
indicated to contribute to poor performance in both athletic competition and train-
ing. Moreover, its format permits rapid assessment of cognitive interpretations for
problem circumstances that are environmentally determined (e.g., Injury Concerns).
That is, we query how each of the items that were endorsed as having occurred
at a frequency of “4” (sometimes) or higher within the elevated scales interferes
with training and/or competition, as appropriate. For instance, if the Dysfunctional
Thoughts and Stress scale was elevated, and an athlete indicated that she often was
too critical of herself during competition, she would subsequently be asked how
being too critical of herself interferes with her performance in competition. The SIC
is also unique in that athletes can be asked to explain how the material reflected in
the item stems is problematic for competition but not training, and vice versa. For
instance, it may be that an athlete reports that she is critical of teammates during
training, but not during competition, because during competition she is “focused on
the task at hand,” whereas training permits her greater “dead time” in which she
has time to notice flaws in others. Of course, such queries assist in gathering valu-
able information that is directly relevant to intervention planning. Moreover, a quick
glance at the corresponding DSPS item provides an indicator of the informant’s will-
ingness to obtain professional assistance in the respective problem area. Naturally,
we praise informants when they indicate they are interested in receiving profes-
sional assistance and query what obstacles make assistance difficult to accomplish
so solutions can be generated.
After the SIC interview, we administer the SARI to better understand the
extent to which relationships may influence sport performance. This instrument
86 B. Donohue et al.
was developed with 198 high school and collegiate athletes, and its psychometric
support is very good (Donohue, Miller, et al., 2007). The SARI includes four inven-
tories, each assessing a relationship constellation (i.e., Family, 23 items; Coaches,
25 items; Teammates, 23 items; Peers, 10 items). For each item, a prompt is read
(i.e., “It’s a problem for me in my sport that ...”) prior to a problem stem (e.g.,
“I feel isolated from at least one of my coaches.”). Participants respond accord-
ing to a 7-point Likert-scale measuring the extent to which the respondent agrees
with the problem statement. Subscales across the four inventories include pressure
to perform, lack of support, pressure to use illicit substances, pressure to quit or
continue unsafely, experiencing embarrassing comments and negative attitude, lack
of concern for teamwork and safety, lack of involvement and high expectations, too
demanding, not a team player, and too non-competitive (Table 5.2).
For each subscale that is elevated, we query how items endorsed “five” (some-
what agree) or higher are problematic (e.g., “How’s being isolated from your coach
a problem for you?”). These queries often lead to rich discussion about the under-
lying, and potentially erroneous, belief systems that athletes may adopt to interpret
the qualitative aspects of their relationships. For instance, if a coach was reported
to “expect too much,” the athlete could be queried to report how this conclusion
was determined, and how it could be resolved. Such questions are directly relevant
to intervention planning. The SARI format also permits the assessor to compare
cognitive problem sets across relationships to some extent. To illustrate, it may be
that the Family Inventory indicates a parent is putting pressure on her daughter to
participate in a sport, whereas coaches, peers, and teammates are not. In t he latter
example, the athlete could be queried to explain how the parents were contributing
pressure, whereas the others were not. Lastly, the SARI may assist in determining
intervention outcomes across time, particularly when interventions are designed to
enhance relationships in team sports where negative and erroneous thinking patterns
sometimes occur (e.g., feelings of isolation, jealousy).
To assist in the economy of our cognitive assessment, the remaining cognitive
measures in our battery are selected based on our initial interviewing. We believe the
Athletic Achievement Motivation Scale (Elbe, Wenhold, & Muller, 2005) is a great
tool to employ when it is important to understand the different aspects of motivation.
Table 5.2 Student–Athlete Relationship Instrument (SARI) Sample Items. Sample directions:
“Please indicate the extent to which you agree or disagree with the following statements
(1 = “extremely disagree,” 7 = “extremely agree”).”
It’s a problem for me in my sport that ...
At least one of my family members needs to praise me more often. 1 2 3 4 5 6 7
At least one of my family members pressures me to participate in a sport when I don’t want to
participate.1234567
At least one of my family members has me do things that could result in me being injured or
worsen existing injuries. 1 2 3 4 5 6 7
At least one of my family members makes rude or embarrassing comments about me. 1234567
Note. The Student–Athlete Relationship Instrument is freely available in Donohue, Miller, et al.
(2007).
5 Cognitive Assessment in Behavioral Sport Psychology 87
For instance, this scale provides an assessment of situationally based intrinsic and
extrinsic motivation, as well as poor motivation in athletes. Certainly, understanding
motives is important to treatment planning, and the results of this scale complement
information gained from the SIC in regard to the interest of athletes in pursuing
sport psychology consultation for specific problem areas.
When “mental toughness” appears to be relevant to the presenting problem, the
Sports Mental Toughness Questionnaire (SMTQ; Sheard, Golby, & van Wersch,
2009) is highly recommended for administration. The SMTQ is relatively brief
(i.e., 14 items) and has strong psychometric support. Respondents are queried
about the extent to which problems occur in sport performance-related domains
(4-point Likert scale; not at all true, very true) that are specific to Constancy (e.g.,
“I get distracted easily and lose my concentration,” “I give up on difficult situa-
tions”), Control (e.g., “I am overcome with self-doubt,” “I worry about performing
poorly”), and Confidence (e.g., “I have what it takes to perform well while I’m
under pressure,” “I interpret positive threats as positive opportunities”). The f ormat
for the SMTQ is similar to that for the SIC and SARI; thus we query respondents
to report how endorsed items in elevated scales affect their sport performance as
previously described. Other potentially useful psychometrically validated cognitive
measures include (1) the short form of the Competitive State Anxiety Inventory-2
(Cox, Russell, & Robb, 1998), which measures cognitive and somatic anxiety, and
state confidence with sport performance; (2) the Symptoms Check-List-90-Revised
(Derogatis, 1994; Vallejo, Jordán, Díaz, Comeche, & Ortega, 2007), which has been
used to identify psychiatric symptoms, including psychological distress, in athletes
(Donohue, Covassin, et al., 2004); (3) the Flow State Scale (FSS; see Jackson &
Eklund, 2002; Jackson & Marsh, 1996; Martin & Jackson, 2008), measuring nine
factors that are r elevant to perceptions of being fully engaged in their sport; (4) the
Dispositional Flow Scale (Jackson & Eklund, 2002) to assess concentration in
sport-relevant tasks; (5) the Self-Efficacy in Sport Scale (Feltz, Short, & Sullivan,
2008) to assess perceived ability to succeed based on personal feedback; (6) the
Winning Profile Athlete Inventory (PsyMetrics Inc, 2006) to assess competitiveness,
dependability, commitment, positive attitude, self-confidence, planning, aggressive-
ness, team orientation, willingness to sacrifice injury, and trust; and (7) the Coping
Inventory for Sport (Gaudreau & Blondin, 2002) to assess task-oriented coping,
distraction-oriented coping, and disengagement-oriented coping styles in athletes
and guide cognitive/behavioral intervention in teaching more adaptive and posi-
tive explanatory s tyles (Peterson & Steen, 2009). Finally, the Performance Failure
Appraisal Inventory (Elliot, Conroy, Barron, & Murayama, in press) is a multi-
dimensional measure of cognitive, motivational, and relational appraisals that are
linked with the sense of fear of failure (FF). This scale provides information that
is relevant to five aversive consequences in athletes (i.e., experiencing shame and
embarrassment, devaluing one’s self-estimate, having an uncertain future, important
others losing interest, upsetting important others).
Behavioral observation. Once the initial target cognitions are identified, we
utilize behavioral observation procedures to more specifically examine how
thoughts are related to actions in performance scenarios (Chapter 12, this volume;
88 B. Donohue et al.
Leffingwell, Durand-Bush, Wurzberger, & Cada, 2005). Observations should occur
in both competitive and practice settings. In each of these settings, we sched-
ule observations when target behaviors are most and least likely to occur. To
decrease the likelihood of response reactivity (an alteration in behavior due to being
observed), we initiate our observations in relatively inconspicuous circumstances
(e.g., sitting with others watching the event) and maintain neutral affect through-
out the process. Observational effects are also minimized by scheduling informal
observation prior to recording practices. Schedules of observation are varied to
accommodate diversity within settings and circumstances, thus enhancing external
validity. We make notes of environmental events that come before, during, and after
behaviors that are associated with both failure and success, and subsequently query
athletes to explain thoughts they may have experienced at key events or moments
during these situations. For instance, a cross-country runner may be observed to
run poorly when her competition looks physically intimidating, but may excel when
her competition is less daunting. If this finding is reliably observed, it becomes
clear that the athlete may experience debilitating cognitions about the competition
immediately prior to running, rather than functionally appropriate thoughts about
her personal preparation. The latter hypothesis would be substantiated if, upon being
queried to report her thoughts immediately before and after the race, it was discov-
ered that she made comments such as “She’s too strong for me” before the race and
reinforced these cognitive beliefs with confirmatory thoughts after the race (e.g., “I
knew she was too much for me”). Comparing cognitions at key behavioral set points
during sporting events assists in minimizing inaccuracies associated with self-report
methodologies (i.e., faulty memories, biased responses) because the observer is
present to substantiate the context in which self-reports are made. Congruence is
an excellent method of confirming the accuracy of self-reported information and
is demonstrated when self-reported cognitions are consistent with observed behav-
iors. For example, congruence would be demonstrated if an athlete reported that she
evidenced derogatory or self-defeating cognitions before, during, or after crying,
pacing, tensing muscles, and rapid breathing.
Interestingly, debilitating thoughts and behaviors are sometimes conditioned to
co-occur in ways that can be understood only after formal cognitive assessment. For
instance, one elite athlete we treated told jokes and laughed about the competition
during warm-ups. Her teammates reported that her comments distracted them from
focusing on what they needed to do prior to the race. When queried to report her
thoughts before an observed warm-up, it was revealed that she experienced wor-
risome thoughts about the upcoming race. Therefore, her jokes appeared to be a
nonfunctional method of distracting her from the worrisome thoughts. Indeed, the
jokes distracted both her teammates and her from thinking about task-important
instructions and positive self-statements that have resulted in optimum performance
in controlled trials (e.g., Donohue et al., 2001, 2006).
Self-monitoring. Encouraging journaling in an open format or use of structured
cognitive tracking assignments may provide fruitful information about cognitive
functioning. However, these methods are notoriously limited in not providing a rep-
resentative sample of cognitions, lack reliability and validity, and are dependent on
5 Cognitive Assessment in Behavioral Sport Psychology 89
the athletes’ self-awareness (see Hackfort & Schwenkmezger, 1989). As an alterna-
tive approach, structured self-monitoring exercises may assist in gaining an accurate
representation of problems interfering with performance (Gardner & Moore, 2006).
Athletes may be instructed to record the frequency of cognitions that occur within
a prescribed time frame (e.g., number of positive self-evaluation statements dur-
ing a 2-h block) and setting (e.g., practice, game, team lunch), or specific thoughts
and ratings of intensity can be recorded during critical points of performance. As
in behavioral observation, the antecedent stimuli (e.g., being criticized) and conse-
quences (e.g., threw ball away) of monitored thoughts should be recorded to assist
in understanding etiological factors maintaining the respective cognitions.
Functional assessment and analysis. Although functional analysis has histori-
cally been utilized to determine factors maintaining identified problem behaviors,
its method is also appropriate in understanding the function of thoughts. In doing so,
hypotheses are formulated about the function of operationally defined thoughts in
relation to environmental antecedents and consequences. We utilize the ABC model
as our method of organization, whereby the “A” stands for antecedents, the “B”
refers to beliefs or behaviors, and the “C” refers to consequences (Groden, 1989;
Iwata, 1994; Iwata, Kahng, Wallace, & Lindberg, 2000). Of course, in cognitive
assessment, the practitioner is primarily interested in gaining an understanding of
dysfunctional thoughts that are influenced by antecedents and consequences. We
relax the “B” ( beliefs or behaviors) part of this model to include dysfunctional
thoughts and images. In Table 5.3, we provide two ABC models: one focuses on
the “B” as a belief and the second focuses on the “B” as a behavior. To demon-
strate these models, in Table 5.3 we create three columns. In the first column, we
record stimuli that precede the respective belief or behavior. In the second column,
we record the respective belief or behavior, and in the third column, we record
consequences of the respective belief or behavior.
The first model at the top of Table 5.3 focuses on a middle-weight boxer’s
belief that an upcoming bout will result in defeat. This belief is likely influenced
Table 5.3 Examples of two ABC models for use in athletes
A (antecedents) B (belief) C (consequences)
Missing training days “I’m going to lose the
upcoming match”
Poor effort/motivation
Knowledge of competition Irritability
Lack of sleep Withdraw of anxiety
Told brother had better
attitude
Quick fuse
Poor response to feedback
A (antecedents) B (behavior) C (consequences)
“I’m going to lose the fight Poor effort during practice Poor timing in skill
“I’m not my brother” Irritability
“They only care if I win” Arguments with others
Told need to practice harder Quick fuse
90 B. Donohue et al.
by the environmental stimuli and cognitions that are listed in the left and right
margins. These stimuli were identified during a behavioral interview. Although other
antecedents and consequences influencing this belief may not have been identified,
if the assessment was sufficient, then etiological hypotheses for this belief may be
drawn. For instance, examining the antecedent factors, it appears missing training
days may have influenced low self-efficacy, particularly because the competition is
expected to be “tough.” Interestingly, lack of sleep is often associated with faulty
thinking patterns and irrationality (Durmer & Dinges, 2005), which may lead to poor
decision making. Lastly, being told by his father that his brother had a better atti-
tude may be detrimental. That is, this statement suggests he has a bad “attitude.”
Additionally, being compared with others in a negative light is ill-inspiring. On
the consequence side, self-defeating thoughts have been shown to decrease motiva-
tion to practice (Elliot & Harackiewicz, 1996) and increase irritability (Cacioppo &
Hawkley, 2009), both of which may have resulted in a pattern of negative inter-
actions with others. At first glance it may appear strange to see the boxer’s
self-defeating t houghts appear to be an influence in reducing his perceptions of anx-
iety. However, defeat is an easy outcome to predict, and anxiety is centered on the
unknown.
The second model in Table 5.3 exemplifies poor effort during practice as a tar-
get behavior for this athlete, and the self-defeating thought “I’m going to lose the
upcoming match” as an antecedent cognition contributing to his poor effort. Unlike
the previous model where the thought “I’m going to lose the upcoming match” was
isolated as the target belief, in this model the boxer’s thought is put into context
with other debilitating thoughts ( i.e., “I’m not my brother”). It becomes clear that
being told by the boxer’s father that the brother had a better attitude was probably
upsetting as the boxer spent time away from productive thoughts about practice to
instead focus on his brother. It can also be hypothesized that brotherly comparisons
contributed to irritability in this boxer. The second model also shows how thoughts
are rarely isolated and that being told he had to practice harder by his coach was
ineffectual or perhaps deleterious to his efforts. Poor effort appears to be reinforced
by poor performance, arguments with others, and potential resentment perhaps due
to the belief that others like him only when he wins. The latter hypothesis suggest
this boxer may be demonstrating a lack of effort to determine if others continue
to support him during defeat because as noted, poor effort is associated with poor
performance.
Testing functional hypotheses. The next step in functional assessment is to deter-
mine which environmental stimuli and thought patterns have greatest influence on
sport performance so competing behaviors and cognitions can be emphasized in
intervention. From the intervention provided in Table 5.3, it is difficult to determine
which factors contribute most to sport performance. However, it may be deduced
that self-defeating cognitive sets (e.g., lack of confidence, insecurities, focus on
issues relevant to resentment) interact with behavioral problems (e.g., lack of effort
during practice, arguments). Therefore, cognitive interventions might incorporate
competing positive self-statements, perspective taking skills training, and emphasize
the benefits of practice, whereas behavioral interventions might include exercises
5 Cognitive Assessment in Behavioral Sport Psychology 91
designed to enhance relationships. It would make sense that the physiological prob-
lems (i.e., lack of sleep, irritability, anxiety) would dissipate when the cognitive and
behavioral problems are effectively addressed.
Prior to testing these hypotheses, all data and hunches should be revealed to
the athlete/performer and relevant significant others to obtain their feedback. First,
participants should be queried to explain what they were hoping to gain from
the assessment process. Certainly, this permits an informed opportunity to address
misconceptions and determine what should be emphasized and avoided while pre-
senting i nformation. We review the implications of each cognitive assessment scale,
starting with the results of non-elevated factors and progressing to an interpretation
of elevated scales. We frequently ask if the information appears to be accurate, listen
to concerns, and make adjustments when discrepancies are reported.
We like to show our clients t he completed ABC analysis. We explain the con-
ceptual aspects of the model because understanding it reinforces the intervention
process. After the model is understood, competing behaviors and cognitions are gen-
erated from the participants. Of course, this helps participants become invested in
the intervention plan and often results in useful strategies to complement the previ-
ously generated cognitions and behaviors. Competing behaviors and cognitions are
prioritized according to the extent to which they are expected to lead to environmen-
tal reinforcement, are incompatible with debilitating thoughts and behaviors, can be
implemented quickly, are desired by the athlete, have occurred previously or may
be acquired without much effort, and have a strong likelihood of generalizability.
Finally, soliciting feedback from athletes and their significant others may be used
as a springboard for intervention. Indeed, asking athletes how they would like to
think during sport events, asking what they are hoping to get from interventions,
and establishing a collaborative approach to intervention are all associated with
consumer-driven evidence-based care. Moreover, we have our clients choose which
evidence-based cognitive interventions, such as the ones reviewed in this book, they
find most appealing and believe will produce the best outcomes.
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Part III
Performance Enhancement
Chapter 6
Goal Setting and Performance Feedback
Phillip Ward
Goal setting and performance feedback are two of the most used and most studied
performance-enhancing strategies in sport. Both strategies have roots outside of
sport with the seminal work for goal setting being conducted in organizational
management in work settings and for performance feedback in both organizational
management and education. Goal setting and performance feedback have been used
extensively in sports settings –long before researchers started attending to their
effects, and probably as long ago as individuals wanted to improve their perfor-
mance. Today goal setting and performance feedback are well known to coaches
and researchers alike as effective tools for performance improvement.
Goal setting and performance feedback commonly fall under the lay umbrella
term of motivation. Motivation is discussed both as a goal of coaches and as a prob-
lem with athletes. It is, however, an amorphous term with multiple meanings in both
the lay and empirical literature. Brent Rushall, arguably the most influential behav-
ioral sport psychologist in the past 50 years, suggests that athletes are considered
motivated if specific behaviors occur at consistently high rates, with seemingly few
rewards (Rushall, 1980). The rates of behavior observed are higher than what might
be considered “normal” in the setting, and the reinforcers for the behavior may not
be obvious to the observers. Sport-specific behaviors can include a variety of behav-
iors such as attending practices consistently, being punctual to practices and games,
completing training tasks and workloads successfully, providing encouragement to
peers, engaging in fair play, and organizing team activities. Goal setting and per-
formance feedback are two of the most effective behavioral interventions that can
produce the outcomes Rushall described as “motivation.”
Goal Setting
“A goal is a level of performance proficiency that we wish to attain, usually within
a specified time period” (Latham & Locke, 2006, p. 332). More specifically, a goal
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The Ohio State University, Columbus, OH, USA
e-mail: W[email protected]
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J.K. Luiselli, D.D. Reed (eds.), Behavioral Sport Psychology,
DOI 10.1007/978-1-4614-0070-7_6,
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Springer Science+Business Media, LLC 2011
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describes a specific behavior such as the jump s hot in basketball or an overhead clear
shot in badminton. Goals are typically presented in statements such as the following:
Soccer player: “I want to make 2 more shots on goal during this game than
I did yesterday.”
Badminton coach: “I want to see your wrist snap as you contact the shuttle in
these next five overhead clears.”
From a behavioral perspective, a goal is a rule (Martin, 1997). Rules represent
behavior that is initially controlled by its antecedents, but is then maintained by
its consequences as the person comes into contact with the consequences of the
behavior. Rules alter behavior because they describe the contingency that results
from following the rule. Some caveats are warranted here. First, the rule may not
describe a contingency (antecedents, the behavior, and its consequences) explicitly,
as in the case of a high school basketball coach saying to his team, “Let’s make
80% of our free throws tonight during the game.” In this example, the consequences
for not making the free throws are either implicit or nonexistent. Second, the con-
tingency described in the goal statement may not be the actual contingency that is
operating. For example, a coach may say to the team that she is going to recognize
players who are performing well using a wall chart to acknowledge their success,
but some players may act to avoid getting cut from the team for poor performance.
There is a substantial general sports psychology literature examining goal setting
(see Horn, 2009, for a review of this literature). This literature as shown that, in
general, goals have been found to be effective at improving performance outcomes
for athletes. However, there are studies that have reported equivocal findings (Locke,
1991; Weinberg, 1994). Locke (1991) argued that the equivocal findings have been
the result of methodological flaws, many of which concern the internal validity of
the studies (e.g., choosing goals that were not actually difficult or relevant). More
recently, Mellalieu, Hanton, and O’Brien (2006) were critical of goal-setting studies
that (a) too often used a limited number of observations of the dependent variables
leading to conclusions based on a non-representative sample of observations; (b)
combined skills rather than focusing on specific discrete skills, and thus increasing
the task complexity that has been shown to be a strong moderator of goal-setting
effects (Latham & Locke, 2006); and (c) too often used dependent variables that
were not important variables for athletes and as such there was little commitment
from t he athletes in achieving the goals.
Within the behavioral sports psychology literature, these issues have not been
prevalent because of the nature of single-subject designs that (a) allow participants
to serve as their own controls, (b) focus on changing specific behaviors one at a
time, and (c) select behaviors as goals that were socially significant for athletes in
the context of their sporting lives. There are, however, some limitations in the behav-
ioral literature and these include the following: (a) the packaging of goal-setting
interventions with other strategies (e.g., verbal and graphic feedback), making spe-
cific interpretations of the effects of goal setting unclear; ( b) limitations in the
number of studies conducted in different sports settings (college, high school, and
clubs represent the majority of research); ( c) lack of demonstrated maintenance and
6 Goal Setting and Performance Feedback 101
generalization effects in many studies; and (d) too few studies that have described
completely the behavioral contingencies in place.
Evidenced-Based Goal-Setting Principles
Three primary principles of goal setting can be derived from the behavioral liter-
ature. As described below, goals should be specific and difficult, goal statements
should define the consequences of meeting or not achieving the goal, and goal
setting is more effective when combined with performance feedback.
Goals should be specific and difficult. Descriptions of behaviors used to set
goals can be defined functionally or topographically (Cooper, Heron, & Heward,
2007). Function-based definitions are used when outcomes of the performance are
expected. For example, in a study of collegiate rugby players, Mellalieu et al. (2006)
used the following function-based behaviors: number of ball carries, number of tack-
les, and number of turnovers won. An advantage of function-based behaviors is that
they are easy to measure because of their discrete characteristics. To illustrate, a
tackle can be defined by its legal definition within the game and if it is effective in
stopping the forward motion of the ball carrier.
Topographical-based behaviors are often seen when referring to technique of
sports skills. For example, Ward and Carnes (2002) used a topographical description
of a performance tactic in collegiate football: “A correct read or drop occurred if the
linebacker moved to the correct zone relative to predefined pass coverage described
in the coaches’ play book” (p. 3). Topographical-based behaviors are more diffi-
cult to measure because they involve more complex definitions than function-based
behaviors.
Note, however, that there is more to a goal statement than merely describing
a specific behavior. The conditions under which a goal is to be performed are
either implicitly or explicitly stated. When a goal such as “Successfully increas-
ing the percentage of successful jump shots in basketball” is stated, there should be
a description of where the jump is to occur. Will it be measured in the context of
free practice, scrimmages, or games as well? Will it be counted from the three-point
line, or anywhere on the court? Similarly, performance-orientated goals function
best with a criterion tied to the performance, such as making 8 out of 10 jump shots
from the free throw line (Martin, 1997). In the vast majority of studies, the coach
has set the criterion performance in the goal. Brobst and Ward (2002) in their study
of female high school soccer players reported t hat the coach and the lead researcher
set a goal of 90% correct performance for three behaviors, which were movement
with the ball, movement during restarts, and movement after the player passed the
ball. The t hree behaviors were selected on the assumptions of the coaches that the
skills could be performed by these players at that level.
A typical goal-setting study in a work setting compares the goal-setting condi-
tion to a “do your best” encouragement condition. The results of more than a 1,000
studies in organizational management have shown that difficult,butpossible to per-
form goals produce the most effort and performance compared to being encouraged
102 P. Wa rd
to “do your best” (Latham & Locke, 2006). These findings have been replicated in
sport settings as well. For example, Boyce (1990) showed the effects of instructor-
set specific goals versus “do your best” encouragement in rifle shooting in a college
physical education class. She later replicated the study in a college tennis setting
comparing “do your best” encouragement with self-set goals and instructor-set goals
with the same results (Boyce, Wayda, Johnston, Bunker, & Eliot, 2001).
One problem with the recommendation of establishing difficult goals is defining
what is meant by the term difficult. To date, no studies have examined the func-
tional definition of “difficult” in sport settings. Latham and Locke (2007) and Locke
(1991) argued that findings from the organizational literature show that difficult
goals should be defined in terms of a criterion where no more than 10% of par-
ticipants could reach the criterion without the use of goal setting or some other
intervention. Accordingly, two caveats are important in establishing specific and
difficult goals that focus on improving performance. First, the behavior should be
in the current repertoire of the performer, or if it is a new skill, it must be achiev-
able. Asking for performance improvement of a sports skill when the person cannot
perform it is counterproductive and may lead to injury. Thus, determining whether
goal is difficult requires some knowledge of the ability of the performer and some
knowledge of the difficulty of the behavior to be performed. Second, the goal must
be realistically achievable within the time frame targeted for the goal to be achieved.
Creating unrealistic performance goals will result in athletes becoming unsuccessful
and frustrated.
In single-subject design studies in sports (see Chapter 4, this volume), the base-
line phase has typically represented the existing conditions present in the setting.
Such conditions might include group expectations, feedback from coaches, and ath-
letes’ knowledge of their performance successes. For example, Swain and Jones
(1995) examined the effects of goal setting on basketball skills of elite college play-
ers over the course of a season. In their study, the existing coaches’ practices were
treated as a constant and their goal-setting intervention was the primary manipula-
tion. There have been no studies to date that have controlled the baseline conditions
to the extent that there was no feedback or instructions from coaches. While this
might limit experimental control, it enhances the ecological validity of these stud-
ies because goal-setting intervention has been used in the actual context it has been
designed for.
Few studies have examined effects of goal setting in sports as a function of skill
level. A study by O’Brien, Mellalieu, and Hanton (2009) showed the differential
effects of goal setting on the performance of teenage elite and nonelite boxers. Their
findings show that elite boxers displayed consistent performance improvement as a
result of the goal-setting intervention and the nonelite boxes improved, but did so
with less consistency. This finding is important to examine more closely particularly
in the context of youth sport settings where athletes are often developing new skills
and refining them.
Goal statements should describe the consequences of meeting or not meeting the
goal. Behavior analysts are well versed in the importance of providing consequences
for behavior. It is commonplace that when goals are stated in educational and sports
6 Goal Setting and Performance Feedback 103
settings, the response consequences either are not stated or if they are stated, the
consequences are not applied. Interestingly, most goal-setting studies, behavioral
or otherwise, have not described the response consequences as a component of the
goal-setting intervention, thus making it difficult to assess the role of variables such
as praise, feedback, and the role of social reinforcement.
When studies have described the response consequences, the most common
strategy used has been praise and recognition of improved performances. Ward,
Smith, and Sharpe (1997) combined goal setting with public posting when assess-
ing the percentage of correct blocks and percentage of correct routes run by wide
receivers on a collegiate football team. During the intervention phase of the study,
the researchers met with the players following each practice session and congratu-
lated them if they met their goal. Contingent on goal achievement, their names were
added to a second poster board called the 90% club. Players who consistently made
the 90% club each week were recognized at the end of the season social event.
Goal setting i s more effective when combined with performance feedback.The
majority of studies have combined performance feedback with goal setting to pro-
duce significant positive effects in the behaviors of athletes. The performance
feedback can be either verbal descriptions or graphic displays (public posting
or private) or both. Other interventions have combined goal setting and perfor-
mance feedback with other components. For example, Wanlin, Hrycaiko, Martin,
and Mahon (1997) demonstrated the effects of a package intervention consisting
of mission development, athlete-set long- and short-term goals, self-talk, and goal
visualization on elite speed skaters practice workloads, off-task behavior, and race
times.
Few studies have directly assessed goal setting with and without other inter-
ventions. Smith and Ward (2006) used a multiple treatment design to assess the
differential and combined effects of goal setting and public posting on the perfor-
mance of wide receivers in college. While each condition was more effective than
baseline, the goal setting and public posting condition was consistently better than
public posting or goal setting alone.
Goal-Setting Principles Included in Interventions
but Not Directly Assessed
Three additional principles are typically associated with goal setting in sport
(Martin, 1997; Rushall, 1980). Although there are no studies in the behavioral sport
psychology literature that have directly evaluated these principles, they are often
included in sport and in organizational management studies of work settings.
Public goals may be more effective than private goals. Despite the fact that when
most individuals set goals, they do so privately, there is little research on this topic.
In goal-setting studies in sports, someone other than the athlete (i.e., coaches, peer,
researchers) has been aware of the goals and the performance objectives, typically
on a practice-to-practice and game-to-game schedule. One of the few studies to use
private goal setting in sport was conducted by Campbell and Martin (1987), who
104 P. Wa rd
used private goal setting and private self-monitoring compared to standard coaching
conditions in youth tennis players. Campbell and Martin found that private goal set-
ting and private self-monitoring did not improve performance beyond that obtained
in the standard coaching conditions. To date, there are no studies that have directly
assessed the effects of private versus public goal setting. There are, however, studies
that have kept the identities of the athletes and their goals from their coaching staff
and other players during the study (e.g., Mellalieu et al., 2006).
There is some evidence that public goals may be better than private goals. Lyman
(1984) reported that public goal setting was significantly more effective than pri-
vate goal setting on the classroom on-task behavior of children with emotional and
behavior disorders. Hayes et al. (1985) conducted two experiments examining pub-
lic versus private goal setting and self-reinforcement of individuals studying for
the graduate record examination. In both studies, the public conditions were more
effective than the private ones. Hayes et al. (1985, p. 201) concluded, “The two
experiments make more plausible the view that self-reinforcement procedures work
by setting a socially available standard against which performance can be evaluated.
The procedure itself functions as a discriminative stimulus for stringent or lenient
social contingencies.”
Implicit in the statement that goals should be public is the recommendation that
there should be a system for monitoring progress toward goals. This system might
include recording of performance by athletes using training logs or by coaching
staff. Public posting of daily performance goals also could be included. Ward and
Carnes (2002) investigated the effects of publicly posting “Yes” or “No” on a chart
to indicate whether collegiate football players had met their daily self-set goals.
The actual goals were never made public to the coaching staff. In addition to the
intervention being effective, the effects were similar to other studies in which the
goals were publicly stated (e.g., Smith & Ward, 2006; Ward et al., 1997).
Break complex or longer-term or larger goals into smaller subgoals. To date,
there are no studies that have evaluated reducing large goals into smaller sub-goals,
although Martin (1997) recommends this approach. However, there is good evi-
dence from the task analysis literature showing that breaking tasks into smaller
steps or stages is effective (Cooper et al., 2007). For example, creating smaller s teps
makes it easier to pinpoint where errors occur and when to provide feedback and
consequences (e.g., reinforcement or correction).
Gain Commitment from Athletes for the Goals. A recurring finding goal-setting
research has been the extent to which the participant is committed to the task. Martin
(1997, p. 45) explains:
Goals a re likely to be effective only if there is continuing commitment to them by the
individuals involved. From a behavioral perspective, commitment refers to statements or
actions by a person setting a goal that imply that the goal is important, that he or she will
work towards it, and that he or she recognizes the benefits of doing so.
An efficient approach to commitment is to involve the athlete in the process of set-
ting goals. While there are some studies demonstrating that self-set goals may not
work as well as instructor-set goals (Boyce et al., 2001), the difference between
these two strategies is not substantial. Yet the majority of studies have shown that
6 Goal Setting and Performance Feedback 105
self-set goal is an effective goal-setting strategy ( e.g., Mellalieu et al., 2006; O’Brien
et al., 2009; Ward & Carnes, 2002).
Performance Feedback
The purpose of performance feedback is to provide information to athletes that
allows them to correct or maintain their performance. Similar to the goal-setting
literature, the findings for feedback are i nconsistent and do not support often-cited
assumptions that (a) more feedback is better (Lee, Keh, & Magill, 1993; Magill,
1994), (b) some feedback is better than no feedback (Lee et al., 1993), or (c) positive
feedback is better than corrective feedback (Brophy, 1981; Brophy & Good, 1986;
Lee et al., 1993; Magill, 1994). Among the reasons for these inconsistent results are
that many feedback studies were conducted under laboratory conditions with little
ecological validity. Also, feedback studies too often have focused on the topography
of the feedback (e.g., positive, corrective, negative) or who provided it (e.g., instruc-
tor or peer) without assessing the effects of the feedback. Other limitations are that
feedback studies have used pre-post measures of student and athlete learning, have
not reported effects of the feedback relative to specific trials, and confused feed-
back with rienforcement (Ward, 1995). Behavioral studies of performance feedback
avoid many of the above limitations.
Note that performance feedback can be provided by coaching staff, peers, self,
and even technology. For example,
Basketball coach: “I saw that your elbow is moving the side when you make
your set shot, this next shot I want you to keep your forearm
vertical as you make your shot.”
Volleyball teammate: “You are standing too high when you bump, bend your
knees more!”
Soccer player: “I can’t believe that I contacted the ball that high, I’ve got
to kick under the ball more.”
Technology: Heart rate monitors that sound when you are above or below
your target heart rate while you are running or electronic
beams that make a sound when a body moves between the
beams indicating that you had too low a trajectory off the
vaulting horse in gymnastics.
In addition, performance feedback can be presented in graphs or charts that are
recorded by coaching staff, peers, self, or researchers. In this overview of perfor-
mance feedback, I include four strategies that have been studied in the behavioral
literature and represent evidence-based methods.
Behavioral Coaching
Studies focusing on behavioral coaching use verbal feedback and instructions as
independent variables and compare the package to the baseline or standard coaching
106 P. Wa rd
occurring in the setting. What distinguishes behavioral coaching using performance
feedback from nonbehavioral studies is that the principles of applied behavior anal-
ysis are used in the design and application of the intervention (e.g., contingent
reinforcement, clearly specified behaviors and contingencies). Behavioral coach-
ing using verbal feedback, instructions, and reinforcement has been effectively
demonstrated in many sports settings, improving (a) play execution by youth and
high school football athletes (Komaki & Barnett, 1977; Stokes, Luiselli, & Reed,
2010; Stokes, Luiselli, Reed, & Fleming, 2010), (b) stroke performance by youth
swimmers (Fitterling & Ayllon, 1983; Koop & Martin, 1983), (c) correct relay tag
performance by competitive inline-roller speed skaters (Anderson & Kirkpatrick,
2002), (d) foul shooting performance of a women’s collegiate basketball team
(Kladopoulos & McComas, 2001), (e) practice and social behaviors by members
of a youth swim team (Vogler & Mood, 1986), and (f) the technique of youth tennis
players (Buzas & Allyon, 1981).
One very promising strategy, long used by coaches, is the “freeze strategy.” In
this feedback technique, the coach calls a freeze to the play in a scrimmage or drill
and players are questioned about their current physical placements relative to the
play. This is followed by modeling and then a replay of the events. The freeze tech-
nique has been used successfully to improve performance of technical skills and
tactics in gymnastics, tennis, and football (Allison & Ayllon, 1980), youth soccer
(Rush & Allyon, 1984), and youth track (Shapiro & Shapiro, 1985).
Public Posting of Performance
Pubic posting is an effective behavioral feedback strategy that has demonstrated
utility in a variety of settings (see Brobst & Ward, 2002, for a summary). Van
Houten (1980) provided two explanations for the effectiveness of public posting.
First, feedback serves to prompt and reinforce athlete performance. Second, public
posting provides specific public expectations that become norms for conduct in an
instructional environment.
Most public posting studies use goal setting and verbal feedback as components
of the intervention because there are advantages to combining them. Goal setting
provides an explicit criterion, as opposed to “do your best,” while public posting is
a technology that makes the performances public and also serves to provide feed-
back to performers. Verbal feedback is implemented frequently with public posting
interventions, though it is often not explicitly described as a component of such
interventions. When verbal feedback is used in conjunction with public posting and
goal setting, it is typically limited to restating what has been publicly posted (e.g.,
Swain & Jones, 1995). However, the act of meeting and providing verbal feedback
may also function as social reinforcement.
Galvan and Ward (1998) used public posting to effectively reduce
unsportsmanlike behavior during t ennis matches by male and female collegiate
6 Goal Setting and Performance Feedback 107
tennis players. In their study they reported that coaches were concerned with inap-
propriate behaviors such as disrespectful physical gestures, swearing publicly, and
throwing and striking objects during tennis matches (e.g., tennis balls and racquets).
The intervention consisted of presenting the data to the tennis players individually
on the frequency of inappropriate behaviors collected during baseline and establish-
ing an expectation in the form of a goal that these behaviors would be reduced from
game to game. The data from games were publically posted in training sessions for
all players to see. While the behaviors were not eliminated for any of the players, the
overall reductions were from means of 14 per game in baseline to 2–4 occurrences
per game during the intervention.
The effects of public posting have also been effectively demonstrated in increas-
ing the success of set plays in scrimmages with measures of generalization to game
performances in collegiate football (Smith & Ward, 2006; Ward & Carnes, 2002;
Ward et al., 1997) and increasing body checks during collegiate hockey games
(Anderson, Crowell, Doman, & Howard, 1988).
Self-Monitoring
Self-monitoring occurs when individuals notice and record specific behaviors
of interest. Several studies have successfully demonstrated the effects of self-
monitoring in improving behaviors of athletes. Hume, Martin, Gonzalez, Cracken,
and Genthon (1985) used a self-monitoring feedback package to improve the per-
formance of youth figure skaters. Similarly, Wolko, Hrycaiko, and Martin (1993)
demonstrated the effectiveness of self-monitoring compared to standard coach-
ing of female youth gymnasts. More recently, Polaha, Allen, and Studley (2004)
used a self-monitoring feedback package to decrease stroke counts in college-aged
swimmers.
McKenzie and Rushall (1974) conducted one of the earliest studies of self-
monitoring in sports. The study took place at a youth swim club where attendance
and work rates (i.e., laps swum) were low. The research evaluated a public posting
of performance intervention, described as follows:
A large waterproof display board was constructed, on which each swimmer could indicate
his/her cumulative attendance at practice. Spaces were also provided for the recording of
each swimmer’s present and best attendance records. Prominent spaces were reserved for
the posting of the names of those who had the best records. (McKenzie & Rushall, 1974,
p. 200)
One of the important characteristics of self-monitoring is the effect on a partic-
ipant’s behavior caused by the act of self-monitoring. This reactivity typically
operates in favor of the intervention, but not always. In their study, McKenzie and
Rushall (1974) concluded that in addition to the self-monitoring, a part of the effects
of their intervention might have been attributable to the coach’s verbal feedback.
Critchfield and Vargas (1991) replicated the McKenzie and Rushall (1974) study
108 P. Wa rd
controlling for coach prompting by scripting t he amount of verbal instructions pro-
vided by the coach. Critchfield and Vargas (1991) reported modest and temporary
increases in behavior under their conditions, raising the question about the role of
reactivity effects in the self-recording performance. In a follow-up study examin-
ing t he frequency of self-recording on laps swum (i.e., after 2 laps, after 4 laps,
or following completion of all the required laps), Critchfield (1999) showed fre-
quent self-recording was less effective for measuring laps swum than infrequent
recording. Collectively, the studies by Critchfield and Vargas (1991) and Critchfield
(1999) show that the mechanisms of self-monitoring are unclear and that there are
contextual nuances that require elucidation in future studies.
Technology
Technology has become increasingly useful in providing feedback in sporting set-
tings and in fitness and health. Consider a person visiting a fitness center and using
exercise cycles, treadmills, and step machines. Once she/he provides information
such as body weight, age, and duration of workout, t he machines follow a set pro-
gram regardless of the effect that the activity has on her/his heart rate response to
exercise. For some people, their heart r ate may be 190 beats per minute (bpm), while
for others it may be 114 bpm. If t he person was wearing a heart rate monitor or if
the machine measured the heart rate using hand sensors, her/his heart rate would be
transmitted to a receiver in the machine. For maximum health benefits, exercisers
should train within their target heart rate zone (i.e., 60–85% of maximum heart rate).
If the machine is tracking the person’s heart rate, it will adjust the load it places on
the body to maintain a personal target zone. The accuracy and immediacy of tech-
nology feedback makes it a useful tool in research and an excellent resource for
coaches and athletes.
Several studies have been conducted using technology as a feedback medium.
Recently Boyer, Miltenberger, Batche, and Fogel (2009) examined the effects of
combining video modeling by experts with video feedback in the performance of
gymnastic skills by female youth gymnasts. Following skill performance, the gym-
nast viewed a video segment showing an expert gymnast performing the same
skill and then viewed a video replay of her own performance of the skill. Each
gymnast was told to try to match her performance to the expert performance. The
gymnast then returned to practice. The intervention was successful for all four gym-
nasts in the study. A similar study was conducted using youth swimmers by Hazen,
Johnston, Martin, and Srikameswaran (1990).
Scott, Scott, and Goldwater (1997) used a shaping procedure with a photoelectric
beam to improve the technical skill and performance of a pole vaulter. The task was
to help the vaulter extend his arms completely prior to take-off. The intervention
involved a verbal prompt of “reach,” which was shouted to the vaulter as he ran
down the runway. He received immediate feedback in the form of a beep when the
photoelectric beam was broken, indicating that he had achieved the desired height
set for that vault. The height of the beam was gradually increased until the vaulter
6 Goal Setting and Performance Feedback 109
reached targeted arm extension at take-off. The increases in arm extension were
matched by increases in bar height clearance.
Hume and Crossman (1992) used contingent musical reinforcement to improve
swimming behaviors on the dry land portion of s wimming practices (e.g., condition-
ing) and decreased nonproductive behaviors (i.e., eating, taking someone’s goggles)
of youth swimmers at practices. There were immediate and substantive increases in
productive behaviors and decreases in nonproductive behaviors.
Finally, several performance feedback “package” interventions have incorporated
combinations of goal setting, relaxation, imagery, self-monitoring, and self-talk with
track and field athletes in Special Olympics (Gregg, Hrycaiko, Mactavish, & Martin,
2004; Wanlin et al., 1997), adult tri-athletes and runners (Patrick & Hrycaiko, 1998;
Thelwell & Greenlees, 2003), youth figure skaters (Ming & Martin, 1996), youth
hockey goaltenders (Rogerson & Hrycaiko, 2002), and collegiate basketball players
(Hamilton & Fremouw, 1985; Kendall, Hrycaiko, Martin, & Kendall, 1990).
Concluding Comments
What is striking about the intervention procedures reviewed in this chapter is
the strength of their effects, and the social validity of the goals, procedures, and
outcomes that have been used. Additionally, the procedures are low cost, easily
implemented, and well accepted in sport settings by coaches and players alike. More
research is certainly needed to identify mechanisms at work in these procedures.
Furthermore, there should be more practitioner-based articles in coaching journals
and more frequent coaching workshops showing how goal setting and performance
feedback procedures can be adopted to improve athletic performance.
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Chapter 7
Cognitive–Behavioral Strategies
Jeffrey L. Brown
Can you think of any professional baseball player who has brought more attention to
the mental aspect of sport than syntactically challenged New York Yankee catcher
Yogi Berra? Berra is frequently quoted: “Baseball is 90% mental and the other half
is physical.” While his quote often prompts a chuckle and a knowing grin, Berra
was right about the cognitive aspects of performance. From baseball to swimming,
soccer to fencing, and golf to running, the brain is a fascinating ally in performance.
Performance is mental.
Today, research continues to reveal that the brains of athletes are somewhat
different than of non-athletes, due in part to the cognitive–behavioral aspects of
deliberate physical and cognitive training (Nakata, Yoshie, Miura, & Kudo, 2010).
Cognitive–behavioral interventions can clearly optimize mental performance and,
in some cases, even change the landscape of the brain. Today, cognitive–behavioral
strategies are fundamental to sport (Williams & Leffingwell, 2002).
When providing psychological services to athletes, it is common for psychol-
ogists to help them create an awareness of mental operations and how they are
connected to physical aspects of sport. For example, an athlete may concentrate
well under pressure or remain motivated even when the score is not in her favor.
It is wrong to assume that athletes who do not understand or have never heard
of cognitive–behavioral interventions are not using them. Rather, a psychologist
should expect that many psychological strategies are at play and that players pos-
sess the capacities to hone those cognitive–behavioral skills and learn new ones
along the way.
Traditionally, seasoned athletes may already possess mental skills which lead
to success. Rather than starting from scratch with the assumption an athlete has
no effective psychological skills at all, cognitive–behavioral consultation may be
a matter of identifying an athlete’s existing mental strengths in order to use
them more. Inasmuch, the role of the cognitive–behavioral psychologist is multi-
faceted, as is the psychological potential of the athlete. With such a good match
between the natural psychological constitution of athletes and the understanding
J.L. Brown (B)
Harvard Medical School, Boston, MA, USA
e-mail: jeffrey_bro[email protected]ard.edu
113
J.K. Luiselli, D.D. Reed (eds.), Behavioral Sport Psychology,
DOI 10.1007/978-1-4614-0070-7_7,
C
Springer Science+Business Media, LLC 2011
114 J.L. Brown
cognitive–behavioral research and theory can offer them, what could possibly go
wrong?
The Reality
Well-meaning coaches and parents take aim at athletes every season, offering novice
psychological advice such as “don’t think about it, just shoot the ball,” “are you
going to let her treat you like that on the field?,” or “let your arms and legs do all
the work.” Such bleacher-coaching can create frustration and reinforce poor cog-
nitive habits, especially for an athlete who has not yet developed a natural ability
to utilize mental skills. Further complicating the relationship between athlete and
sport psychologist is a cultural expectation that athletes are stalwart, tough, and
psychologically invincible.
During the early years of my doctoral training in cognitive–behavioral therapy, a
college football player who had been sidelined because of a broken arm was referred
to me. A few weeks after his arm was broken, his younger sister was killed in
an automobile accident. In the initial session, he appeared kind and reserved. He
answered questions with a “yes, sir” and “no, sir” attitude, almost like I was his
coach. The player was mildly disinterested and quietly reported that, in spite of the
recent events in life, things were going fine.
As the session progressed, the alliance grew only minimally as he talked about
what his sister and he used to do for fun. At times, he reported his thoughts and
emotions to me almost as if our conversation was a media interview and he needed
to stay in the middle of the road. At the time, his response style seemed reasonable
given the tremendous trauma and loss he had faced. I concluded his flat affect and
his mildly blunted communication was simply the best he could give considering
his circumstances.
We finished the session and scheduled a second appointment. Within hours of
scheduling the second appointment, the office manager forwarded a message to me.
The player had called to cancel the appointment. His message read: “My coach
required me to come to one meeting and that’s all I’m going to do.”
We could have a consultation heyday interpreting and analyzing this first session
and resulting phone message, but I can tell you now the most compelling informa-
tion had to do with what I learned from that athlete. What had been overlooked and
could have simply been addressed with an empathic question about it was this ath-
lete’s sense of control about being in therapy. He had no control over his arm being
broken or his sister’s accident. Now, his coach had taken control away once again
by requiring him to see a psychologist.
Some of what I had noticed in that first session with the athlete was accurate: the
blunted affect, the distance, the lacking energy. But I had forgotten a well-known
belief about athletes. Qualitative research and anecdotal accounts consistently reveal
athletes avoid the stigma that they believe accompanies mental health treatment.
Just like this football player, athletes may routinely struggle with initiating therapy,
issues of control, and being vulnerable.
7 Cognitive–Behavioral Strategies 115
Table 7.1 Circumstances
appropriate for
cognitive–behavioral
intervention
Learning skill, form, or technique
Developing concentration and focus
Increasing confidence
Increasing leadership abilities
Developing or terminating a career
Managing life stresses and life events
Rehabilitating an injury
Substance use
Aggression or anger management
Perfectionism
Eating disorders
Training compliance
Team cohesion
Communication
Time management
Etzel, Ferrante, and Pinkney (2002) highlight factors such as performance
demands and career transitions that can place unique demands on athletes.
Athleticism i s about strength and formidability. Athletes will have spent hundreds
and, in many cases, thousands of hours training their bodies for strength, endurance,
agility, and speed. Finding themselves in an unpredictable, vulnerable situation must
indeed feel counterintuitive. Therefore, it is critical for the psychologist attempt-
ing to work with an athlete to understand how t o frame the relationship in a way
that it offers strength and avoids the athlete’s likely fear of a stereotypical What
About Bob? or Good Will Hunting experience (Glick & Horsfall, 2009). Relative to
this topic, see Table 7.1 for typical reasons athletes would actually use cognitive–
behavioral therapy in a therapeutic setting. If you do not have an alliance, then
cognitive–behavioral work will never happen.
The Alliance
Because athletes are uncomfortable with the idea of therapy and vulnerability, I have
learned to offer psychological services that are characterized by what I have coined
as a “personal consultant” model. I ask athletes to think of me as a personal consul-
tant who is an expert in human behavior, rather than thinking of me as a counselor
or therapist. To an athlete who is concerned about delving into psychological mat-
ters, a personal consultant model seems more acceptable and permits the athlete to
seemingly avoid the stigma of “seeing a counselor” or “having a therapist.” It is no
surprise athletes find it hard to initiate therapy, let alone stay in it. Therapy is both an
art and a skill, and cognitive–behavioral theory is a wonderful guide for negotiating
and developing the relationship.
This chapter aims at helping readers understand more about how to success-
fully engage an athlete in cognitive–behavioral therapy, build an alliance, and
offer research-based behavioral interventions. I discuss basic tenets of the inter-
ventions, how to implement them, and the potential outcomes and pitfalls. My
116 J.L. Brown
perspective is that a sport psychologist’s competence in cognitive–behavioral ther-
apy can be a mitigating factor when engaging an athlete in the process of change
and improvement.
Know Your Clients and the Language They Speak
A lesser known author whose book is wedged on my bookshelf somewhere between
Sigmund Freud and B. F. Skinner is George Herman Ruth (1928) – yes, the leg-
endary homerun-hitting icon (i.e., “Babe” Ruth), who has been associated with a
worn-out cognitive distortion Bostonians called a curse until 2004. (After 86 years,
Red Sox Nation finally restructured its irrational belief with evidence of a World
Series win.) Mr. Ruth was hardly the traditional academic type, but he made a
notable assertion in his 1928 book that the baseball players he played with were reg-
ular people from different walks of life. Many of them had varied interests ranging
from finance and politics to musical skill. Irrespective of a psychologist’s theoreti-
cal orientation, I suppose most clinicians today would agree with Ruth’s revelation
from over 80 years ago that athletes are regular people who, by the mere fact of run-
ning the human race with everyone else, will likely face psychological adversities.
Today, cognitive–behavioral psychology is logically teamed with sport psychology,
not just because research has discovered the fit, but because cognitive skills are what
athletes use. Thus, working with athletes now requires specialization and exper-
tise, both in understanding the sport culture and in demonstrating competence in
cognitive–behavioral psychology.
While I have maintained a traditional cognitive–behavioral practice working
with adults and adolescents, I have found that work with athletes and cognitive–
behavioral therapy dovetail quite smoothly. My background as a college baseball
player no doubt stirred my interest in sport psychology, but it was not until later in
graduate school that I was able to develop actual professional competence in sport
psychology and cognitive–behavioral therapy.
It is quite compelling and frankly convenient that athletics and a cognitive–
behavioral therapy framework share some common terminologies such as goals,
practice, evaluation, and feedback. Therefore, a cognitive–behavioral approach is
particularly helpful for and interesting to athletes who may need more traditional
clinical support from time to time. Additionally, in order to deliver cognitive–
behavioral interventions effectively, it is requisite to know its historical and
contemporary implications.
A Very Brief History of Cognitive–Behavioral Psychology
and Sport Psychology
Both cognitive–behavioral psychology and sport psychology have been predicated
by generations of research and application in various domains of sport. In the
1890s, the study of sport and behavior made its American debut when Norman
Triplett researched bicyclists’ performances both individually and against each
7 Cognitive–Behavioral Strategies 117
other. Shortly after the turn of the century, Triplett passed the baton to his prodi-
gious student, Coleman Griffith, who is considered the father of American sport
psychology (Weinburg & Gould, 1995).
Ghinassi (2010) provides a succinct overview of the development of cognitive–
behavioral therapy per se, beginning with the influence of early theorist George
Kelly, whose personal construct theory reflected his belief that an individual pos-
sesses constructs or representations that are “the way in which an individual
understands or construes the world. Constructs help predict what will happen and
how to react appropriately” (p. 33). Sport psychologists using cognitive–behavioral
approaches in sport settings frequently find Kelly’s constructs to be at work (Kelly,
1963). In general, it is commonplace (and frequently preoccupying) for athletes to
want to predict outcomes, compare themselves to others, and make decisions about
self-confidence in the absence of physical, objective evidence.
While Kelly may have made it to the psychology hall of fame a first, two
more inductees who would eventually keep him company are Albert Ellis (2001)
and Aaron T. Beck (Beck, 1995). Both men were psychoanalysts-turned-cognitive-
behaviorists. Ellis, who died in 2007, developed what is currently known as Rational
Emotive Behavior Therapy. I personally saw Ellis in action in his New York City
institute on more than one occasion and can attest that his approach was focused
more on problem solving through the elimination of cognitive distortions and less
on alliance building with the client. Even at the risk of making a client look unin-
telligent, Ellis would attack illogical and irrational thoughts. Beck, on the other
hand, emphasizes a more collaborative approach with a client. The collabora-
tion explores negative beliefs and schemas that lead to negative emotion about
the self. A host of other cognitive–behavioral psychologists’ influences continue
to shape our understanding of therapeutic theory, strategy, and technique in sport
psychology.
More than a century after its inception, sport psychology is a clearly defined spe-
cialty that is recognized formally by national organizations such as the Association
of Applied Sport Psychology and the American Psychological Association’s
Division 47, Exercise and Sport Psychology. Sport psychology possesses its own
ethics code and offers a certification process intended to establish competency
through documentation of coursework and supervised training experiences through
the Association for Applied Sport Psychology. Sport psychologists who are certified
as a consultant in the Association for Applied Sport Psychology may also serve on
the United States Olympic Committee (USOC) Registry of Psychologists. Inclusion
on the Registry creates an opportunity to work with Olympic athletes and teams
who utilize USOC resources throughout the country.
Myths about Cognitive–Behavioral Therapy: Protecting
Theory and the Athlete
The history of cognitive–behavioral psychology and sport psychology was not
free of challenges and biases. Today, misunderstandings about cognitive–behavioral
therapy exist and directly affect professional practice. Clearly responding to these
118 J.L. Brown
misunderstandings may help athletes more confidently engage in therapy and have
informed expectations. Five myths about cognitive–behavioral therapy and sport
seem to exist.
Myth 1: Cognitive–Behavioral Therapy Is a Collection of Tricks
Many prospective clients and some clinicians unfamiliar with cognitive–behavioral
therapy erroneously believe it consists of tricks or gimmicks for developing a par-
ticular skill. Add to that belief an underlying notion that these alleged tricks require
only little effort and what you will quickly have is a recipe for disaster, with the
psychologist being blamed for failure. Athletes need to be educated about how
cognitive–behavioral therapy is a diverse, research-based approach to healthy think-
ing, feeling, and behavior. While it should be methodically and characteristically
strategic, it is not a secret formula or a series of fancy parlor tricks.
Myth 2: Cognitive–Behavioral Therapy Produces
Immediate Effects
In some cases, it is true that clients respond to cognitive–behavioral interventions
faster than others. Realistically, cognitive–behavioral therapy is an approach that
brings about change over time through cognitive processes such as restructuring,
self-monitoring, practice, and experimentation with new thoughts or behaviors. The
human physique changes as the result of deliberate, purposeful, and repetitive exer-
cise over time. The same effort is r equired for the human brain (Brown, Fenske, &
Neporent, 2010).
Prospective clients may initiate contact for services because of a performance or
critical competition that is to occur in a matter of days or weeks. Not surprising,
the telephone call or email has often been prompted by increased anxiety related to
the approaching competition. Anxiety in parents or spouses, not just in the athletes,
can be a driving force for a referral as well. Psychologists should avoid being driven
by the client’s anxiety or the unrealistic expectation that any positive outcome will
be immediate. Quickly clarify what is to be expected, emphasizing consistent work
over time.
Myth 3: Once Learned, Cognitive–Behavioral Strategies
Should No Longer Be Practiced
The comparison of cognitive–behavioral training and physical training is a per-
fect analogy. When helping clients understand how to incorporate and benefit from
cognitive–behavioral work on an ongoing basis, it is good to explain it as a devel-
opmental process. Be sure to include sport examples. Football players would not
start lifting weights a week or two before their first game. Rather, they would
7 Cognitive–Behavioral Strategies 119
be lifting weights months before and throughout the season. Marathon runners
do not take a quick jog around the block or make fast friends with a treadmill
just days before staring down 26.2 miles. They train months at a time in prepa-
ration for a single performance. Mental training, just like physical training, should
be strategic and ongoing. Compliance with cognitive–behavioral training can be
difficult.
In our win-at-all-cost culture, athletes s eem to have an increased interest in
psychological aspects of performance. Moreover, research and brain science is pro-
gressing at such a rapid pace that the lay public is learning more about the brain’s
capabilities for success and how they can take an active role in the process. In that
same busy culture, however, athletes frequently learn psychological concepts, but
fail to practice or apply them. It is much like knowing that the oil in a car needs to
be changed regularly, but it does not happen. It is clear the car will run better, be less
apt to need repair, and promote overall efficiency. But, nonetheless, it is neglected.
Encourage athletes to consistently practice and use cognitive–behavioral strategies
as part of ongoing training commitments.
Myth 4: Cognitive–Behavioral Therapy Is Used Only
for Treating Psychopathology
A vast collection of research has examined cognitive–behavioral therapy’s t reatment
efficacy across a wide range of psychopathological conditions. Indeed, cognitive–
behavioral therapy is the treatment of choice for many conditions, ranging from
irritable bowel syndrome and obsessive compulsive disorder to sexual dysfunc-
tion and panic disorder (Freeman, 1995). However, it is not limited to just treating
uncomfortable or annoying symptoms. Indeed, most people, athletes included, will
likely benefit from therapy at some point in life. Even more, athletes and other per-
formers can benefit from cognitive–behavioral interventions prior to experiencing
a mental health crisis. It is this differential you will make to your client that can
spark interest and build an alliance. Emphasizing the positive health components of
cognitive–behavioral psychology will help eliminate the theory’s wrong exclusive
association with pathology (Brown et al., 2010).
Myth 5: Any Clinician Can Provide Cognitive–Behavioral
Therapy
This perception is probably linked to the “bag of tricks mentality” about cognitive–
behavioral therapy. Once you know the tricks, you are an expert. Schinke and
Watson (2009) draw on the ethics code set forth by the Association of Applied
Sport Psychology when directing t hat “the critical aspect of competence is that
it is acquired with time and effort” (p. 17). It is clear that competence as a
cognitive–behavioral psychologist does not happen as a matter of chance or by
the ability to follow cook-book methodology. Cognitive–behavioral interventions
120 J.L. Brown
should be uniquely designed and offered to athletes based on their individual
strengths and deficits (Hays, Thomas, Maynard, & Bawden, 2009). It is the result
of combined formal study, supervision, and practice that develops the competent
professional.
Further, sport psychologists are not qualified to practice based on general interest
in sports or previous athletic experience. While experience is a good teacher for
many aspects of life and drawing on previous athletic experience may be helpful
when building an alliance with a client, it is insufficient for meeting professional
standards of practice (Brown & Cogan, 2006).
Behavioral Interventions in Sport
Cognitive–behavioral interventions in sport abound, but I will focus on three nec-
essary strategies that should be in every sport psychologist’s toolbox. Each section
includes a brief description of the intervention, a vignette, and a discussion. I also
point out potential pitfalls when implementing each strategy.
Goal Setting
As I emphasized previously, cognitive–behavioral therapy and sport psychology
complement each other quite naturally. Athletes are goal- and performance-oriented,
much like cognitive–behavioral therapy. A foundational element to cognitive–
behavioral therapy is to have at least one specific goal. When athletes commit
to goals, it is likely they will strive to meet them and, in that pursuit, desire to
apply even more psychological strategies (Crust & Azadi, 2010). It is the role
of the psychologist to help an athlete define a goal well and offer additional
cognitive–behavioral strategies for reaching it.
A well-defined goal not only is a starting point for working together, but also
helps the psychologist and athlete immediately share something in common, ulti-
mately strengthening their growing alliance. Goal setting as a cognitive process is
valued as an effective cornerstone to all other performance-enhancement strategies
(Meyers, Whelan, & Murphy, 1996).
Goal Setting Vignette: Alexis on the Run
Alexis, a 26-year-old law student, wants “to run better.” She runs every day, but
thinks she could make her running “more effective.” She explained her work in
law school is tedious and time consuming. One evening she went running with a
friend who was soon moving to a different state. Alexis experienced the run in such
a positive way that she was hooked. When asked what she specifically wanted to
improve about her running, she said that she did not know, but she just thought
something could be different in a better way.
7 Cognitive–Behavioral Strategies 121
Goal Setting: Intervention and Discussion
Alexis’s intentions are good. She is motivated and wants help. A key piece that is
missing for her is a clear goal. In cases of goal setting, it is helpful to explain t wo
types of goals to an athlete. The first type of goal is an outcome goal. An outcome
goal is exactly what it sounds like. It is the measure of a performance after it is over.
Outcome goals come in t he form of a final score, a time or some measurement. What
an outcome goal ignores, and what is critical to helping an athlete set healthy goals,
is the actual behavior during the performance.
The second type of goal that is critical is the performance goal. Performance
goals are individualized, personal goals that are unique to an individual performer. it
is common to have multiple performance goals within one outing or activity where
a goal can be reached. Before identifying performance goals for Alexis, I should
emphasize that the other critical criteria for goal setting is ensuring that goals are
objective and measurable.
Too often an athlete sets vague goals such as “feeling better about how I played”
or “making my coach believe in me.” While those goals have meaning to the athlete,
it may be unclear when the athlete actually meets that goal. Therefore, it is important
to make certain a goal is objective and measureable (this is what we call a “behav-
ioral” goal). I use a two-question test for athletes to determine if they have identified
a useful goal. First, to test for objectivity, I ask, “If ten people watched you perform,
would they all agree that you met your goal?” Next, to test for measurability, I ask,
“Did you use some unit of measurement to describe your goal?” For example, did
the athlete use minutes, pounds, seconds, or days or, in the case of emotion, rated
its strength on a scale of 1–10? If the answer to both of these questions is yes, then
the athlete has likely identified a useful, behaviorally focused performance goal.
Additional factors that are crucial when setting performance goals include devel-
oping an evaluation and feedback system, sharing goals with encouraging support
systems, and ensuring that the athlete has the skill necessary to reach the goal.
In Alexis’s case, she would fail the test, would not she? It is the psychologist’s
responsibility to help her clarify those goals. To help Alexis, the two-question test
might sound something like this:
CBT: Alexis, when you are running and enjoying it, what would other people
who were watching you say you’re doing when that happens.
Alexis: They’d see me running through the city in the evening.
CBT: How long would you have been running when they saw you?
Alexis: About 30 minutes into my run.
CBT: If they were watching for you all of the time, how often would they see
you running?
Alexis: Twice a week, probably. Law school keeps me busy, you know!
CBT: You said you wanted your running to be better in a different way. What
would all of those people see you doing if your running was better?
Alexis: They’d probably see me more often. When I run I enjoy it so much, but
it’s hard to find the time to do it. They’d probably see me running with
friends, too.
122 J.L. Brown
This example of a brief interchange demonstrates a process of converting an
athlete’s soft goals into objective, measurable goals. There is more work here to do
with Alexis, but she and the psychologist are on a better track to understand what
she truly desires. Likely her goal will include increased frequency and time, as well
as more social contact. As goals solidify for Alexis, it will become more clear what
other behavioral strategies might help her in her quest and how her performance
goals will become a priority over her outcome goals.
Evidenced-Based Self-Talk
The positive influence an athlete’s thinking can have on emotion and performance
can be significant (Tod, Thatcher, McGuigan, & Thatcher, 2009). For example,
Medvic, Madey, and Gilovich (1995) described how Olympic athletes who medaled
have a r ange of thought styles about their success, even though they performed
expertly. For two reasons, such research is significant to the athlete who is con-
sidering cognitive–behavioral therapy. First, it is helpful for athletes to learn from
research that focused on successful, not pathological, athletes. Secondly, focusing
on how those athletes think normalizes and perhaps even makes the information
gleaned from the research desirable to know. As a clinician, you do not have to trick
an athlete into utilizing cognitive–behavioral strategies, but it is important to present
the most helpful information in a useful manner so the athlete can make an informed
decision.
Evidence-Based Self-Talk Vignette: Theo’s Thoughts
Theo is a 17-year-old high school pitcher. His pitching is inconsistent because his
confidence wavers. While he denies it to his coach and parents, he revealed in con-
sultation that he is often intimidated by certain teams or strong hitters. He was able
to identify an anxious feeling related to lacking confidence, which surfaced a couple
of days before a game against strong performers and continued through his pitching
outing.
Evidenced-Based Self-Talk: Discussion and Intervention
Theo was asked to keep a self-talk log. For his convenience and for compliance, he
simply kept an index card in his hip pocket and wrote down three pieces of infor-
mation: (a) the situation, (b) his thoughts, and (c) the resulting emotion or behavior
(see Table 7.2 for a sample of Theo’s self-talk log).
Fundamental to cognitive–behavioral therapy is making certain that an individ-
ual’s thoughts are rational and not distorted. I prefer to use the terms “accurate”
and “evidence based” when working with athletes, primarily because words like
“irrational” and “distorted” can have a negative connotation. Thinking accurately
7 Cognitive–Behavioral Strategies 123
Table 7.2 A sample self-talk log
Situation Thoughts Resulting emotion or behavior
Reading box scores of
opposing team
“They are going to hit me out
of the ballpark.”
Anxiety, loss of sleep
Pre-game team meeting “My coach doesn’t believe I
can pitch well.”
Anxiety, decreased confidence
Listening to teammates talk
about how we need to win
“I’m going to let my team
down.”
Dread, embarrassment
about oneself and having evidence for those beliefs is essential for a solid perfor-
mance. Athletes are usually aware of their performance statistics (times, personal
records, averages, and so forth); therefore, the evidence-based way of t hinking is
quite natural and makes sense.
As Theo reviewed his thought log in session, it was easy to take aim at the
thoughts section of the log and identify thinking that had no evidence. For example,
Theo thought the batters on the opposing team would hit his pitching so well that
he would be embarrassed and removed from the game. Helping him identify that he
was predicting the future and that any team is expected to hit the opposing team’s
pitching caused his anxiety to reduce and made him feel better about throwing in
the game.
Many different types of cognitive errors can exist, and it is the cognitive–
behavioral psychologist’s responsibility to understand how inaccurate ways of
thinking can be at work both obviously and covertly. The Feeling Good Handbook
by David Burns (1999) is a classic read and a good starting point for understanding
various distortions that can be upheld and can affect emotion and self-perception.
Imagery
Imagery, sometimes referred to as visualization in sport psychology, is a heav-
ily researched behavioral technique that demonstrates robust efficacy (Gould,
Damarjian, & Greenleaf, 2002). Whether it is used for skill acquisition, relaxation
in a pre-competition routine, or mastery of emotional balance and control during
competition, imagery is a skill that is likely useful to any athlete who integrates
it into a training routine on a regular, consistent basis. Two powerful character-
istics of imagery that are particularly advantageous to athletes are vividness and
controllability.
Vividness refers to the quality of imagery and how detailed it becomes through
the use of all the senses. Sight, sound, smell, touch, and taste help enrich the vivid-
ness of a rehearsed image. For example, the sounds of the stadium, the taste of
salty sweat on one’s lips, the pressure of tightly laced cleats, the smell of popcorn,
heat rub ointment, or chlorine each adds a dimension to an athlete’s imagery. These
details probably help the brain rehearse an experience as if it were real and activates
similar parts of the brain, whether the activity is imagined or actually motor based
124 J.L. Brown
(Guillot et al., 2009). Also, rehearsing and practicing the performance at the speed
it naturally occurs are preferable.
The second factor unique to imagery is controllability. Simply, an athlete can
control the image to whatever degree she/he prefers. Athletes can create situa-
tions utilizing imagery where they may not otherwise experience or experience
infrequently. Taking risks, pushing physical limits, or increasing the frequency of
repetitions for practice are ways controllability can be used to maximize imagery’s
benefits.
Two separate but related measures for understanding an athlete’s imagery expe-
rience is the Sport Imagery Questionnaire (SIQ; Hall, Stevens, & Paivio, 2005)
and the Sport Imagery Questionnaire for Children (SIQ-C; Hall, Munroe-Chandler,
Fishburne, & Hall, 2009).
Imagery Vignette: Tristan Sees Himself Improve
Tristan has been a goalie for his local town soccer team for the past 2 years. Even
though he started playing soccer later in life than most of his peers, his natural
talent quickly catapulted him to being goalie. Knowing that he had started playing
soccer several years after his peers did, he often felt behind and still made some
rookie mistakes. He wanted to improve his performance and his confidence, but was
already maximizing his time with daily practices and college preparatory classes at
school.
Imagery: Intervention and Discussion
While Tristan’s schedule was full, he was able to devote 20 min each day to work on
imagery in his bedroom. He used his imagery to expose himself to numerous situa-
tions where he had to respond as a goalie. Using an assortment of scenarios, Tristan
imagined in real-time himself responding positively, effectively, and confidently on
the soccer field. When he was ready, he also imagined making typical goalie errors
and then successfully and immediately recovering from them. He found he could
actually experience more blocks and situations in 20 min of imagery work than
he could during 90 min of practice. Additionally, he was able to develop imagery
as a skill that he could use for pre-game work or for taking exams in his college
preparatory classes.
Conclusion
Sport is an extraordinary metaphor for life because it aptly teaches social and
intrapersonal values such as resilience, motivation, integrity, and cooperation.
Historically, sport has been enjoyed as a cultural activity well before any con-
temporary psychological theory came along to explain what the brain is doing
during competition and how to help it do it better. By now, it is clear that
7 Cognitive–Behavioral Strategies 125
cognitive–behavioral psychology has caught up to sport in many aspects. Athletes,
coaches, and parents now have volumes of research and interventions about how
cognitive—behavioral psychology can improve performance. The professional sport
psychologist today has the responsibility and privilege of working with multifaceted
players with typically high expectations for performance. Balancing those expecta-
tions, reducing stigmas about mental health, developing an working alliance, and
delivering interventions in a skillful, ethical manner are all in a day’s work.
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Chapter 8
Establishing and Maintaining
Physical Exercise
Christopher C. Cushing and Ric G. Steele
Physical exercise can be defined as any goal-directed activity that is intended to
improve or maintain physical fitness, and which involves the movement of skele-
tal muscles resulting in energy expenditure (Caspersen, Powell, & Christenson,
1985). This definition is important because it establishes the goal-directed nature
of physical exercise, which distinguishes it from physical activity. For example, in
reading this chapter, a number of physical activity behaviors will be performed (e.g.,
turning pages, shifting positions, walking to retrieve refreshments). However, these
behaviors are not intended to promote or maintain health and therefore do not con-
stitute physical exercise. Despite the importance of this distinction, it is difficult for
researchers to objectively measure bouts of physical exercise, and gross measures
of physical activity are usually used as a proxy indicator. Because this chapter is
focused on physical exercise promotion, we will discuss findings in terms of phys-
ical exercise even when the primary studies used physical activity as an outcome
variable.
By way of an overview, we begin with a brief review of the physical and psy-
chological benefits of physical exercise to provide the reader with an understanding
of the types of outcomes that have been associated with physical exercise. Next, we
aim t o present two goal-oriented theoretical frameworks that are regularly applied
to physical exercise and describe intervention components that map onto these the-
oretical frameworks. Finally, we conclude with brief recommendations for practice
and future research based on theoretical models.
Benefits of Physical Exercise
Beyond increasing athletic performance, routine physical exercise has a number of
benefits for health and well-being across the lifespan. Individuals who are more
physically active have a lower risk of cardiovascular and cardiorespiratory disease,
R.G. Steele (B)
Clinical Child Psychology Program, University of Kansas, Lawrence, KS 66045, USA
e-mail: [email protected]
127
J.K. Luiselli, D.D. Reed (eds.), Behavioral Sport Psychology,
DOI 10.1007/978-1-4614-0070-7_8,
C
Springer Science+Business Media, LLC 2011
128 C.C. Cushing and R.G. Steele
cancer, degenerative bone conditions, endocrine system disorders, and the nega-
tive physical sequela associated with obesity (Folsom et al., 1997; WHO, 2004).
In addition to higher levels of physical health, individuals that are physically
active also experience significant psychological benefits (Hamer, Stamatakis, &
Mishra, 2009; Ströle, 2009;Walsh,2011). One area of recent exploration is the
impact of physical exercise on hippocampal volume and memory. Animal mod-
els have demonstrated significant increases in brain derived neurotrophic factor
(BDNF), hippocampal volume, and memory in rats that engage in more physical
exercise (van Praag, 2008). Recently, an intervention trial of prescribed moderate
physical exercise (40 min per day × 3 days per week) demonstrated signifi-
cant salutary changes in hippocampal volume among previously sedentary adults,
providing preliminary evidence that physical exercise is linked to central ner-
vous system functioning and subsequent psychological ability (Erickson et al.,
2011). Similarly, a 13-week after-school exercise intervention study in overweight
children demonstrated significant improvements in cognitive ability with a dose–
response effect (Davis et al., 2011). Results indicated that children in exercise
groups evidence significantly greater changes in executive functioning compared
to a control group. In addition, the investigators discovered a dose–response effect
for both mathematics achievement scores and executive functioning, indicating
that 40 min of exercise produced superior benefits to 20 min of exercise. Taken
together, these studies suggest that many biologically mediated psychological ben-
efits of physical activity are available beyond the physical health benefits of
exercise.
Physical exercise also impacts a number of subjectively experienced psycho-
logical constructs. Routine physical exercise appears to help alleviate depression
(possibly through the BDNF pathways described above); in fact, the evidence
for the impact of physical exercise on depression is so convincing that some
have called for physical exercise interventions as primary or adjunctive treat-
ments for clinical depression (Fox, 1999;Walsh,2011). Quality of life (QOL) also
shows promise as one of the variables that is most sensitive to change in healthy
lifestyle interventions. In a recent meta-analysis of physical exercise interventions,
Conn, Hafdahl, and Brown (2009) discovered a small but significant effect size in
experimental studies that prescribe physical exercise. It is important to note that
these interventions were not designed to modulate QOL directly. This means that
physical exercise confers a direct benefit on QOL even when interventions hold
changes in other variables as their primary focus (e.g., recovery from myocar-
dial infarct). The finding that physical activity interventions produce changes in
QOL is consistent with some descriptive work indicating that children who are
more active during the school day experience better QOL irrespective of their
weight status (Shoup, Gattshall, Dandamudi, & Estabrooks, 2008). An implica-
tion of these studies is that engaging in physical activity even when a disease or
physical health process is involved can derive important subjective psychological
benefits.
8 Establishing and Maintaining Physical Exercise 129
Baseline Levels of Physical Exercise and Recommendations
The Center for Disease Control (CDC) and the Surgeon General recommend that
adults participate in a minimum of 150 min of moderate-to-vigorous physical
exercise each week, with an ideal target of 300 min, and that children partici-
pate i n 60 min of moderate-to-vigorous physical exercise every day of the week
(Department of Health and Human Services, 2008; CDC, 2011). A recent analysis
of objectively reported physical exercise collected as part of the 2003–2004 National
Health and Nutritional Examination Survey (NHANES) revealed that 6–11-year-old
children are the only group that can be said to meet their broad moderate-to-vigorous
physical exercise recommendations, but even this group was not engaged in enough
vigorous physical activity (Troiano et al., 2008). Moreover, compliance with physi-
cal exercise recommendations is even poorer when considering bouts of physical
exercise lasting 10 min or more, which is thought to be a measure of planful
sustained exercise (Troiano et al., 2008).
Theoretical Models of Health Promotion
“There i s nothing so practical as a good theory” (Lewin, 1951, p. 169). Lewin’s
words are not lost on the modern behavioral scientist. In fact, behavioral psychology
has seen a large expansion of theoretical work since the time of Lewin’s writing,
and it can be difficult to find one overarching theory to guide the development and
implementation of physical exercise research. Evidence is available to support a
number of important and frequently articulated theories governing the promotion of
physical exercise, including Social Cognitive Theory (SCT; Bandura, 2004), Control
Theory (CT; Carver & Scheier, 1982), the Theory of Reasoned Action (Fishbein &
Ajzen, 1975; Fishbein, 1967), and the Theory of Planned Behavior (Ajzen, 1985;
see Rapoff, 2010, for a general review of these theories).
In this chapter we focus primarily on two of these (SCT and CT) for a num-
ber of reasons. First, both the Theory of Reasoned Action and the Theory of
Planned Behavior significantly overlap with each another (Rapoff, 2010) and fre-
quently do not explain variance above what can be explained using SCT (Bandura,
2004; Dzewaltowski, Noble, & Shaw, 1990). Second, CT and SCT are highlighted
here because of the clear link to operant psychology and the implications for set-
ting up antecedents and consequences necessary to establish and maintain physical
exercise. More specifically, we believe these theories have both heuristic and prac-
tical applications, in that they frequently describe contextual preconditions for
behavior change. Below we briefly describe the application of SCT and CT to
physical exercise promotion. The sections below are brief discussions of complex
theories. The interested reader is referred to Bandura’s (2004) application of his
theory to health behavior change and Carver and Scheier’s (1982) seminal arti-
cle on CT for additional reading. Finally, we conclude with a comment on the
130 C.C. Cushing and R.G. Steele
importance of determining the effectiveness of individual theoretical/intervention
components.
Social cognitive theory. As it applies to physical exercise, SCT specifies that
five core factors influence an individual’s ability to self-regulate their behavior
(Bandura, 2004). First, the individual must have sufficient knowledge about the
risks of sedentary behavior and benefits of engaging in physical exercise. Indeed,
many physical exercise interventions attempt to modulate this factor; a recent review
article revealed that 55% of physical activity interventions designed for adults
include some component of educating participants about the health consequences of
their current behavior (Michie, Abraham, Whittington, McAteer, & Gupta, 2009).
Further, in a large meta-analysis of clinical trials to promote physical exercise in
adults with chronic illness, Conn, Hafdahl, Brown, and Brown (2008) reported that
interventions with supervised exercise sessions were no more effective in changing
physical activity than those that relied exclusively on educational or motivational
sessions. Given the relatively robust overall effect sizes of included studies, these
results underscore the importance of educational and/or motivational components
of treatments.
The individual’s beliefs about the expected costs and benefits for different health
habits make up outcome expectancies, the second factor in Bandura’s (2004) model.
This construct is subdivided into physical, social, and self-evaluative areas. Physical
outcome expectations are perhaps most obviously related to physical exercise. An
individual who believes that physical exercise will make them tired, sweaty, and
uncomfortable can be said to have negative physical outcome expectations and
will engage in less physical exercise (Nelson, Benson, & Jensen, 2010). For such
individuals, the behavior therapist may need to set graded tasks, provide specific
encouragement, and model attentional redirection so that the individual can begin
to derive pleasure from physical exercise. Social outcome expectancies include the
approval or disapproval (i.e., social contingencies) one receives from others for per-
forming a behavior. The behavior therapist should be careful not to overlook this
factor when addressing the initiation of a new physical exercise regimen, especially
in overweight or obese individuals. Recent research has indicated that overweight
young girls who are dissatisfied with their body engage in significantly more physi-
cal activity than those who are happy with their body; however, this healthy exercise
behavior is negatively moderated by weight-related criticism (Jensen & Steele,
2009). The final outcome expectancy is self-evaluation. This includes the personal
standards and positive and negative evaluations used to judge one’s health behavior.
The role of the behavior therapist is to ensure that the individual is setting concrete
and proximal goals (e.g., “walk for 10 min three times this week”) rather than distal
or abstract goals (e.g., “get healthy”). By setting concrete proximal goals that can
be followed by more close approximations of a long-term goal, the behavior ther-
apist can reduce the negative self-evaluations because the individual is more likely
to attain their goal and have a positive appraisal of their own ability (i.e., improved
self-efficacy).
Understanding and ameliorating impediments or barriers to goal attainment
constitutes a critical component of Bandura’s (2004) model of health behavior
8 Establishing and Maintaining Physical Exercise 131
promotion. Individuals are often able to think of many barriers to physical activity
(e.g., low energy, lack of time, etc.). Using SCT, the behavior therapist may be able
to help the individual identify and remove barriers to achieving their goals. This is
a common component of interventions for physical exercise; Michie and colleagues
(2009) discovered that 42% interventions to affect physical exercise in adults use
barrier identification and amelioration as an intervention component. However,
Conn et al. (2008) reported that interventions that included barriers management
(among adults with chronic illness) were no more effective than interventions that
did not include such strategies. Clearly, more work in this area is needed to resolve
the unique contribution of barriers management to exercise promotion.
Bandura (2004) has argued that perceived self-efficacy is the most central tenet of
his theory to explain behavior. Self-efficacy is the belief in one’s ability to accom-
plish a goal. Individuals with higher self-efficacy set loftier goals and remain more
firmly committed to them (Bandura, 2004). Bandura posits that self-efficacy has a
direct impact on individual behaviors as well as indirect effects, through the four
processes detailed above. For example, a person may know that exercise is impor-
tant to attain cardiovascular health and set a goal to adhere to the Surgeon General’s
recommendations for physical activity. However, if the individual does not believe
that they can engage in aerobic exercise or if they believe that exercise will not lead
to their end goal of cardiovascular health, then motivation to engage in the behavior
will remain low. They may also believe that their efforts generally do not produce
the outcomes that they desire (low self-evaluative outcome expectations). Because
self-efficacy is so central to Bandura’s (2004) theory, there is not a single inter-
vention component that is thought to modulate self-efficacy; rather, all intervention
components of social cognitive interventions affect self-efficacy in each of the four
domains identified above.
Two large tests of SCT variables have demonstrated indirect effects of self-
efficacy on physical exercise. The largest study to date was conducted by Anderson,
Wojcik, Winett, and Williams (2006) in the context of a church-based health-
promotion intervention. The investigators used structural equation modeling (SEM)
to test the impact of social support, self-efficacy, physical outcome expectations,
and self-regulation on physical activity in a group of 999 primarily overweight and
obese churchgoers in southwest Virginia. The results of the SEM analysis explained
46% of the variance in objectively measured physical activity. SCT variables that
impacted physical exercise were social support mediated by self-efficacy, and self-
regulation, underscoring the importance of self-regulation and social support to
a healthy lifestyle. In their discussion of the findings, the authors suggested that
self-efficacy may be an important intermediate step to physical exercise adoption;
however, self-regulation conferred the largest independent effect.
Similarly, Rovniak, Anderson, Winett, and Stephens (2002) examined SCT vari-
ables as a predictor of physical exercise in a structural equation model of data
from a large sample of undergraduate students. Specifically, the model (which
explained 55% of the variance in the data) suggested that social support signifi-
cantly influenced self-efficacy for exercise and that this relationship was mediated
by self-regulation. That is, participants who experienced higher self-efficacy also
132 C.C. Cushing and R.G. Steele
engaged in more self-regulatory behaviors leading to greater physical exercise adop-
tion. Taken together, the results of Rovniak et al. (2002) and Anderson et al. (2006)
indicate that self-efficacy may be the cognitive set that increases the probability of
physical exercise occurring, but self-regulation is the observable behavioral support
that facilitates an individual’s engagement in physical exercise. These are partic-
ularly important to the behavioral therapist in that they highlight the importance
of assessing social support and self-efficacy for exercise followed by coaching in
adoption of self-regulatory behaviors.
Control theory. In their seminal article, Carver and Scheier (1982) described
interdisciplinary cybernetic control theory and how it can be applied to goal-oriented
cognitive–behavioral psychology. CT posits that behavior is a part of a negative
feedback loop. The loop is negative because it serves always to reduce the discrep-
ancy between a current state and a reference value (e.g., a goal). The loop functions
such that information from the environment (e.g., self-monitoring) is perceived by
an individual and compared against a reference value (e.g., goal). According to CT,
if the individual perceives a discrepancy between the current state and the reference
value, the individual performs a behavior to decrease the discrepancy. Once goals
are formulated, individual behaviors are simply an effort to correct the discrepancy
between the ideal goal and the current state.
Within CT, all behavior is hierarchically organized. Therefore, an individual can
engage in multiple goal-oriented behaviors simultaneously, and each of these behav-
iors can serve to move the individual closer to multiple hierarchically organized
goals at one given time point. Due to this hierarchical organization, CT can be
used to explain thoroughly expansive concepts such as engaging in a level of exer-
cise consistent with the sociocultural ideal (e.g., achieving health through exercise,
adhering to exercise recommendations, becoming a fit person) as well as lower-order
goals such as the contraction of individual muscle groups that produce voluntary
running. Therefore, CT is simultaneously more expansive and reductionistic than
social learning theory.
Practically, CT requires that an individual sets a goal for physical exercise.
For example, an individual may set a goal to walk for 40 min per day 3 days
per week. The next step is to monitor one’s compliance with the goal. If a self-
monitoring record shows that exercise was performed on 2 of 3 days, then the
individual uses this feedback to resolve the discrepancy between the goal and
the performance. In this system, if the superordinate goal (see above) is “overall
health,” then the individual will produce a behavioral change to increase physical
exercise. By this point, the experienced behaviorist can probably predict that the
key intervention components in CT are goal setting (including frequency, inten-
sity, and duration), self-monitoring, feedback, and review of goals. It is important
to note, however, that CT would suggest t hat these processes have a synergistic
effect on each other. This does not necessarily require that intervention compo-
nents always be used in conjunction with one another. For example, modulating
self-monitoring may change the feedback available to an individual without requir-
ing that the interventionist actively participate in providing feedback. Nonetheless,
interventions that attempt to modulate more than one component of a synergistic
8 Establishing and Maintaining Physical Exercise 133
system ought to have a larger impact on behavior than those that affect only one
component.
A major piece of supporting evidence for the use of CT in physical exercise pro-
motion comes from a meta-regression conducted by Michie and colleagues (2009).
The investigators were not able to identify a large number of intervention studies
that employed all components of CT (See Table 8.1); however, when CT compo-
nents were used in combination, they produced larger effect sizes than the largest
effect size produced by any one technique alone. This finding argues for the syn-
ergistic effect of the theoretically derived intervention components reviewed above.
In contrast (and perhaps surprisingly), Conn et al. (2008) reported that though the
use of any one CT component (i.e., behavioral strategy) was associated with larger
effect sizes than interventions that included none, the inclusion of multiple CT com-
ponents (e.g., contracting, feedback, goal setting, and self-monitoring) provided no
net increase in effect size.
Importance of self-monitoring. As we noted previously, it is important to consider
individual intervention strategies that appear to affect engagement in physical exer-
cise. Table 8.1 provides a list of Michie and colleagues’ (i.e., Abraham & Michie,
2008; Michie et al., 2009) taxonomy of intervention components from the two
theoretical frameworks reviewed above.
When considering the importance of individual program components, behavioral
interventionists should understand the importance of self-monitoring in physical
exercise interventions. Most interventions reviewed by Abraham and Michie ( 2008)
employed self-monitoring of physical exercise as a method of gathering assessment
data and providing feedback about goal attainment. Indeed, Michie and colleagues’
(2009) meta-analysis revealed self-monitoring to be the most important compo-
nent of behavioral interventions designed to produce changes in physical exercise.
Table 8.1 Taxonomy of
behavior change techniques
from Abraham and Michie
(2008) and Michie et al.
(2009)
Theoretical framework
Social cognitive theory Control theory
Provide information on
consequences
Prompt specific goal setting
Prompt intention formation Prompt review of behavioral
goals
Prompt barrier identification Prompt self-monitoring of
behavior
Provide general
encouragement
Provide feedback on
performance
Set graded tasks Prompt intention formation
Provide instruction
Model or demonstrate the
behavior
Note: For a review of health behavior change studies using each
of the intervention components listed above, see Michie et al.
(2009).
134 C.C. Cushing and R.G. Steele
Further, in their meta-analysis of exercise promotion programs for adults with
chronic illness, Conn et al. (2008) reported that interventions that included self-
monitoring produced significantly greater effect sizes than interventions that did not
include self-monitoring.
Self-monitoring can include objective data such as a pedometer, which gathers
step counts to be recorded in a log and returned to an interventionist. These protocols
are remarkably effective at increasing physical exercise. A recent systematic review
by Bravata et al. (2007) revealed that participants in randomized controlled trials
who used a pedometer significantly increased their physical exercise by 2,491 steps
more than control participants; participants in single-group observational studies
increased their step count by 2,183 steps per day. An important consideration is that
the i nclusion of a step goal (e.g., 10,000 steps per day) was a significant predictor of
step count, which is also consistent with CT. The evidence is clear, when attempting
to modulate physical exercise, there are remarkably sound theoretical and empirical
grounds for the inclusion of self-monitoring as an intervention component.
Typical Intervention Delivery Mechanisms
Primary care. Visits to primary care physicians are obvious opportunities to address
physical exercise with individuals in need of lifestyle change. Physicians themselves
have demonstrated that they are willing and able to participate in brief (<10 min)
consultations to promote physical exercise in their patients (Pinto, Goldstein,
DePue, & Milan, 1998). However, time constraints in primary care offices leave
physicians with barely enough time to make brief verbal and written recommen-
dations, which are largely ineffective at changing physical exercise behaviors (e.g.,
Hillsdon, Foster, & Thorogood, 2005; Lawlor & Hanratty, 2001).
Nevertheless, some examples do exist that show an additive effect of physi-
cian advice, behavioral counseling, and self-monitoring for improving adherence
to physical exercise recommendations. For example, Armit and colleagues (2009)
recruited a sedentary sample of 50–70-year-old patients presenting to a primary
care physician’s office. Participants were assigned to receive a brief recommenda-
tion from a physician, a brief physician consultation plus a behavioral counseling
session with an exercise physiologist, or a brief consultation plus behavioral coun-
seling session and a pedometer for self-monitoring. All participants demonstrated
improvements in physical exercise, but the group receiving all three intervention
components demonstrated better physical fitness and greater adherence to physi-
cal activity recommendations. Therefore, direct recommendations from a physician
may be part of helping individuals increase physical exercise, while the ideal
primary care intervention will include additional behavioral counseling components.
Future work is needed to help ensure appropriate allocation of resources to indi-
vidual patients. Baseline patient characteristics such as social support, self-efficacy,
and barriers to physical activity mediate the effectiveness of brief behavioral
counseling sessions targeting physical exercise (Steptoe, Rink, & Kerry, 2000).
Therefore, it is possible that relatively low-effort inexpensive interventions may
8 Establishing and Maintaining Physical Exercise 135
work for a large portion of the population with good social support, high self-
efficacy, and low barriers to physical activity. In these cases, physicians may be able
to provide a recommendation for exercise, a low-cost pedometer, and materials for
self-management. Before such a program can be effective, however, more emphasis
will need to be placed on the assessment of psychosocial variables during primary
care visits.
Community-based programs. Compared to primary care interventions,
community-based physical activity interventions are more well established
and better researched. In fact, the evidence base for such programs is so large
that a recent cost-effectiveness computer simulation study concluded that seven
types of community-based interventions are appropriate for broad dissemination to
promote physical activity (Roux et al., 2008). Of the programs that intervened at
the individual level (Kriska et al., 1986; Lombard, Lombard, & Winett, 1995), both
programs used components of SCT and CT to produce behavior change: program
activities were tailored to individual’s preferences, interests, or readiness to change;
participants were taught specific skills to help improve self-efficacy for exercise
activities; and assistance was provided to help participants build social support
for healthy behaviors. These characteristics are noteworthy in their similarity to
the model evaluated by Anderson and colleagues (2006) above. In addition to the
SCT components, individuals were assisted in setting behavioral goals for physical
exercise and taught to self-monitor their goal attainment. Beyond SCT and CT,
participants were also taught specific operant conditioning skills for self-reward
and reinforcement of positive health behaviors.
School-based interventions. Not surprisingly, theories of physical activity pro-
motion that are effective in adults are also useful in school-age children and
adolescents. Principles of SCT and CT are observed in interventions for school-
age children; however, less time is spent identifying barriers to physical exercise,
and more structure is placed around opportunities for exercise. For example,
in a 2-year trial of the Sports, Play, and Active Recreation for Kids (SPARK)
program, Sallis et al. (1997) delivered health-related curriculum combined with self-
management strategies such as self-monitoring, goal setting, stimulus control, and
self-reinforcement to fourth- and fifth-grade students. Ultimately, the intervention
was successful in increasing physical exercise during school, but not during leisure
time. This finding is consistent with the larger literature on school physical exercise
promotion programs (Dobbins, De Corby, Robeson, Husson, & Tirilis, 2009). The
SPARK program was limited to the school setting; however, programs that have
included a community-based component in addition to the school curriculum have
also demonstrated poor adoption of physical exercise outside the classroom (Nader
et al., 1996).
Future work to increase the impact of school-based interventions should con-
sider a socioecological model that includes family involvement. Within the pediatric
overweight literature, a consistent predictor of child and adolescent weight loss is
parental weight loss (Hunter, Steele, & Steele, 2008; Sato et al., 2011). Such find-
ings indicate that the environmental contingencies at work in children’s homes are as
important as those in their schools when helping children adopt healthy behaviors.
136 C.C. Cushing and R.G. Steele
Dramatic improvements in physical exercise are possible using CT interventions
when parents are put in control of making television watching contingent on the
performance of physical activity (Roemmich, Gurgol, & Epstein, 2004). In addition,
recent work has demonstrated that the natural decline in physical activity observed
in adolescence is accelerated in eighth grade when family support for physical activ-
ity is low (Dowda, Dishman, Pfeiffer, & Pate, 2007). Taken together, these findings
indicate that parents should be targeted for direct intervention regarding their own
health behavior as well as their management of their child’s behavior.
Innovative delivery mechanisms. With the success of fact-to-face interventions
for physical exercise, behavior change researchers have begun to look to technol-
ogy as the next wave of intervention delivery for physical exercise interventions.
Within the broad health behavior change literature, it is known that technologically
driven interventions are best when they are modeled after what works in face-to-
face interventions, are more interactive, and use theoretically meaningful behavior
change principles such as goal setting, immediate feedback, self-monitoring, and
barriers identification (Cushing & Steele, 2010; Hurling, Fairley, & Dias, 2006;
Ritterband et al., 2003). Specific work in the area of physical exercise is relatively
limited but appears to hold promise for technologically delivered interventions (e.g.,
van den Berg, Schoones, & Vilet Vieland, 2007). A recent review of technolog-
ically based interventions for general health behavior revealed that theoretically
based interventions produce larger effects than atheoretical interventions (Webb,
Joseph, Yardley, & Michie, 2010). The results of the review also indicate that many
of the theoretically consistent strategies identified above are effective in modify-
ing behavior such as modeling, goal setting, prompt feedback, barrier identification
and amelioration, plan for developing social support, self-monitoring, feedback on
performance, and education about consequences of behavior.
One successful technologically delivered intervention utilized a totally auto-
mated Internet, email, and mobile phone system to increase physical exercise
(Hurling et al., 2008). In this study, Hurling and colleagues collected baseline infor-
mation using a Web-based system to conduct an assessment of barriers to physical
exercise and make a plan for participating in physical exercise. The 9-week program
was designed to provide both normative and ipsative feedback on physical exercise
behavior and employed some cognitive strategies for modifying beliefs about barri-
ers to physical activity. At the end of the trial, participants receiving the intervention
demonstrated a significantly greater increase in physical exercise than the control
group, and consequently lost more body fat.
Conclusions and Future Directions
Drawing from the above review of the literature on SCT and CT as it relates to
the promotion and maintenance of physical exercise, the following conclusions and
recommendations appear warranted. When working with typical individuals (i.e.,
individuals not engaged in elite athletic competition), the interventionist is encour-
aged to consider family or peer support for exercise behaviors. Consistent with SCT,
8 Establishing and Maintaining Physical Exercise 137
the best practice is to create an expectation that support is available to help the indi-
vidual attain reasonable goals. Consistent with Anderson et al.’s (2006) findings, a
“team” approach may facilitate self-efficacy and self-regulation. Further, inclusion
of family and/or peers may increase opportunities for modeling of healthy behaviors
and social reinforcement of approximations to health-related goals.
Relatedly, the literature suggests that the successful behavior therapist will assess
an individual’s self-efficacy for exercise and the appropriateness of her/his exercise
goals. In individuals with particularly low self-efficacy for physical exercise, the
behavior therapist should pay particular attention to ensure t hat the client has set
easily reached proximal goals. This will serve to build self-efficacy and set the stage
for self-regulatory interventions, both of which have been shown to be related to
successful outcomes in terms of exercise promotion.
As detailed above, both SCT and CT underscore the importance of self-
monitoring. Depending on the individual goals, self-monitoring can involve the
use of a pedometer, heart-rate monitor, or exercise log. The individual should be
instructed to review self-monitoring promptly and to either seek corrective feedback
from the behavior therapist in session or to use the self-monitoring record to adjust
behavior themselves. The behavior therapist should help the individual identify and
remove barriers to exercise. Depending on the individual’s level of preparedness for
exercise, it may be necessary to provide education, encouragement, and modeling
of basic exercise behaviors.
With regard to the adoption of technologically based interventions, we encourage
the behavior therapist to hone their skill in applying Abraham and Michie (2008)
behavior change taxonomy to commercially available products. For example, sites
such as www.livestrong.com help individuals set goals, self-monitor, receive social
support, and provide feedback on performance. A recent multiple-baseline study
examining the utility of self-monitoring on an iPod Touch
TM
demonstrated marked
and sustained improvement in self-monitoring compliance among three overweight
adolescent girls, providing evidence that commercially available products can serve
as a helpful adjunct to treatment (Cushing, Jensen, & Steele, 2011).
With regard to the research literature, we argue that research on exercise pro-
motion has reached a point that developing new theories f or behavior change has
relatively limited utility. As suggested by the similarities in the two theoretical mod-
els presented here (as well as the similarity of these models to others not reviewed),
there appear to be a set of core principles that can be successfully applied to exercise
promotion. In fact, most intervention programs appear to integrate effective aspects
of several theories (see Conn et al., 2008; Michie et al., 2009).
Rather, instead of generating new theories to explain the promotion and mainte-
nance of physical exercise, we argue for further elaboration of the conditions and
contexts under which known efficacious principles can be effectively implemented
(see Glasgow, Lichtenstein, & Marcus, 2003; Steele, Mize-Nelson, & Nelson,
2008). As eloquently explained by Glasgow and colleagues, efficacy studies pro-
vide evidence that an intervention can be beneficial under “ideal” circumstances,
whereas effectiveness studies provide evidence that the intervention is beneficial
when implemented under “real world” (and usually considerably more difficult)
138 C.C. Cushing and R.G. Steele
conditions. Our read of the literature suggests that the field has much to offer in
terms of efficacy (i.e., “what can work”), but somewhat less depth in terms of what
“does work” when scaled at the community level.
To this end, Abraham and Michie’s (2008) taxonomy for categorizing interven-
tion components may provide a conceptual framework for further studies aimed at
“scaling the science up” to programs that can impact larger numbers of individu-
als. In addition to this conceptual framework, Glasgow, Vogt, and Boles’s (1999)
RE-AIM dimensions for program evaluation provide a structural framework for
understanding the reach, adoption, implementation, and maintenance of health pro-
motion interventions and programs in terms of both efficacy and effectiveness.
A great deal of the retrospective work in the area of exercise promotion is reviewed
in this chapter. However, prospective component analyses of behavior change com-
ponents will be necessary to help s treamline interventions, and methodologically
complex studies may be necessary to fully explore “What components work, for
whom, and under what circumstances?” (Elkin, Roberts, & Steele, 2008).
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Chapter 9
Behavioral Momentum in Sports
Henry S. Roane
Introduction
The property of momentum is one of the most fundamental principles of physics.
The basis of physical momentum is Newton’s second law of motion, which states
that the change in the movement of an object is inversely related to its mass. Thus,
momentum describes the relationship between the velocity of an object and the
mass of that object. This relationship is expressed as p = mv, where momentum
(p) is a product of the mass (m) and velocity (v) of an object. Thus, the greater
physical mass or velocity an object has, the greater is its momentum. Conversely,
the greater an object’s momentum, the more opposing force required to alter the
object’s momentum.
The property of momentum applies to multiple dimensions of the physical world,
including sports performance. It is common for athletic competition to involve phys-
ical momentum that is altered by s ome alternative physical event. To illustrate, a
running back weighing 93 kg running at a speed of 8 m/s would have momentum
of 744 kg m/s. If the running back’s forward progress is disrupted by an external
event (e.g., a linebacker), his velocity would decrease, thus decreasing his momen-
tum. Likewise, the amount of force applied by the opposing player (e.g., a 143-kg
defensive lineman or an 86-kg cornerback) to the running back will affect the degree
to which the running back’s momentum is disrupted. Alternatively, a player who is
smaller than the r unning back (e.g., a wide receiver) might have less momentum
and, consequently, might be differentially affected when hit by a defensive lineman
or cornerback.
All sports, whether they are considered “contact” sports (e.g., hockey, rugby)
or “non-contact” sports (e.g., tennis, baseball), involve manipulations of physi-
cal momentum in some form. For example, in American football, a particularly
hard tackle might be affected by momentum (e.g., a fast-moving linebacker hits a
H.S. Roane (B)
Department of Pediatrics and Psychiatry, SUNY Upstate Medical University, Syracuse,
NY 13210, USA
e-mail: [email protected]
143
J.K. Luiselli, D.D. Reed (eds.), Behavioral Sport Psychology,
DOI 10.1007/978-1-4614-0070-7_9,
C
Springer Science+Business Media, LLC 2011
144 H.S. Roane
quarterback who is slowing down while running out of bounds). Likewise, in racing,
the momentum of one car coming out of a turn might be greater than that of a com-
petitor, thus allowing the first car to overtake the second one (e.g., “Schumacher had
the momentum going into Turn 1, which allowed him to get by Button,” SpeedTV
coverage of the Spanish Grand Prix, May 9, 2010). In both cases, the term momen-
tum is correctly applied in that the objects in question (a car or the linebacker)
have physical attributes that are measurable. Understanding the impact of physical
momentum might affect the development and enforcement of rules regarding safety
in sports. For example, in baseball a metal bat is usually lighter than a wooden bat.
Consequently, a swing with a metal bat is likely to generate more velocity than one
with a wooden bat, resulting in the batter hitting the ball harder, which might be ben-
eficial in gameplay (e.g., more likely to hit a homerun). However, harder hit balls
might also increase the likelihood of more severe injuries, which has led to some
bans on the use of metal bats in baseball (Rivera, 2007).
Although the term momentum can be technically applied to some physical
aspects of sporting behavior, this is not the most common manner in which momen-
tum is discussed in sports. Rather, momentum is a term commonly used to describe
some aspect of psychological behavior in sports. Any sports fan is familiar hearing
about a player having momentum in a sporting contest (e.g., a shooter with a “hot
hand”) or a team gaining/losing momentum over the course of a season (Bresnahan,
2010). In addition, the term “momentum” is frequently applied to describe the
behavior of an individual or a team in sports. It is not uncommon to hear commen-
tary on a team carrying momentum into a coming season, an individual building on
the momentum established from a previous game, or, within the context of a game, a
team gaining or losing momentum (Deford, 1999). Even some popular sports video
games have a measure of momentum to indicate which team is more likely to play
better throughout a contest. As noted by Mace, Lalli, Shea, and Nevin (1992), this
form of momentum is based on the notion that “success breeds success.” Conversely,
there is the implication that playing poorly will lead to additional poor play (i.e., the
wheels coming off), which might be interpreted as “losing” momentum. In these
descriptions, the term momentum is applied as a metaphor. That is, the behavior
described (e.g., winning several games at the end of the season) has no true mass
or velocity per se; rather the term is used to describe what might be considered a
psychological form of momentum.
Psychological Momentum in Sports
The term momentum is frequently applied to describe individual or team sports per-
formance. And, unsurprisingly, there is literature in the field of sports psychology
that discusses this type of momentum. Within athletic competition, psychological
momentum has been described in the simplest form as winning the first match or
scoring first (e.g., Weinberg, Richardson, & Jackson, 1981). More complex defini-
tions of psychological momentum are multidimensional and incorporate a variety
of psychological constructs. For example, Iso-Ahola and Mobily (1980) described
9 Behavioral Momentum in Sports 145
momentum as changes in the perceptions of events that occurred early in a given
competition and the impact of those events on subsequent gameplay (defined as cog-
nitive and physiological changes in behavior that are associated with better or worse
success later in the game). Taylor and Demick (1994) described a model of psy-
chological momentum in sports in which momentum is accounted for by changes
in precipitating events (i.e., events in the course of a game that might be deemed
positive or negative by the players), cognition/affect (e.g., reports of how likely a
player/team was to make another goal, shot, etc.), and changes in behavioral persis-
tence and performance (e.g., altering shot selection), which result in changes in the
behavior of the target individual or team and the opponent.
As might be expected from the varying ways in which momentum has been
conceptualized and quantified, research in sports psychology has demonstrated
wide-ranging results about the extent to which certain variables affect momentum.
Gayton, Very, and Hearns (1993) examined momentum (defined as scoring first or
“winning” the first period of play) within professional hockey. These results found
that scoring first or winning the first period was associated with winning the game in
66.5% and 72.5% of cases, respectively. These results are, of course, correlational,
as many other variables can affect the outcome of a game; yet the results of Gayton
et al. are similar to other results involving individual sports performance (e.g., scor-
ing first in a tennis match increases the probability of winning that match; Silva,
Hardy, & Grace, 1988).
Regarding more complex models of psychological momentum, Taylor and
Demick (1994) found that individuals who won a tennis match were more likely
to have experienced a preponderance of positive precipitating events than nega-
tive precipitating events (with the inverse being true for losers of the match). In a
follow-up investigation, Mack and Stephens (2000) used a basketball shooting task
to evaluate psychological momentum. These results showed that poorer momentum
(defined as making or missing one of three shots) was associated with changes in
cognition (i.e., lower self-efficacy and affect ratings). However, poorer momentum
was not clearly correlated with response persistence, a finding that was somewhat
contradictory with previous research.
In sum, the field of s ports psychology has presented a range of conceptual mod-
els for approaching an analysis of momentum in sports. Collectively, the results of
previous investigations afford the following conclusions: (a) scoring first is better
in terms of an increased likelihood of winning a game, (b) experiencing events
that are more favorable is associated with a greater probability of winning, and
(c) good performance is associated with reports of better psychological function
regarding gameplay. Although this research may offer insights into sports perfor-
mance, methods of consultation, and coaching strategies, the types of procedures
used in the studies noted above are uncommon within the context of behavior
analysis. Two of the defining characteristics of behavior analysis are a focus on
observable events (rather than self-report of psychological constructs) and a link
between the behavior being observed and some well-researched conceptual model
of behavior (Baer, Wolf, & Risley, 1968). Consequently, behavior analytic exami-
nations of momentum in sports have involved direct observation and quantification
146 H.S. Roane
of sports-related behavior and linking this behavior to a broader conceptualization
of response persistence. Prior to discussing previous behavior analytic research on
momentum in sports, it i s important to introduce the theoretical underpinning of that
research.
Momentum and Behavior Analysis
Within the field of behavior analysis, the property of momentum has been applied
metaphorically to describe the behavior of various organisms under differing con-
ditions. Although this description of momentum is not technically accurate (e.g.,
behavior has no specific mass), the metaphor of “behavioral momentum” is used to
describe the relationship between response rate and resistance to behavior change
when certain “disrupter” events occur (Nevin, 1996). Within the framework of
behavioral momentum, the velocity of a response is analogous to the rate of
reinforcement. Mass is conceptualized as the persistence of behavior over time
following at least one event change (e.g., no longer providing reinforcement for
responding). Thus, behavioral momentum is a two-component variable that includes
the ongoing rate of the response and the resistance of change in response rate
when responding is disrupted by programmed (or unprogrammed) variables (Nevin,
Mandell, & Atak, 1983). Though an in-depth discussion of behavioral momentum
is beyond the scope of this chapter, a brief overview of the procedures and general
findings of this conceptualization of behavior is necessary to understand the role of
behavioral momentum in sports performance.
In basic laboratory research, behavioral momentum is commonly evaluated
in a multiple-schedule paradigm. In a multiple schedule, there are at least two
distinct behavioral contingencies presented, each of which is associated with a
unique discriminative stimulus. The combination of the contingency and its respec-
tive discriminative stimulus is referred to as a component. To evaluate behavioral
momentum, the researcher first establishes stabile patterns of responding in one
component. Next, that component is removed and the second component (differ-
ent contingency and different discriminative stimulus) is introduced. The second
component might involve reinforcing the behavior less frequently or not at all (i.e.,
extinction). The variable of interest is how long responding persists when the con-
tingencies change from the first component to the second (e.g., responses that persist
longer in the second component are deemed to have greater momentum).
Using an approach similar to that described above, Nevin, Tota, Torquato, and
Shull (1990) evaluated resistance to change in a laboratory environment using
pigeons as subjects. The birds were trained to peck colored light panels, each of
which was associated with a different rate of reinforcement (e.g., 15 reinforcers
per hour). In one condition, responding on the left panel produced reinforcement
at a rate of 45 per hour and responding on the right panel resulted in 15 rein-
forcers per hour. In two other conditions, responding on the left panel resulted
in no reinforcement, whereas responding on the right panel resulted in relatively
low (15 reinforcers per hour) or relatively high (60 reinforcers per hour) rates
of reinforcement. Once stabile responding was achieved in these conditions, this
9 Behavioral Momentum in Sports 147
responding was disrupted either by giving the pigeons extra food (satiation) or by
presenting a situation in which responding on the panels no longer produced rein-
forcement (extinction). Resistance to behavioral change was assessed by comparing
responding under each of these conditions to behavior under the disrupter context
for extended period of time (e.g., 4 h). Each condition produced a different slope
of responding in relation to how much behavior persisted after exposure to the dis-
ruptor. Responding under some conditions (left panel responding for 15 reinforcers
per hour) decreased more quickly than under other contexts (e.g., when left panel
responding resulted in 15 reinforcers per hour and right panel responding resulted
in 45 reinforcers per hour). These results showed that resistance to change varied as
a function of the context in which initial responding was trained (i.e., responding in
the different contexts varied in terms of behavioral momentum).
The roots of behavioral momentum lie within the experimental analysis of behav-
ior. However, this general principle has been applied frequently to issues of clinical
concern. Initially, this application consisted of procedures designed to increase
client compliance with difficult-to-complete instructions. For example, Mace et al.
(1988) applied Nevin’s model of behavioral momentum to decrease noncompliance
with instructions for four individuals with developmental disabilities. Participants
were identified to be noncompliant with specific tasks (e.g., cleaning the bathroom).
These tasks were deemed low-probability tasks in reference to the participants’
uncommon rate of completion. By contrast, a series a “high probability” tasks
were identified for each participant (e.g., prompting the participants to give a high
five). In general, noncompliance with low-probability requests was high when these
tasks were presented sequentially. However, when these same tasks were embedded
within a sequence of high-probability tasks (e.g., four high-probability instructions
preceded a low-probability instruction), noncompliance with the low-probability
tasks decreased significantly. From the perspective of behavioral momentum, the
higher rate of reinforcement obtained with the high-probability instructions pro-
duced a response sequence that was more resistant to change when a disrupter event
(i.e., a low-probability instruction) was presented. Said another way, the momen-
tum of compliance with the high-probability requests led to greater compliance
with low-probability requests. Since Mace’s initial application of the behavioral
momentum metaphor to the treatment of problematic behavior, the efficacy of the
high-probability instructional sequence has been replicated repeatedly in various
response-acquisition programs (e.g., Belfiore, Lee, Vargas, & Skinner, 1997) and in
the treatment of problematic behavior disorders (e.g., Dawson et al., 2003; Zarcone,
Iwata, Mazaleski, & Smith, 1994). In addition, the effects of response persistence
have recently been evaluated within the context of treatments for problematic behav-
ior displayed by individuals with developmental disabilities (Mace et al., 2010).
Previous Research on Behavioral Momentum and Sports
Although there are likely sport-specific variables that affect an individual’s or a
team’s momentum, some researchers have catalogued events that seem to influence
an observer’s interpretation of momentum. Burke, Burke, and Joyner (1999) had a
148 H.S. Roane
seasoned (former player and coach) basketball observer watch 14 basketball games
(11 college and 3 high school) and report when a team started to show “momentum.”
The observer then indicated the events that preceded the team’s momentum (e.g., a
dunk, causing a turnover, a string of unanswered points, crowd noise), events that
occurred during momentum (e.g., steals, blocked shots, dunks, 3-point goals), and
events that ended the momentum (e.g., time-out, steal, turnover). The latter category
was scored for both the team that possessed momentum and their opponent. Across
50 observed momentum events, the most common momentum started was good
play by one team and poor play by the other. Specific to events that started momen-
tum, 3-point shots, defensive stops, and steals were the most often cited events.
Within a momentum run, the primary observed events were turnovers (favoring the
momentum team), increased crowd noise, defensive stops, and steals. Finally, the
events that were most often associated with the end of momentum were a turnover
by the momentum team, missed shots by the momentum team, and time-outs called
by the opponent. As might be expected, the momentum team was found to outscore
the opponent by a margin of 7.58 to 2.62 during the momentum interval.
The descriptive results of Burke et al. (1999) are interesting because they con-
tribute to a number of events that could be categorized as potential reinforcers or, in
the case of momentum-ending events, disruptors. As noted in the previous section,
responses that result in a high rate of reinforcement are more likely to be resistant
to a disruptor event. That is, those r esponses may be said to have greater behav-
ioral momentum. In other words, as an individual’s behavior results in more and
more reinforcer delivery, the responding of that behavior should be more likely to
persist (i.e., greater momentum) than the behavior of a responder who contacts less
reinforcement. As noted previously, this procedure has been the focus of a number
of investigations in both experimental and applied research (see Nevin, 1996,fora
review). In addition, the generality of the behavioral momentum metaphor has also
been examined within the context of sport performance (albeit in a relatively small
set of investigations).
Mace et al. (1992) applied the concept of behavioral momentum to men’s col-
lege basketball. The premise of their research was similar to that of others who have
examined the notion of momentum in sports. However, rather than evaluating psy-
chological interpretations of momentum, Mace et al. conceptualized momentum in
relation to the property of momentum in physics. Specifically, good gameplay was
conceptualized as “velocity” (i.e., response rate), and continued good game perfor-
mance when faced with some adverse event (e.g., a turnover favoring the opponent)
was equated with “mass.” Using a sample of college basketball games, Mace et al.
sought to address the performance within the framework of behavioral momentum
along two dimensions: (a) whether a team would perform better after an adverse
event if their pre-event scoring rate was relatively high or low and (b) whether a
team’s scoring rate would persist following a period of time-out.
Mace et al. (1992) examined the responding of 14 teams across seven college
tournament basketball games (although two teams were dropped from data analy-
sis given atypical distributions of local reinforcement rates; described below). Data
were collected on three classes of events: (a) reinforcers obtained by the target team
9 Behavioral Momentum in Sports 149
(i.e., 3-point goals, 2-point goals, 1-point foul shots, steals/turnovers favoring the
target team), (b) adversities encountered by the target team (i.e., turnovers favoring
the opponent team, missed field goals or free throws, committing a shooting foul),
and (c) responses to those adversities (i.e., a reinforcer or adversity that occurred
during the first possession following an adversity). Responses to adversities were
categorized as favorable or unfavorable depending on the nature of the target team’s
first response following an adversity (i.e., whether they encountered a reinforcer or
another adversity).
Using these data, Mace et al. (1992) calculated an overall rate of reinforcement
for the entire sample. These results indicated that, on average, reinforcers occurred
once per minute. Next, the data collected by Mace et al. were examined to address
the two principal research questions. To determine if a team’s response to an adver-
sity was associated with their rate of reinforcement before that adversity, the authors
calculated the number of reinforcers that occurred during each 3-min interval that
preceded an adversity (i.e., a local rate of reinforcement). These local reinforcement
rates were t hen grouped into three categories: relatively low (0 or 0.33), medium
(0.67 or 1.0), and high (1.33 or better). Following computation of the local rein-
forcement rates, the data on each team’s response to a given adversity was assessed
by examining the percentage of those adversities that were responded to favorably
relative to the local rate of reinforcement. Mace et al. noted a positive correlation
between a team’s local rate of reinforcement and their having a positive response to
an adversity. To illustrate, teams with a low local rate of reinforcement (0 or 0.33)
responded favorably to an adversity 44.1% of the time, whereas teams with high
local rates of reinforcement (1.33 or more) responded favorably 68% of the time.
Overall, the likelihood of a favorable response to an adversity increased as the local
rate of reinforcement increased for 67% of the teams in the analysis.
To address the second research question, how a team’s performance would be
affected by an opponent calling a time-out, Mace et al. (1992) calculated a ratio of
the target team’s and their opponent’s local rates of reinforcement. The presentation
of these ratios permitted a comparison of the two team’s reinforcement rates at any
given point in the game. The authors then evaluated these ratios for each 3-min
interval that preceded a time-out, each 3-min interval that followed a time-out, and
the overall course of the game (independent of 3-min intervals before time-outs).
In general, teams were more likely to call a time-out when their opponent’s rate
of reinforcement was 2.63 times greater than their own. Interestingly, the results
showed that the average reinforcement ratio dropped from 2.63 before the time-out
to 1.11 following the time-out. This outcome clearly suggests that calling a time-out
was an effective method of disrupting a team’s rate of reinforcement. Said another
way, calling a time-out was an effective method of altering a team’s momentum.
In a follow-up investigation, Roane, Kelley, Trosclair, and Hauer (2004) exam-
ined the generality of the Mace et al. (1992) findings by examining the influence
of behavioral momentum with women’s college basketball. The rationale for the
Roane et al. investigation was that women’s college basketball was associated with
unique differences relative to men’s basketball. These variables could have affected
gameplay and, correspondingly, relative rate of reinforcement. To illustrate, Roane
150 H.S. Roane
et al. noted that women’s basketball was associated with different rules regarding
ball advancement and the shot clock (5 s shorter in women’s basketball at the time
of the investigation). In effect, these variables could alter the “pace” of a game.
Likewise, women’s basketball often differs from men’s basketball in terms of the
style of play (e.g., more perimeter play in women’s basketball) and gender differ-
ences for certain basketball-related abilities (Smith, 2002). Again, these variables
could have resulted in alterations in reinforcement obtainment (e.g., outside shots
are generally less accurate than shots made in the post). The authors postulated
that these two variables, differences in rules and style of play, could have altered
obtained rates of reinforcement, which might have made women’s basketball less
resistant to the influence of disruptor events.
Roane et al. (2004) used data collection procedures and operational definitions
that were similar to those developed by Mace et al. (1992). Specifically, data were
collected on three class of events for each of the 12 teams (i.e., each game was
watched twice, and each team was recorded as the target team in the separate view-
ings). The events included reinforcers obtained by the target team (i.e., 3-point
goals, 2-point goals, 1-point foul shots, steals/turnovers favoring the target team),
adversities encountered by the target team (i.e., turnovers favoring the opponent
team, missed field goals or free throws, committing a shooting foul), and responses
to those adversities (i.e., a reinforcer or adversity that occurred during the first pos-
session following an adversity). Data were collected across a series of six college
basketball games during a national women’s championship tournament.
Results of the Roane et al. (2004) analysis were examined using the same data
analysis procedures described by Mace et al. (1992). First, an overall rate of rein-
forcement was calculated for each team by dividing the number of reinforcers
obtained by the length of the game (i.e., total game time consisted of all play
time and time-out periods but did not include the halftime duration). As expected,
Roane et al. observed a lower overall rate of reinforcement for women’s basketball
(0.67 reinforcers per min) than that observed by Mace et al. for men’s basketball
(1.0 reinforcers per min). Next, local rates of reinforcement were examined to eval-
uate the extent to which a team’s local rate of reinforcement would affect that team’s
response to an adversity. This measure was calculated by counting the number of
reinforcers that occurred in a 4.5-min period prior to an adversity (Note: Roane
et al. used a 4.5-min interval, rather than a 3-min interval used by Mace et al., to
hold constant an average of three reinforcers before an adversity; recall that the
average rate of reinforcement was lower in the Roane et al. sample than in the Mace
et al. sample). The local rates of reinforcement were then grouped into categories
that characterized generally poor performance (reinforcement rate of 0–0.44), better
performance (reinforcement rate of 0.67–1.11), and good performance (reinforce-
ment rate of 1.33 or greater). These rates were then examined as to each team’s
responses to adversities given their local reinforcement rate. Across teams, Roane
et al. found a general relation between local rate of reinforcement and favorable
responses to adversities. For example, at a low level of performance (0 or 0.44 rein-
forcers per min), 37% of adversities were responded to favorably; this increased to
49% for good levels of performance (1.33 or greater). This outcome was similar to
9 Behavioral Momentum in Sports 151
the positive correlation observed by Mace et al., though Roane et al. found no such
differences in response to adversities when unweighted means were used to analyze
the results. On a team-by-team basis, the results of Roane et al. also differed slightly
from those of Mace et al. Specifically, Roane et al. found that responses to adversi-
ties increased as a function of rate of reinforcement for the minority of teams (4 of
12) in their sample, whereas this effect was noted for more teams (8 of 12) in the
Mace et al. investigation.
By contrast, the results of the Mace et al. (1992) and Roane et al. (2004) investi-
gations yielded similar outcomes regarding the extent to which a time-out called by
the opponent effectively functioned as a disruptor event. Both studies found that a
target team calling a time-out was an effective disruptor event in terms of decreas-
ing the opponent’s reinforcement ratio. Specifically, Roane et al. found that calling
a time-out decreased average reinforcement ratios from 2.35 before the time-out to
0.64 after the time-out (similar to the respective 2.63 and 1.11 reinforcement ratios
noted by Mace et al. under the same contexts).
Although somewhat disparate results were found in aspects of the Mace et al.
(1992) and Roane et al. (2004) investigations, the general conclusion suggests
that sports behavior is amenable to the application of the behavioral momentum
metaphor. In light of the differences found regarding i ndividual team’s responses to
adversities, one might argue that the Roane et al. results actually strengthen the con-
clusions drawn by Mace et al. That is, Roane et al. noted lower overall reinforcement
rates (0.67 reinforcers per min) than Mace et al. (1.0 reinforcers per min) and also
found less favorable responses to adversities for the women’s sample relative to the
men’s sample. These data support the use of the behavioral momentum metaphor
in that resistance to change is dependent upon the rate of reinforcement. Thus, it is
consistent with the findings of previous research on behavioral momentum that the
overall lower rate of reinforcement in women’s basketball would be associated with
a general less favorable response to adversities.
Extension of the Behavioral Momentum Metaphor
to Other Sports
The combined results of Mace et al. (1992) and Roane et al. (2004) suggest that
the behavioral momentum metaphor can be applied to the sports performance.
The impact of behavioral momentum can be felt upon the performance of a team
and (perhaps) that of individual athletes. Also, there are two potential avenues of
ongoing research regarding the application of behavioral momentum to sports per-
formance. The first involves examining the generality of the Mace et al. and Roane
et al. results to sports other than college basketball. The second involves extend-
ing the momentum metaphor as an intervention to improve sports performance.
This section will briefly discuss the potential use of data derived from momentum
analysis and sports and potential areas of future investigation.
The results of Mace et al. (1992) and Roane et al. (2004) suggest that calling a
time-out is an effective strategy for decreasing an opponent’s rate of reinforcement.
152 H.S. Roane
Having said that, both studies found that certain teams were more effective at being
able to determine when it was best to call a time-out. Mace et al. noted that one
team in their sample (Illinois) called a time-out at a much less disparate reinforce-
ment ratio than did another team (Michigan). Similar individual team differences
were noted in the sample obtained by Roane et al. This could be because coaches
tend to base their decisions for calling a time-out on factors other than relative rein-
forcement rates (Duke & Corlett, 1992). However, these combined results suggest
that coaches should pay greater attention to ongoing reinforcement ratios for their
team and for individual players such that coaches can make empirically based deci-
sions about when it is best to call a time-out. It is not uncommon for a college
basketball team to have approximately five assistants on a bench during a given
game. One of these assistants could be charged with calculating ongoing rates for
this purpose such that coaches make decisions regarding the use of time-out on more
quantitative data.
Rates of reinforcement during basketball games could also be assessed on an
individual-player basis. Such information might be useful for determining offen-
sive strategy (i.e., increasing the probability of getting the ball to a player with
a higher rate of reinforcement in a given timeframe) or defensive strategy ( e.g.,
double-teaming such a player). Anecdotally, many sports observers would likely
agree that teams try to give the ball to a player who is having a “hot streak,” though
the notion of getting “hot” in basketball has been challenged (Gilovich, Valone, &
Tversky, 1985). However, it appears that such decisions are primarily made on a
player’s scoring performance as opposed to his or her obtainment of other rein-
forcers (e.g., taking a charge, generating a steal). Mace et al. (1992) noted that
identifying “hot” players and targeting them accordingly might be an effective way
of affecting overall team performance. For example, if the “hot” player scores when
the ball is passed to him/her, this would also reinforce the player who passed the
ball or another player who set up a screen that would, in turn, affect the class of
responses that are generally thought of as good team play.
Basketball is unique among team sports. For example, in basketball there is a
fairly high rate of possession changes. Other sports such as hockey and soccer also
have a high rate of possession change; however, these sports are associated with
less obtainment of points (a reinforcer) than basketball. Such differences obviously
affect the overall rate of reinforcement that occurs in a game. When attempting
to apply the behavioral momentum metaphor to other sports, one must consider the
types of reinforcers and adversities encountered in those sports. For example, soccer
is associated with adversities (e.g., offsides) and reinforcers (e.g., corner kicks) that
are unique to that sport. A relative abundance of either event could affect overall
team performance even though the number of points obtained in a match is relatively
low. Managers already make attempts to address such events by “slowing down
the game” through shorter passes, more controlled ball handling, etc. However, it
would be of interest to see how such events affect team performance in soccer and
other sports.
A second line of investigation with regard to individual and team sports perfor-
mance would be to apply the methods used in behavioral momentum analyses to
9 Behavioral Momentum in Sports 153
tactics already employed by coaches and players. For example, a common strategy
in American football is to call a time-out just before an attempted field goal at the
close of a half (i.e., “icing” the kicker). In baseball, batters have a tendency to call
for a brief time-out or step out of the batter’s box as a pitcher nears his windup in an
apparent attempt to disrupt the pitcher’s performance. The effects of these events as
disruptors are unknown. Although these tactics seem to be work infrequently, their
persistence of use seems to suggest it might be effective intermittently. An anal-
ysis of momentum in such a situation might be assessed by comparing a kicker’s
rate of reinforcement in the presence or absence of such a disruptor (though this
analysis would be quite limited given the limited number of potential reinforcers a
kicker might obtain) or the degree to which the frequency of batter time-outs affects
a pitchers rate of reinforcement (e.g., throwing strikes, forcing a putout).
Coaching tactics to affect the isolated performance of a single player could also
impact the performance of a team. Using the tactic of icing a kicker as an example,
the impact of this tactic could have widespread effects beyond the accuracy of the
kick. For example, a missed kick could be considered a defensive reinforcer (for
the non-kicking team), which could reinforce other behavior in that same class of
responses. Alternatively, a successful kick could influence a class of offensive rein-
forcers. The relation between coaching tactics to isolate individual performance and
the impact of this on overall team obtained reinforcement (beyond points generated
or missed from the kick attempt) would be an interesting line of future investigation.
Many sports have designed defensive strategies specifically to combat an oppo-
nent’s offensive capabilities. Examples include penalty kill substitutions in hockey,
a defensive player “spying” on a specific offensive player in American football, or
substituting in a defensive specialist in basketball. Again, the effects of such proce-
dures seem to justify the continued use of such procedure. However, these changes
could each be examined as disrupter events and their effects quantified within the
context of the behavioral momentum metaphor (i.e., an examination of how such
events affect an opponent’s ongoing rate of reinforcement).
Finally, there are specific terms used to describe a player’s performance over
a period of time. It is not uncommon to hear a soccer player described as being
“in form” or a baseball player being described as “in the zone.” Both terms imply
that the player in question has a recent history of obtaining a relatively high rate of
reinforcement (e.g., more base hits, higher on-base percentage), and such behavior
seems similar to that of a player who is having a streak (e.g., goals scored, home-
runs hit). However, such descriptive labels have not been quantified. Examining
various rates of reinforcement for players would enable coaching staff to select
advantageous substitutions, team rosters, batting orders, etc. Alternatively, exam-
ining these data might indicate to an opponent how best to counter players whose
rate of reinforcement is relatively high.
Sports performance and coaching are behaviors that appear to be based less on
objective data and more on subjective notions of a specific strategy given the occur-
rence of certain events. Previous research (e.g., Mace et al., 1992; Roane et al.,
2004) suggests that examining within-game performance can lead to optimal play
calling (e.g., calling a time-out before two team’s reinforcement ratios become too
154 H.S. Roane
disparate). The quantification of sports performance via the conceptualization of
behavioral momentum holds promise for enhancing the performance of sports teams
and individuals competitors.
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Part IV
Special Topics
Chapter 10
Developing Fluent, Efficient, and Automatic
Repertoires of Athletic Performance
Brian K. Martens and Scott R. Collier
To master a form, it must be practiced 10,000 times.
– Unknown martial arts master
Some years back, the first author was conversing with a catcher for a minor league
baseball team and asked the naïve question, “So what do you think about when
deciding where to throw the ball to make a play?” The catcher immediately replied,
“There’s no time to think, I just react automatically. If you have to think, then
it’s already too late.” This statement captures the essence of much of the research
reviewed in this chapter. Namely, accomplished athletes in a variety of domains
(e.g., fast ball sports, team sports, martial arts) are able to execute complex chains
of behavior so accurately and quickly in response to changing situations that their
performance appears both effortless and automatic. In interactive sports, master ath-
letes seem at times to move in unison with their opponents as if in a coordinated
dance, rather than in response to the other’s actions (Ueshiba, 1987). In individual
sports such as golf, elite players are known for their ability to consistently execute
difficult shots under seemingly impossible conditions (e.g., Phil Mickelson’s 200+
yard shot from behind a tree on pine straw during the 2010 Masters Tournament that
landed only feet away from the 13th pin).
Despite domain-specific differences in skills, it is widely believed that a
lengthy period of deliberate practice is essential for developing expert performance
(Ericsson, Krampe, & Tesch-Romer, 1993; Ward, Hodges, Williams, & Starkes,
2004). Estimates of how long a period of time is required to reach elite or master
status in sports have been compared to the 10 year/10,000 h rule required for exper-
tise in other domains (e.g., chess and music; Ericsson et al., 1993; Simon & Chase,
1973). Although a good rule of thumb, the number of years required to reach elite
status in sports is generally more than 10 years, and accumulated practice hours are
usually less than 10,000 due to resource, motivational, and effort constraints inher-
ent in participating in sports over a long period of time (Baker, Cote, & Abernethy,
2003; Ericsson et al., 1993). For example, Baker et al. asked a sample of Australian
B.K. Martens (B)
Department of Psychology, Syracuse University, Syracuse, NY, USA
e-mail: bkmarten@syr.edu
159
J.K. Luiselli, D.D. Reed (eds.), Behavioral Sport Psychology,
DOI 10.1007/978-1-4614-0070-7_10,
C
Springer Science+Business Media, LLC 2011
160 B.K. Martens and S.R. Collier
national team players from three sports (netball, basketball, and field hockey) to
report on when they began participating in sports, how many hours per week they
practiced, and how long it took to reach the elite level (i.e., selection to the national
team). On average, the athletes reported 12.9 years of participation and 3,939 h
of accumulated practice prior to team selection. Using similar retrospective recall
methods, Helsen, Starkes, and Hodges (1998) examined the amount of time spent
in practice by Belgian international-, national-, and provincial-level soccer and field
hockey players. Both international- and national-level soccer players peaked in their
reported number of weekly practice hours (an indication of maturity in the sport)
15 years into their careers. By 18 years into their careers, international players had
accumulated an average of 9,332 practice hours. International field hockey play-
ers reached their peak of weekly practice 18 years into their careers, accumulating
a total of 10,237 h of practice time. At 12 years into their careers, international-
level wrestlers reported accumulating an average of 5,882 h of practice time (Ward
et al., 2004).
According to Ericsson et al. (1993), deliberate practice is not simply drill or
repetition but is a “highly structured activity, the explicit goal of which is to
improve performance” (p. 368). As such, deliberate practice is typically individ-
ualized and involves periodic instruction by a coach; increasingly more difficult,
domain-relevant training tasks; close monitoring of performance (by both the ath-
lete and the coach); feedback and reinforcement; and re-teaching of poorly executed
skills. Another defining feature of deliberate practice is that it requires sustained
effort and attention and therefore can be engaged in only for relatively brief inter-
vals (i.e., 1–2 h at a time). In sports, the duration of practice sessions is constrained
even further by muscle fatigue and the risk of injury. Therefore, athletes who are
committed to achieving expert levels of performance typically adhere to a practice
schedule involving one or two sessions a day, every day, for 10+ years (Ericsson &
Charness, 1994).
The astute reader will no doubt notice that many of the strategies involved
in deliberate practice (i.e., a well-sequenced curriculum, direct instruction of
component skills, brief repeated opportunities to respond with feedback and rein-
forcement) are consistent with other domains of behavioral skill training such as
adaptive behavior, communication, and basic academic competency (Martens, Daly,
Begeny, & VanDerHeyden, in press; Martens & Witt, 2004). A considerable amount
of research exists concerning the use of these strategies to improve athletic perfor-
mance, particularly goal setting, feedback, and public posting (e.g., Brobst & Ward,
2002; Koop & Martin, 1983; Mellalieu, Hanton, & O’Brien, 2006; Smith & Ward,
2006; Ward & Carnes, 2002). This research is discussed at length in Chapters 6
and 7 of this volume. In contrast, monotonic increases in proficiency that result from
deliberate practice over a long period of time (Ericsson et al., 1993) exhibit unique
features and benefits (e.g., efficiency, automaticity, resistance to distraction), which,
in turn, alter both goals and strategies of practice itself. Surprisingly, these goals
and strategies are consistent with ancient wisdom concerning the development of
complex performance repertoires (e.g., martial arts training), and it is these unique
features that we focus on in the present chapter.
10 Automatic Repertoires 161
The first half of the chapter describes how behavioral principles (e.g., stimulus
control, chaining of component skills) are involved in the development of fluent,
efficient, and automatic performance. Specifically, we describe the goals of long-
term deliberate practice and how performance changes as a result, and review
research concerning how practice should be structured to maximize performance
gains and maintain motivation for more accomplished athletes. The second half
of the chapter describes neuromuscular adaptations that result from prolonged
practice and that underlie efficient and automatic performance. Here we describe
both neuromuscular (e.g., neuromuscular facilitation and recruitment efficiency)
and physiological (e.g., mechanistic and muscle plasticity) changes produced by
prolonged engagement at high levels in athletic activities.
Behavioral Principles Underlying Automaticity
Goals of Prolonged Deliberate Practice
If your opponent does not move, you remain still. If there is even
the slightest movement, you have already moved accordingly.
– Tai chi master, Wu Yu-hsiang (1812–1880)
A useful model for summarizing research into the development of proficient
performance is known as the Instructional Hierarchy (IH; Daly, Martens, Barnett,
Witt, & Olson, 2007; Haring, Lovitt, Eaton, & Hansen, 1978; Martens et al., in
press; Martens & Witt, 2004). According to the IH, learning any new skill involves
progression through a series of stages that correspond to increasingly higher levels
of proficiency. First, one must learn to perform the skill or its component behaviors
correctly in the absence of assistance and in response to key discriminative stim-
uli (acquisition). Discriminative stimuli signal when to execute a skill and whether
it is likely to be reinforced (i.e., produce the desired outcome). In the context of
sports training, discriminative stimuli might involve short directives from a coach
(“Pass!), the coach’s whistle, the location of the ball, or a certain configuration of
players on the field. Once a skill has been acquired, focus shifts to performing it
both accurately and quickly, or at rates approximating those of competent perform-
ers (f luency; Binder, 1996). Because emphasis is placed on the speed of responding
during fluency building, skills are often practiced in isolation and at levels of diffi-
culty commensurate with the learner’s ability (i.e., drill with instructionally matched
materials; Martens et al., 2007). Practice under more demanding, difficult, and var-
ied conditions strengthens control over responding by more naturalistic, game-like
discriminative stimuli (maintenance), and eventually produces the ability to perform
behaviors under novel conditions and as part of more complex, composite skills
(generalization). After a variety of composite skills have been mastered, the learner
becomes capable of reorganizing and reconstituting these skills to meet the demands
of changing situations (adaptation) (Binder, 1996; Johnson & Layng, 1996).
With respect to athletic performance, component behaviors are typically com-
prised of short motor segments that constitute the basics or fundamentals of a
162 B.K. Martens and S.R. Collier
particular sport (e.g., hitting a forehand in tennis, trapping and passing a ball in soc-
cer). From the perspective of the IH, the first goal of prolonged deliberate practice
is to build fluency (i.e., accuracy and speed) in these component motor skills so they
can be combined into composite motor chains (Mechner, 1995). Chaining refers
to the procedure by which one learns to perform a series of behaviors in sequence
following presentation of a discriminative stimulus and ending with reinforcement
(Alberto & Troutman, 2003). Completion of each behavior in the sequence (i.e.,
link in the chain) serves two functions: (a) as a discriminative stimulus for execut-
ing the next behavior in the chain and (b) as a conditioned reinforcer for executing
the previous behavior in the chain.
When teaching chains of responses that do not already exist in a learner’s
repertoire, each component behavior must be taught directly. Depending on the
number of behaviors involved, the initial acquisition of these response chains can
be a time-consuming process requiring tens and even hundreds of prompted trials
(e.g., Luyben, Funk, Morgan, Clark, & Delulio, 1986). Several methods for teach-
ing chained responses have been reported in the literature and include backward
chaining (teach behaviors in reverse order from the terminal response), forward
chaining (teach behaviors in sequence starting with the initial response), and total
task presentation (teach all behaviors each trial; Test, Spooner, Keul, & Grossi,
1990). For example, Luyben et al. used the forward chaining method to teach three
adults with mental retardation a nine-step sequence for executing a soccer pass.
Five levels of most-to-least prompting (i.e., full physical guidance, partial physi-
cal guidance, modeling, gestural prompt, verbal prompt) were used to produce a
correct response at each step, and all correct responses were reinforced with ver-
bal praise. Although all three participants reached criterion performance for passing
a ball within 29 training sessions, the number of prompted trials (approximately
20 per session) ranged from 440 to 580.
For learners who are already fluent in component skills, composite motor chains
can be trained relatively rapidly (Hodge & Deakin, 1998). It is not uncommon
for accomplished athletes to periodically reorganize their skill sets (e.g., “make
over” their swing), adopt more effective game s trategies, and/or alter their atten-
tional focus (Bell & Hardy, 2009). Changes such as these constitute refinements
in skill execution and are reinforced when they lead to better outcomes in com-
petition. For more proficient players, novel chains of composite skills can be
shaped through instruction and reinforcement (Scott, Scott, & Goldwater, 1997)
and may even emerge spontaneously with continued, attentive practice. For exam-
ple, Kladopoulos and McComas (2001) used the total task presentation method
to teach correct foul-shooting form to three NCAA Division II women’s basket-
ball players. The study began with a component analysis of techniques reported
in instructional manuals, resulting in a chain of five target behaviors. Dependent
variables included the percentage of shots made without touching the backboard
and the percentage of shots executed with correct form. During the training condi-
tion, proper form requirements were reviewed prior to each block of 10 trials, and
descriptive praise was provided for their use. Results showed that the percentage
of shots taken with correct form increased immediately (i.e., from 1 to 3 sessions
10 Automatic Repertoires 163
or 10–30 trials) to 100%. The percentage of shots made also increased for all three
participants.
Harding, Wacker, Berg, Rick, and Lee (2004) attempted to shape novel sequences
of techniques in martial arts students by directly reinforcing variability in respond-
ing. In this study, two adult Kenpo karate students participated in both drill and
sparring sessions with their instructor. During baseline, the students were instructed
to use any of 54 different hand-and-foot techniques in any combination in response
to the instructor’s punches. Students were also told to use the techniques in differ-
ent combinations, and the number of different techniques executed by the students
was recorded each session. In the differential reinforcement condition, the instructor
provided brief verbal feedback and praise during training drills when the students
executed a different technique or combination of techniques. Repeated techniques
were ignored. Results showed an increase in the variability of techniques performed
by both students during drills, and these increases in variability generalized to
sparring sessions.
Once a number of composite motor chains have been acquired, the second goal of
deliberate practice is to develop increasingly more ballistic execution of these chains
so they can be performed repeatedly in the same way under different conditions
(Mechner, 2009). A well-known finding of basic operant research is that reinforce-
ment makes behavior more frequent, stereotyped, and efficient (Daly, Martens,
Skinner, & Noell, 2009; Reynolds, 1975). Moreover, decreases in variability that
result from the practice and reinforcement of motor skills appear to be accompanied
by a shift in neural activation from widely distributed cortical regions (e.g., sensori-
motor, temporal, and occipital cortexes) to subcortical regions (e.g., basal ganglia)
(Floyer-Lea & Matthews, 2004; MacPherson, Collins, & Obhi, 2009). This suggests
that with practice, the execution of composite motor chains gradually shifts from
conscious to unconscious (i.e., automatic) control (MacPherson et al.). In the con-
text of deliberate practice, expert athletes are likely to experience repeated cycles of
effortful and attentive learning, a gradual reduction in effort following practice, and
a shift to unconscious control as they master subsequent skill sets. Over time, this
cycle is likely to be well discriminated and perhaps even anticipated, leading to the
negative reinforcement of practice behavior by a reduction in effort. Once a skill set
has been practiced to automaticity, it is essentially forgotten (i.e., no longer requires
conscious control) and the learner can concentrate on other or more advanced
skills.
Practicing a skill to high levels of fluency or automaticity carries additional ben-
efits for the performer that have been summarized by fluency researchers with the
acronym RESAA (Johnson & Layng, 1996). These benefits have been used as mark-
ers in other domains (e.g., training academic skills) to determine how much practice
is necessary for a skill to be functional, and as such represent functional fluency
criteria. These benefits would seem particularly applicable to sports and suggest
that athletic skills practiced to functional fluency aims are more likely to exhibit
(R)etention in the absence of practice, (E)ndurance over longer practice intervals,
(S)tability in the face of anxiety or distraction, (A)pplication to composite skills
and untrained conditions, and (A)dduction (spontaneous modification) to meet novel
164 B.K. Martens and S.R. Collier
demands (Martens et al., in press). In support of this position, Driskell, Willis, and
Copper (1992) conducted a meta-analysis of research concerning the effects of over-
learning (i.e., practicing skills beyond an initial accuracy criterion) on retention.
They found a mean effect size of 0.44 for physical tasks and a mean effect size
of 0.75 for cognitive tasks. In addition, the effect size for retention was signifi-
cantly correlated (r = 0.48) with the degree of overlearning (e.g., 50% vs. 200%)
but not the retention interval (r = –0.0021). The authors concluded that overlearn-
ing produces moderate and significant increases in retention, and this benefit “may
be particularly important for team training, in that integrated team performance
requires that each team member retain a high level of proficiency to support overall
team performance” (p. 620).
A third goal of deliberate practice is to bring the automatic execution of complex
skills under control of key discriminative stimuli in the competitive environment. In
sports, athletes are required to respond quickly and accurately to changing arrays
of complex stimuli (Ericsson & Charness, 1994). As an example, a player receiving
service in tennis may have only 500–600 ms in which to organize and execute a
return (Abernethy, 1991). Doing so requires attention to relevant features of the
stimulus complex (e.g., the location and speed of the approaching ball), move-
ment to a favorable position on the court, execution of the return in a fashion
similar to how it was practiced, evaluation of the outcomes of the action, prepa-
ration for the next response, and so on. Research in this area falls under the general
rubric of decision making and has compared decision accuracy, reaction times,
response uncertainty, and anticipatory strategies of athletes at different levels of
expertise (Abernethy, 1991; Ericsson & Charness, 1994). Findings have suggested
that (a) reaction time increases with increases in stimulus-response uncertainty,
(b) reaction time is affected less when the task is highly practiced, and (c) reac-
tion times for skilled athletes are actually similar to those of novice performers on
laboratory tasks (Abernethy, 1991).
Findings such as these led Abernethy and his colleagues to examine why highly
skilled fast ball and racquet sport athletes appeared to be unhurried when return-
ing a shot even though they were subject to the same uncertainty and reaction time
constraints as novices. Through a series of investigations, these researchers exam-
ined expert–novice differences in the use of visual cues (i.e., discriminative stimuli)
that provide “reliable anticipatory information” (Abernethy, 1991, p. 203) about
the flight of the approaching shot. The hypothesis was that elite athletes move into
position and begin executing a return much sooner than novices and in response
to visual cues (e.g., arm position) from their opponents that occur well before the
shot is actually struck. By selectively masking different portions of an opponent’s
body in video presentations, they found that international-level badminton players
did indeed make use of earlier cues in the motor behavior of the other player to
predict the landing position of the shuttle. Put another way, expert athletes essen-
tially circumvented reaction time constraints in deciding where the shuttle would
land by attending to visual cues more proximal to the opponent’s movement. This
suggests that one reason why elite athletes appear unhurried and to move almost
in unison with their opponents is that prolonged practice under varying conditions
10 Automatic Repertoires 165
allows them to develop increasingly more accurate anticipation or “looking ahead”
capacity by attending to much earlier visual cues (see also Mechner, 2009). Athletes
who have truly mastered a sport respond automatically to stimuli, of which novices
may not even be aware.
Practice Techniques for More Proficient Athletes
If you pay attention to your spirit and ignore your breathing, your
striking force will be as strong as steel. If you pay attention to
your breathing, your force will be inactive and ineffective.
– Tai chi master Wu Yu-hsiang (1812–1880)
As noted previously, achieving high levels of proficiency in a sport generally
requires a well-sequenced set of skill-building activities, direct instruction and feed-
back by a coach, and a long period of effortful, attentive practice aimed at improving
performance. Although these strategies can be used with athletes of all skill lev-
els, several training methods have been shown to be differentially effective for
more proficient performers. In general, these strategies are designed to help ath-
letes make more s ubtle refinements in the execution of complex skills and to reduce
the disruptive effects of anxiety on performance.
Modeling by experts. One training strategy for helping athletes at more advanced
levels refine their skills is simply exposure to expert models. Weissensteiner,
Abernethy, and Farrow (2009) conducted an interview-based qualitative study of
the developmental histories of 14 elite male cricket players, administrators, and
coaches. Several key developmental factors were found to support the progression to
elite status (e.g., strong parental support, access to resources) including an extensive
history of observational learning in the sport. Specifically, expert batters reported
spending hours carefully observing cricket games as well as the techniques of their
favorite sports heroes. Apparently, by observing more accomplished players, the
developing athletes were able to learn nuances in performance that could not be ade-
quately described in words. Along these lines, Hodge and Deakin (1998) examined
the effects of modeling with and without accompanying verbal descriptions (what
the investigators termed “context”) on the ability of expert and novice Canadian
martial artists to replicate novel kata (i.e., sequenced patterns of already acquired
offensive and defensive techniques). Expert practitioners (first-degree black belts)
exhibited higher percentages of serial accuracy than novices (green and orange
belts) across all conditions, presumably due to “their extensive experience in the
martial arts domain” (p. 268). In addition, the results indicated that the increased
recall demands of the running verbal context actually hindered the performance of
the novice group, particularly on initial trials.
Use of metaphors. Another potentially useful strategy for promoting more
nuanced performance of skills is to make use of metaphors during training. As noted
by Mechner (1995), metaphors are commonly used by coaches to elicit associations
regarding the features of movement that are relevant to a particular skill. For exam-
ple, by invoking the image of being “like the eagle which glides serenely on the
166 B.K. Martens and S.R. Collier
wind, but which can swoop instantly to pluck a rabbit from the ground” (Liao, 1990,
p. 115), a martial artist may be encouraged to evade attacks by calmly moving in
circles, each one connected to the next and then abruptly exiting or closing these
circles when the time comes to attack.
Due to the large number of character ideograms in the Chinese language, the
Chinese continued to rely on block printing well after the invention of movable-type
presses. As a result, early efforts to document key training principles in the martial
arts were necessarily terse in an attempt to capture the essence of a movement as
efficiently as possible. In tai chi chuan, for example, this explains the often poetic
names of postures (e.g., high pat horse, snake creeps down) as well as the frequent
use of metaphors (e.g., “When in stillness you should be as the mountain”; Liao,
1990, p. 115) in classic writings.
Attentional focus prompts. As skills become increasingly well practiced, a per-
former’s attention gradually shifts from the conscious and deliberate execution of
component behaviors to execution of the entire chain. Through repetition, chained
sequences of behavior become more stereotypical and automatic, allowing the
focus of attention to shift to intended performance outcomes (e.g., positioning of
the ball in an opponent’s court, moving an opponent into position for a strike).
Even after extensive practice, the latter stages of this progression can be disrupted
when athletes experience anxiety, or what is commonly referred to as “chok-
ing” in high-stakes competition. One hypothesis for why this occurs is that under
anxiety-provoking conditions, athletes redirect their attention internally (e.g., to the
execution of component behaviors), which interferes with the automatic control
of highly practiced skills (Bell & Hardy, 2009). This suggests also that providing
prompts to help an athlete reestablish an external focus might facilitate performance
under such conditions. Bell and Hardy tested this hypothesis by assigning 33 skilled
male golfers (mean handicap of 5.5) to one of three conditions: an internal focus
group (position of the wrist), a proximal external focus group (position of the club
face), and a distal external focus group (flight of the ball). After warming up, each
participant was required to hit three blocks of 10 chip shots from approximately
22 yards (20 m) while the distance from the pin of each ball was scored. The atten-
tional focus manipulation was conducted by having each player repeat a brief phrase
that corresponded to their assigned condition prior to each shot (e.g., “wrist hinge”).
Blocks of trials were repeated under a neutral condition and an anxiety-provoking
condition in which participants were t old that their performance would be evaluated
by a PGA professional, publicly posted, and monetarily rewarded if they showed
improvement.
Manipulation checks revealed significant differences in self-reported attentional
focus as well as state anxiety levels across the relevant conditions. In terms of per-
formance, significant differences were found among all three groups under both
neutral and anxiety conditions, with shot accuracy being the highest f or the distal
external focus group followed by the proximal external focus group and then the
internal focus group. Although mean performance for both the internal focus group
and the proximal external focus group was slightly less accurate under the anxiety
condition, the distal external focus group was slightly more accurate. The authors
10 Automatic Repertoires 167
concluded that it is preferable for highly skilled performers to adopt a distal exter-
nal focus under varying performance conditions, the exception being if they are
attempting to consciously remake aspects of a skill (Bell & Hardy, 2009).
Rhythmic priming cues. An interesting but sometimes overlooked component of
highly trained motor sequences is their rhythm or temporal phasing (MacPherson
et al., 2009). For example, research has suggested that skilled competitive cyclists
(M = 11 years in competition) have a preferred rhythm or cadence that is less
variable than that of unskilled cyclists and is associated with lower heart rate and
oxygen uptake (i.e., requires less effort and is more efficient; MacPherson, Turner, &
Collins, 2007). Moreover, the more experienced the athlete, the more rapidly they
are able to return to their optimal cadence following disruption (e.g., matching pedal
strokes to a metronome), suggesting that domain-specific practice and experience
are “important factors in establishing stable movement parameters” (MacPherson
et al., 2007, p. 52).
Just as anxiety can disrupt performance by interfering with the automatic reg-
ulation of well-practiced movement sequences, MacPherson et al. (2007)have
suggested that it may also interfere with a movement’s temporal phasing. That is,
negative emotional states may hinder performance of complex skills by destabiliz-
ing previously established rhythms. This being the case, MacPherson et al. have
suggested that one possible training technique for more proficient athletes is to
provide external cues that help reestablish these optimal rhythms. As an exam-
ple of the potential effects of this technique, Southard and Miracle (1993) had
eight female college varsity basketball players alter the relative timing and dura-
tion of their pre-shot rituals for foul shooting. Each participant completed 15 foul
shots under standard-time ritual, halftime ritual, double-time ritual, and variable-
time ritual conditions. The relative timing of behaviors was preserved under the
first three conditions but not the last (i.e., variable time condition). Results showed
that participants made significantly more foul shots under the standard-, halftime,
and double-time conditions in comparison to the variable-time condition, suggest-
ing that maintaining the relative timing of their eight-step, pre-shot rituals was an
important determinant of shot success.
Reinforcing deliberate practice. A final consideration concerning training tech-
niques for elite athletes is the problem of how to maintain the motivation to practice
over a 10+-year period. In their seminal paper on deliberate practice, Ericsson et al.
(1993) characterized effortful practice as lacking inherent enjoyment but rather
being engaged in as a means of improving performance. In contrast to this assertion,
several studies in which athletes were asked to rate their enjoyment of various prac-
tice activities (e.g., team practice, watching games, weight training) have suggested
that many of these activities were moderately to highly enjoyable (Helsen et al.,
1998; Hodge & Deakin, 1998). Using an 11-point scale with 0 = low enjoyment
and 10 = high enjoyment, Belgian soccer players in the Helsen et al. study gave
mean ratings above 5 on 13 of 19 items. Items rated the highest included watching
soccer and analyzing game videos individually, working one on one with a coach,
and practicing technical and tactical skills in team. Similar results were reported
for a sample of Canadian martial artists who gave mean ratings above 5 on 21 of
168 B.K. Martens and S.R. Collier
26 different activities (Hodge & Deakin, 1998). For this sample, the highest rated
items included training with others in kata or sparring, training one on one with the
instructor, and practicing kata individually.
These data as well as research into developmental factors contributing to the
achievement of elite status suggest that reinforcement for deliberate practice comes
from multiple sources. These sources are likely to include social-positive reinforcers
from a parent, coach, or other players (e.g., praise, corrective feedback, shared expe-
riences of hardship), social-negative reinforcers in the context of competition (e.g.,
avoiding being benched, losing the game, or having a losing season), and automatic
or self-mediated reinforcers that arise from closely monitoring one’s own perfor-
mance (e.g., the experience of being “in the zone,” a reduction in effort that comes
from achieving automaticity, matching of performance to a valued model, improve-
ment over time). Given that deliberate practice is geared toward the attainment of a
goal (i.e., improving performance) and requires a considerable amount of time prac-
ticing alone, this latter category of reinforcers would seem particularly important for
maintaining motivation.
Neuromuscular Correlates of Automatic Performance
Xian Tian means inborn, congenital, or first nature. Hou Tian refers
to those qualities that arise after birth. To reach the highest level, you
must turn Hou Tian abilities into Xian Tian abilities.
– Tai chi master, Tung Ying Chieh (1897–1961)
Deliberate practice produces movements that become “second nature” to individ-
uals who spend years perfecting these movements. Early in the 1900s, Sherrington’s
work revealed that movement is controlled in the central nervous system by cen-
tral pattern generators (CPGs), which are specialized neural networks that, when
provoked, provide oscillatory motor output without oscillatory input (Sherrington,
1910). Subsequent work in this area has shown that these centers control tasks such
as respiration and locomotion. It would appear that repeated activity provides local-
ization in specific centers of the brain, which help develop spinal cord–mediated
responses that are crucial for fluid and efficient movements. For example, consider
learning to walk or acquiring gait pattern as part of a maturational process that
begins in the toddler stage. This is a task we rarely think about, yet we come to
exhibit smooth fluid movements during locomotion as a result of practice over an
extended period of time. Locomotion is the most extensively studied of the CPGs
within the central nervous system, although more recent studies have examined
other CPG-controlled movements (Guertin & Steuer, 2009). This section provides
an overview of the physiological concepts for spinal cord–mediated responses and
rate coding of motorneurons to facilitate “deliberate practice” so performance can
become consistent and automatic. Second, in this section we discuss neuromuscu-
lar adaptations that arise from prolonged engagement in a domain-specific training
regimen.
10 Automatic Repertoires 169
Repeated Practice and Muscle Fiber Recruitment
Force output during a voluntary contraction can be increased by two methods: either
by increasing the number of active motor units or by increasing the firing frequency
of the active motor units. The Henneman size principle of motor unit recruitment
demonstrates that the smallest units are recruited first, and thereafter, other motor
units are recruited in a sequential order based on physical size (Henneman, 1957).
This lends to orderly recruitment of muscle fiber, which has specific energy relation-
ships between muscle fiber recruitment to task. For example, one would not want to
maximally recruit their forearm muscles to pick up a pencil, as the force required
for the task is low. However, if one needed to pick up a much heavier object, the
muscle recruitment would demand the involvement of more powerful muscle fiber.
The motor unit recruitment progression would typically go through type I → type
IIa → type IIx, so activities requiring low force would typically need to recruit only
type I muscle fiber to complete the required task. This lends to energy conserva-
tion since activities requiring low force need to recruit only muscle fiber that will
be most efficient at completing the desired task. Incremental force increases will
lead to greater recruitment of muscle fiber involving the larger, yet more fatigu-
ing motor units to meet the demands necessary to complete the task (Henneman,
Somjen, & Carpenter, 1965). This orderly recruitment also minimizes the develop-
ment of fatigue by allowing the most fatigue resistant fibers to be used most of the
time, holding the more fatigable fibers in reserve until needed for higher forces, thus
allowing for greater fine motor control.
The precise mechanisms of fatigue vary depending on the specifics of the task.
Fatigue ultimately depends on the intensity of the activity, the type of contrac-
tion, and the muscle group utilized. Deliberate practice increases time to fatigue
by adapting muscles to the style of training; hence, if power training is undertaken,
the muscles become faster and stronger, yet endurance training (long duration) will
result in less readily fatigued muscles.
Skeletal muscle shows great plasticity in adapting to the type of training envi-
ronment in which it is placed. Mechanisms responsible for muscular adaptation for
power (force) or endurance are various and include increased skeletal muscle blood
flow and mitochondrial adaptation, increases in muscle growth factors, increases in
neural adaptation, increases in muscle cross-sectional area, and enzymatic changes
in fiber-type specificity. Training for muscle power relies on a strength regimen that
is best explained by the SAID principle where we realize a Specific Adaptation to an
Increased Demand on the muscle. When a training load becomes easier to lift, the
demand on the muscle becomes less; therefore, one must increase the demand on the
muscle and the resistance to facilitate improvements in strength. Without an over-
load on the muscles, there will be no improvement in strength. When training in this
fashion, one realizes that improvements in strength are specific to the movement that
is being repeated, which can be explained by neural adaptation. Over time, there is
a marked improvement in muscle strength due to increases in muscle growth factors
that increase the cross-sectional area of the muscle fiber. It is important to note that
humans do not add more fiber, but increase the size of existing fibers. This increase
170 B.K. Martens and S.R. Collier
in size will increase the velocity of muscle shortening, which will increase speed,
leading to enhanced peak rate of force generation and also a higher frequency of
excitation, which will permit a higher rate of force development. We know that type
II muscle fiber shows the greatest propensity for hypertrophy, yet fatigue quicker
than type I muscle fiber (greater endurance). Since we cannot change the type of
muscle fiber from type I to type II and vice versa, we change the enzyme character-
istics enabling each type to behave more like the type characteristic of the training
we are performing. Typically, resistance training for power results in the use of type
II fiber and Staron et al. (1990) have shown an increase in type IIa with a concomi-
tant decrease in type IIb fibers over 20 weeks of progressive resistance training.
Power lifters make poor marathoners and vice versa, which is the premise behind
fiber-type specificity.
Marathon training is considered a form of endurance training where the mus-
cles undergoing frequent repetition become more fatigue resistant. This decrease
in fatigability is in part due to an increase in the sheer numbers of mitochondria
(Hoppeler et al., 1985) and an increase in types I and IIa enzyme expression (Friden,
Sjostrom, & Ekblom, 1984). While an individual is exercising, the more mitochon-
dria results in a superior supply of energy (ATP) for aerobic activities resulting
in longer performance times with less fatigue. Aerobic training also increases the
blood supply to the active muscle by creating larger capillary networks or beds that
improves oxygen and nutrient delivery to the exercising fibers and a more effica-
cious removal of metabolic wastes (K
+
,H
+
) (Collier et al., 2008; Hudlicka, 1990).
The smaller circumference of these muscle fibers may possess an advantage over
their power training counterparts as the adaptation may allow for greater diffusion
of metabolites and nutrients allowing the body to continue with exercise further over
time without the build-up of exercise-limiting factors.
Training for Muscle Strength and Efficiency
As one continues with their training regimen, the frequency and type of training
can contribute to more effective gains in their respective competition and/or fit-
ness. The question of single vs. multiple sets has been an issue for decades in
the exercise science literature. It has been shown that single sets are as effective
as multiple sets on variables such as strength (Starkey et al., 1996), yet multiple
progressively heavier sets show greater increases in muscle hypertrophy (Kraemer
et al., 2002). It is necessary to keep in mind that as one continues with their train-
ing regimen, recent data show that highly trained athletes require multiple sets of
resistance exercises per muscle group to elicit maximal strength gains (Peterson,
Rhea, & Alvar, 2005), whereas untrained individuals seem to benefit most from
programs using fewer sets per muscle group (Galvao & Taaffe, 2004; Peterson
et al., 2005). Much like the SAID principle, any individual undertaking a weight-
training regimen should increase the intensity of their program by adding sets
as time progresses or their program will not keep progressing with increases in
strength.
10 Automatic Repertoires 171
Perhaps not fully understood is the combination of strength and endurance-
training programs, which are expected to yield the best of both training regimens
in less combined time. Termed “cross-fit” programs, athletes and fitness gurus alike
purport gains in overall aerobic fitness while maintaining or improving overall
strength. As mentioned previously, it may be best to separate t raining by the specific
outcome desired, such as dedicating strength training to one day and cardiovascu-
lar training to a separate day. It has been shown that both untrained and trained
participants who undertake combined endurance and strength training during the
same workout show attenuated strength gains in comparison to individuals who
undertook separate training days for strength and cardiovascular fitness (Dudley &
Djamil, 1985; McCarthy, Pozniak, & Agre, 2002; Sale, Jacobs, MacDougall, &
Garner, 1990).
It is well known that men are generally stronger than women when total force is
compared, and the upper body shows the greatest sex differences as men have been
shown to be 50% stronger than their female counterparts (Morrow & Hosler, 1981).
These sex differences are attributed to the larger muscle mass with which men are
genetically predisposed, due to 20–30 times higher concentrations of testosterone
than in women. Yet women show greater gains in strength after a weight-training
regimen. However, in terms of fatigue, women show less fatigue during acute
endurance bouts than age-matched men (Clark, Collier, Manini, & Ploutz-Snyder,
2005). This could be due to less active hyperemia, where women can keep the
muscle perfused with blood due to less occlusive force when compared to the
greater contraction force of men, which cuts off blood supply to the active mus-
cle. Further, women may show a greater propensity to utilize accessory muscles
to keep the fatiguing contraction to produce measurable force over longer periods
of time.
When an individual undertakes a resistance or endurance training activity, one
common result is the onset of muscle soreness. For novice athletes, the greatest
degree of muscle soreness usually results in 48 h after the completion of their train-
ing session. Delayed onset muscle soreness (DOMS) is still not fully explained;
however, it is commonly thought of as a muscle tissue injury resulting from the
excessive force on the connective and muscle tissue that causes an inflammatory
response resulting in edema. The edema is what causes pain until the swelling sub-
sides and movement becomes pain free. In athletes and individuals with years of
fitness training, one experiences soreness when they change exercises, intensity, or
duration of their current regimen. This changes the angle of pull on the muscle and
connective tissue, causing inflammation and pain even in the well trained. However,
the pain and edema will be much less after repeated bouts as training results in
adaptation of the neural system, metabolites, and active tissues.
Previously it was thought that strength training would lead to a decrease in
flexibility, and the myth of becoming “muscle bound” was propagated. This is coun-
terintuitive since an increase in muscle around the joint actually leads to an increase
in flexibility (Kraemer, Ratamess, & French, 2002). Stretching or flexibility training
may increase flexibility, but it has recently been shown that improved flexibility may
not decrease the incidence of sports or exercise-induced injuries (Hart, 2005). Yet it
172 B.K. Martens and S.R. Collier
is well known that stretching exercises lead to gains in flexibility, which is needed
to optimize the efficiency of movements used in many martial arts, gymnastics, and
other sports where flexibility is of great importance.
Summary
In 2004, Timothy Noakes and colleagues hypothesized that physical activity was
controlled by a central governor in higher centers of the brain and that the human
body synergistically functioned as an extremely complex system during exercise.
They proposed a continuously altering pacing strategy, a “black box theory,” which
theorized that exercising skeletal muscle responded to afferent feedback from
different physiological systems and the sensation of fatigue was the conscious
interpretation of these homoeostatic, central governor control mechanisms (Noakes,
St Clair Gibson, & Lambert, 2005).
With repeated bouts of exercise, an exercise training effect is shown where the
body responds almost with previous knowledge of the task. Physiological responses
are closely tied with psychological responses to produce a calculated amount of
effort. If we increase the intensity or demand on the physiological system, we
will realize a physiological adaptation resulting in a greater tolerance (i.e., training
effect) that enables the physiological system to work at the heightened level. After
several years of this training routine, the central movements will seem effortless and
even automatic.
The keys to training for automaticity, then, are to understand the component
behaviors that relate to the task, repeatedly practice these behaviors, and add training
conditions that will aid one’s ultimate training goal. If one requires speed, then the
addition of resistance training will help lengthen the muscle, which in turn makes it
faster and stronger. If endurance is the desired outcome, the application of aerobic
training will increase the time one will be able to endure the higher metabolic cost of
endurance events. It is important to remember that fatigue during any form of exer-
cise occurs without evidence of related failure of physiological homoeostasis. This
means that the body may still be able to perform, but attention and concentration
may be compromised by fatigue. This clearly suggests that psychological training
is an integral part of the training regimen and should occur at the same intensity as
one’s physical preparation.
Conclusion
A basic tenet of this chapter is that for any voluntary activity, reaching the level
of automaticity requires a large number of opportunities to respond. Developing
automaticity in the complex repertoires of skilled athletic performance requires a
complete commitment to one’s sport followed by a long period of deliberate practice
(Weissensteiner et al., 2009). What does an athlete gain in return for this invest-
ment? The answer to this question requires an appreciation for human beings as
10 Automatic Repertoires 173
highly adaptive organisms. One mechanism by which we adapt to our environ-
ment is what Skinner (1987)termedoperant selection, or when certain behaviors
in a person’s repertoire come to predominate because they are functional (i.e.,
repeatedly reinforced) by that environment. From this perspective, automaticity can
be viewed as both a behavioral and a physiological adaptation to participating in
sports. Behaviorally, deliberate practice produces a large repertoire of skilled per-
formance, the components of which can be executed with high levels of fluency, can
be instantly reorganized to meet changing demands, and are under strong stimulus
control of subtle cues in the competitive environment. Physiologically, deliberate
practice produces a shift from conscious to automatic control over behavior, increas-
ing optimization of muscle recruitment strategies, and changes in muscle physiology
to support more efficient movement. In short, in return for a long period of deliberate
practice, research suggests that an athlete literally becomes a different person – one
uniquely suited both behaviorally and physiologically to meet the unique demands
of their sport.
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Chapter 11
Sport Neuropsychology and Cerebral
Concussion
Frank M. Webbe
Sport neuropsychology defines a discipline of recent origin that combines the two
stand-alone disciplines of sport psychology and neuropsychology. Sport psychology
can be defined as “the scientific study of people and their behaviors in sport and exer-
cise activities and the practical application of that knowledge” (Weinberg & Gould,
2007, p. 4). Neuropsychology studies the relationship between the functioning brain
and behavior, with behavior often broken down into intellectual, emotional, and
control components (Lezak, 1983). Both parent fields exhibit several similarities,
and each has its scientific and applied sides. Experimental neuropsychology uses
methods from experimental psychology to uncover the relationship between the ner-
vous system and behavior, where behavior includes overt as well as within-brain
cognitions. Both human and animal experimentation are common in experimental
neuropsychology for the same methodological and ethical reasons that both exist
in more standard behavior analytic approaches. Clinical neuropsychology applies
neuropsychological knowledge to the assessment, management, and rehabilitation
of people with neurobehavioral problems due to illness or brain injury. It brings
a psychological viewpoint to treatment, to understand how such illness and injury
may affect, and be affected by, psychological factors.
In sport psychology, the split is more complex. Exercise science is the predom-
inant scientific side, but the psychology half also is divided into clinical versus
scientific aspects. Obvious areas of overlapping interest exist between sport psy-
chology and neuropsychology. For example, exercise science studies motor control
and motor learning in sport. Brain injuries might obviously impact such learn-
ing and performance, and the rehabilitative effects of relearning motor behavior
might in turn affect recovery processes in the brain. A sport neuropsychological
approach would map such relationships. Sport science also includes exercise phys-
iology, which details how the demands of exercise alter homeostatic levels and
contribute to phenomena such as fatigue, motor errors, and disoriented thinking. For
example, marathon runners and other endurance athletes ultimately endure compro-
mise of normal metabolic functioning. Exercise physiologists might be interested
F.M. Webbe (B)
Florida Institute of Technology, Melbourne, FL, USA
e-mail: webbe@fit.edu
177
J.K. Luiselli, D.D. Reed (eds.), Behavioral Sport Psychology,
DOI 10.1007/978-1-4614-0070-7_11,
C
Springer Science+Business Media, LLC 2011
178 F.M. Webbe
Table 11.1 Comparison of
sport psychology vs. sport
science disciplines
Sport science – exercise Sport psychology
Biomechanics Abnormal psychology
Exercise physiology Clinical/counseling psychology
Motor development Developmental psychology
Motor learning and control Learning/behavior analysis
Sport pedagogy Personality psychology
Sport sociology Physiological psychology
in knowing when such compromise occurs, what the biomarkers are, whether it can
be prevented or delayed, and how the altered physiology will impact performance.
Table 11.1, adapted from Weinberg and Gould (2007), indicates the various subareas
within the sport science-exercise versus psychology domains.
The areas of research and practice that define modern sport neuropsychology
include (1) sport-related concussion, (2) role of exercise in enhancing neurocog-
nitive functioning, (3) neurodevelopmental contributions of sport participation,
(4) exercise and sport in the rehabilitation of persons with brain damage, and
(5) psychoeducational interventions with athletes playing contact sports.
One of the more exciting aspects of this discipline is the heterogeneity of topics
that are studied. However, one topic that actually originated the discipline and that
has assumed its greatest prominence in the past year is the study of concussions
in sport.
Sport-Related Concussion
What Is a Concussion?
The most elaborated area of interest in sport neuropsychology is the study of
sport-related concussion. There have been many definitions of concussion over the
years, with no single one achieving unanimous acceptance. However, at the 3rd
International Conference on Concussion in Sport held in Zurich in November 2008,
the A-list of attendees arrived at the following consensus definition ( McCrory et al.,
2009, p. 186):
Concussion is defined as a complex pathophysiological process affecting the
brain, induced by traumatic biomechanical forces. Several common features that
incorporate clinical, pathologic and biomechanical injury constructs that may be
utilized in defining the nature of a concussive head injury include:
1. Concussion may be caused either by a direct blow to the head, face, neck or
elsewhere on the body with an impulsive force transmitted to the head.
2. Concussion typically results in the rapid onset of short-lived impairment of
neurologic function that resolves spontaneously.
3. Concussion may result in neuropathological changes, but the acute clinical
symptoms largely reflect a functional disturbance rather than a structural injury.
11 Sport Neuropsychology and Cerebral Concussion 179
Table 11.2 Concussion severity grading guidelines
AAN Cantu
Grade I Symptoms < 15 min and No
LOC
All symptoms < 30 min and No LOC
Grade II Symptoms > 15 min and No
LOC
Symptoms > 30 min but < 7 days and/or PTA >
30 min but < 24 h and/or LOC < 1 min
Grade III Any LOC Symptoms > 7 days and/or PTA >24 h and/or
LOC > 1 min
4. Concussion results in a graded set of clinical symptoms that may or may not
involve loss of consciousness. Resolution of the clinical and cognitive symptoms
typically follows a sequential course; however, it is important to note that, in a
small percentage of cases, post-concussive symptoms may be prolonged.
5. No abnormality on standard structural neuroimaging studies is seen in
concussion.
In order to create standardized approaches for discussing concussion severity that
can be of functional use to health-care personnel, systems for grading concussions
have been developed that concentrate primarily on loss of consciousness (LOC) and
posttraumatic amnesia (PTA), two common symptoms of the many that may occur
following concussion. Although many such systems have been developed over the
years, two predominate and are summarized in Table 11.2.
The guidelines of the American Academy of Neurology (AAN; Kelly &
Rosenberg, 1997) emphasize the qualitative importance of LOC, whereas the guide-
lines developed by neurosurgeon Robert Cantu (Cantu, 1986, 2001) distinguish
between brief and extended LOC, and also emphasize the duration of PTA. Injuries
are classified as Grade I (mild), Grade II (moderate), or Grade III (severe).
As was strongly emphasized in the Zurich Conference and is hinted at in the
Cantu grading guidelines, duration of recovery is a key element in determining the
severity of concussion. This often not only puts diagnosticians in a quandary, but
also provides a challenge to sufferers, their families, and their teams, not to know
fully the severity until recovery actually occurs.
Sport-Related Concussion
Sport-related concussion defines the phenomenon of concussion as occurring within
a sport context. For example, when two soccer players leap into the air, each attempt-
ing to head the ball but instead crashing their heads into each other with a resulting
impairment in brain functioning, the mechanics of a sport-related concussion have
occurred. As one or both players slowly rise to t heir feet and receive assistance
in leaving the field, a trainer may already be asking them questions to determine
the extent of amnesia, confusion, and disorientation that is present. They also will
have noted whether any loss of consciousness has occurred. The presence of such
180 F.M. Webbe
symptoms affirms functionally that a concussion has taken place. Such an incident
represents a sport-related concussion. In subsequent hours and days, issues of symp-
tom severity and duration will be assessed with an eye toward subsequent return
to play.
Concussion Pathophysiology
As the definition given above indicates, cerebral concussion is a closed head injury
that follows either impact blows to the skull and/or abrupt acceleration of the brain
within the skull. Examples of such trauma are a punch to the face or the rapid decel-
eration that occurs when a body in motion suddenly is blocked or thrown to the
ground. When the concussive event causes a rotational acceleration such that the
brain tissue moves against itself inside the skull, the risk for significant functional
impairment increases (Barth, Freeman, Broshek, & Varney, 2001). Once such phys-
ical forces have been applied to the brain, a succession of morbid outcomes ensue,
including rapid increase followed by massive decrease in intracranial pressure
(Hovda et al., 1999) and decrease in cerebral blood flow even as metabolic demands
for oxygen and glucose increase drastically (Giza & Hovda, 2001). Shortly after the
concussive event, electrical activity of the brain is depressed (West, Parkinson, &
Havlicek, 1982). The metabolic conversion of glucose and oxygen to cellular energy
first increases tremendously, consuming all available energy stores, and then stays
in decline for at least several days (Giza & Hovda, 2001). (See Webbe, 2006,fora
more detailed summary of the pathophysiology of sports-related concussion). The
pressure wave induced by accelerative forces acting on the brain within the skull
may produce differential accelerations of tissues (e.g., gray vs. white) that can result
in stretching or even shearing. Stretching of neural tissue, especially the axons,
causes cytoarchitectural changes that can include the unregulated opening of ion
channels followed by a flux of ions that produce massive cellular excitation, and
consequent depletion of cellular metabolic resources. The brain remains in a very
unstable state of metabolic demands uncoupled from cerebral blood flow for several
days to several weeks on the average (Giza & Hovda, 2001).
Traditional neuroradiological tools used in clinical practice such as computed
tomography (CT) and magnetic resonance imaging (MRI) are of little value in
determining the severity of concussive injury (Johnston, Ptito, Chankowsky, &
Chen, 2001) or in predicting recovery for two reasons. First, the structural dam-
age caused by concussion most likely happens at a level far more microscopic than
can be detected by any but the most sophisticated research scanners. Second, the
extent of structural injury even when detected appears to be a poor predictor of
functional deficit (Bigler & Orrison, 2004). Functional MRI (fMRI) and positron
emission tomography (PET) are much more likely to detect post-concussion anoma-
lies since the primary abnormalities are physiological/metabolic in nature (Chen,
Johnston, Collie, McCrory, & Ptito, 2007). We do know from research studies
of concussed patients that cortical brain activity declines following the trauma.
Electrophysiological techniques also show promise for detecting the presence of
11 Sport Neuropsychology and Cerebral Concussion 181
and severity of brain trauma, although such measures as event-related poten-
tial and evoked potential measurement rarely stray from the research laboratory.
Neuropsychological testing not only represents a functional assessment, but also
is objective (compared with simple symptom surveys), relatively inexpensive, and
widely available.
Concussion Epidemiology
Participation in sports is at an all-time high. In 2003, at the high school and college
levels where the sanctioning organizations carefully track participation, student-
athletes numbered 6,844,572 and 377,641 participants, respectively – an increase
of nearly 6% over the 5-year period beginning 1998 (NFHS, 2005; NCAA, 2004).
Although participation trends in youth sports are maintained less rigorously, the
National Council of Youth Sports estimated recently that 38,259,845 children (63%
boys) were engaged in some type of formal participation with teams or leagues
(NCYS, 2001).
Athletes of any age are at risk for concussion, the most common form of neu-
rologic head injury. Generally speaking, concussion risk increases as a function of
the speed of movement in the sport coupled with the intensity of physical contact.
The resultant collision frequency is one accepted measure of risk (Powell, 2001).
Thus, sports with the highest concussion risk include American football, soccer, ice
hockey, rugby, and lacrosse. However, there are some sleepers that might not be
considered at first because participation rates are much lower. These include eques-
trian sports, rodeo, and wrestling. Boxing, mixed martial arts, and other ring and
cage fighting events award a victory to a contestant who causes a brain injury to
the opponent. It is not surprising that concussion risk in these events is exception-
ally high (Powell, 2001). Both adults and children are subject to injury in any sport,
whether it is organized or informal. The United States Consumer Product Safety
Commission (CPSC) has listed the top 10 sports and activities in the United States
that result in the most head injuries for children under 14. The sport/activity and
the number of admissions reported in hospital emergency departments in 2007 are
shown in Table 11.3 (CPSC, 2007).
Particularly with the publicity generated by the forced retirements of high-
profile professional athletes and recent discoveries of chronic effects of repetitive
head trauma (to be discussed later), sports-related concussion has become a very
visible public health issue. According to the Centers for Disease Control and
Prevention, more than 300,000 athletes per year suffer sport-related head injuries
in the United States (Centers for Disease Control and Prevention, 2010; Sosin,
Sniezek, & Thurman, 1996). Since this estimate is based only on athletes who
lost consciousness – a phenomenon that occurs in only about 10% of diagnosed
concussions – the likely incidence of these injuries is much higher. Furthermore,
many concussions go unrecognized. In a recent study that examined reporting of
concussions, 620 Canadian collegiate athletes were asked to complete symptom
checklists based upon their experiences of the previous year. Seventy percent of
182 F.M. Webbe
Table 11.3 Emergency room
admissions for head injury
2007
Cycling 32,899
Football 17,441
Baseball and softball 13,508
Skateboards/scooters (powered) 11,848
Basketball 10,844
Skateboards/scooters 10,256
Winter sports (skiing, sledding, snowboarding,
snowmobiling)
7,546
Powered recreational vehicles 7,460
Water sports (diving, scuba diving, surfing,
swimming, water polo, water skiing)
6,498
Trampolines 6,360
football players and 63% of soccer players reported that they had experienced symp-
toms during the prior season that were consistent with concussion. Of these, only
23% of the football players and 20% of the soccer players actually recognized that
they had likely suffered a concussion or had been treated for a concussion (Delaney,
Lacroix, Leclerc, & Johnston, 2002). Even taking into account the weak reliability
of self-report data, these findings of underreporting are fully supported by addi-
tional outcome reports from other studies using both self-report and documentary
data (Goodman, Gaetz, & Meichenbaum, 2001; McCrea, Hammeke, Olsen, Leo, &
Guskiewicz, 2004; Williamson & Goodman, 2006). Moreover, athletes are noto-
rious for hiding concussion symptoms when they do occur, often in attempts to
prevent removal from an athletic contest and/or hasten return to play (Echemendia &
Julian, 2001; Gerberich, Priest, Boen, Straub, & Maxwell, 1983).
Gender
Concussion in sport is not an equal-opportunity injury in incidence, severity, or
symptom duration. Most recent studies have indicated that women and girls tend
to fare worse with these injuries than do men and boys. Regarding incidence, Dick
(2009) has reviewed key studies reported in PubMed going back to at least 10 years.
These included studies of concussion in all major contact sports played by both men
and women. He reported that in 9 of the 10 incidence studies covering football, soc-
cer, basketball, and ice hockey, women suffered a higher frequency of concussions
than did men, with outcomes reaching statistical significance in four of the nine
comparisons. Regarding symptoms, Broshek et al. (2005) and Lovell et al. (2006)
found that women reported more symptoms and greater severity of those symptoms
than did men. Regarding severity, Broshek et al. (2005) found that across several
sports, women’s baseline scores on key neurocognitive measures declined signifi-
cantly more following concussion than did those of men injured in the same sports.
Colvin et al. (2009) reported similar outcomses for soccer-specific concussions.
It appears clear that gender represents a risk factor in sport-related concussion
and posttrauma symptom and cognitive status. This conclusion naturally stimulates
the question why. We could theorize that women may be at greater risk for more
11 Sport Neuropsychology and Cerebral Concussion 183
severe injury and post-concussion effects due to lower body mass and smaller neck
size and supporting musculature. Conversely, it could be argued that concussion-
inducing collisions in women’s s ports may be less severe because of overall lower
body mass entering into the force–mass relationship. Either way, studies have not
yet supported these kinds of theories. Indeed, Colvin et al. (2009) report that it is
gender and not mass that appears to be most critical.
Some studies have reported that women appear to be more forthcoming in their
symptom reports than men (Broshek et al., 2005; Lovell et al., 2006), so the pos-
sibility exists that some differences may be due to gender-discrepant incidence and
symptom reports. However, the authors of both these studies argue against such
an interpretation since the neurocognitive testing outcomes also supported gender
differences.
From a physiological standpoint, gender differences in hormonal systems, cere-
bral organization, and musculature may partially explain the differential findings.
Results of studies with animals have implicated the sex steroid hormone, estro-
gen, as important in gendered differences in outcome from experimentally induced
brain injury. However, some studies support a protective effect of estrogen; some
demonstrate an exacerbation of injury (Roof & Hall, 2000). These discrepant find-
ings may be due to major differences in methodology including the mechanism
for producing traumatic brain injury (TBI), pretreatment regimen, and even inher-
ent differences in effects of exogenous versus endogenous estrogen. Progesterone
appears to reduce post-TBI neural impairment in humans, most likely by inhibiting
destructive membrane changes and the resulting vasogenic edema (Roof & Hall,
2000; Roof, Duvdevani, & Stein, 1993). In summary, despite some conflicting stud-
ies regarding estrogen’s role following TBI, the bulk of the hormonal data supports
a neuroprotective role for both estrogen and progesterone.
The fact that gender may differentially determine TBI incidence, severity, and
symptom resolution is a common thread of discussion in experimental neurology,
but less well known in neuropsychology. There are considerable gender differences
in the neural anatomy and physiology, cerebrovascular organization, and cellular
response to concussive stimuli. For example, cortical neuronal densities are greater
in males, while the number of neuronal processes is greater in females (de Courten-
Myers, 1999). Females also exhibit greater blood flow rates and higher basal rates
of glucose metabolism (Andreason, Zametkin, Guo, Baldwin, & Cohen, 1994;
Esposito, Van Horn, Weinberger, & Berman, 1996). To the extent that female brains
may have higher cortical metabolic demands, the typical decrease in cerebral blood
flow along with the increased glycemic demands caused by TBI may interact with
the already high gendered demands and result in greater impairment in females than
in males.
Acute Effect of Concussion History
In addition to the high rate of injury within sports, mounting evidence has also sug-
gested that the rate of re-injury is higher in athletes who have experienced a prior
concussion. For example, Guskiewicz et al. (2003) found that once a concussion
184 F.M. Webbe
is sustained, athletes are four to six times more likely to experience a second con-
cussion, even if the second blow is relatively mild. Along similar lines, athletes
with a history of multiple concussions have a greater risk of experiencing more
severe symptoms at the time of their next concussive injury (Collins, Lovell, Iverson,
Cantu, Maroon, & Field, 2002; Colvin et al., 2009). Subtle but significant, prolonged
cognitive effects of concussion also have been demonstrated in “normal” asymp-
tomatic high school athletes who suffered two or more concussions in the past, when
compared to youth who reported only one or none (Moser, Schatz, & Jordan, 2005).
In addition to the potential long-term morbidity associated with concussion, sec-
ond impact syndrome, a rare but nonetheless devastating and usually fatal medical
event, has been identified as a risk in athletes 21 years old and younger (Cantu,
1998). Second-impact syndrome is characterized by rapid brain swelling after the
athlete suffers a second impact to the brain before they have fully recovered from the
first insult. The mechanism for this catastrophic injury has been modeled in animal
studies and is linked to the changes in brain metabolism following trauma (Giza &
Hovda, 2001).
Physical Forces That Produce Concussion
Barth and colleagues have suggested that severity of injury and subsequent neu-
rocognitive impairment can be estimated by the acceleration-deceleration forces
acting on the brain (Barth et al., 2001). Since many studies have been conducted
with animals, and since animals and humans vary greatly in their ability to with-
stand impact and accelerative forces on the head, much of the literature is difficult to
integrate. In general terms, it is clear that when peak decelerative forces occur over a
very brief duration the risk of brain injury greatly increases. Naunheim, Standeven,
Richter, and Lewis (2000) indicated in their review that a score in excess of 1,500
on the Gadd Severity Index, or above 1,000 on the Head Injury Criterion (HIC), or a
peak accelerative force of 200 g should be considered thresholds for single impacts
likely to “cause a significant brain injury” in adult humans. These values were esti-
mated based upon animal studies and observations of accident outcomes in humans.
Naunheim et al. (2000) also measured peak accelerative forces in athletic competi-
tion by using an accelerometer embedded in helmets worn by soccer, football, and
ice hockey players. They recorded no impacts that approached the 200 g level, but
also observed no events that were correlated with reports of concussion. Although
this study does not shed light on the force–concussion relationship it does suggest
that concussion-generating forces occur with merciful infrequency in these sports.
Children differ from adults in critical aspects of neural development such that
the child’s brain may be more vulnerable to injury from forces that would not seem
problematic with adults. For example, Schneider and Zernicke (1988) studied con-
cussion risk in soccer heading via a computer simulation model, within which the
characteristics of the human participant along with the ball factors (acceleration,
vector, and mass) were varied. After first calculating typical accelerative forces in
players and nonplayers who were participating in a moderate heading drill, they
11 Sport Neuropsychology and Cerebral Concussion 185
applied the obtained acceleration, mass ratio, and duration values to the model. They
reported that unsafe values of the HIC (>1,000) occurred when children were mod-
eled in both translational and rotational acceleration conditions, and for adults in the
rotational condition. Because the outcomes with children suggested an interaction
between the mass of the individual with the mass of the ball as a critical variable in
the equation, Schneider and Zernicke (1988) recommended the use of small soccer
balls in contexts in which children might be heading.
Sport as a Laboratory Assessment Model: The Gold Standard
for Studying Sport Concussion
Barth’s study of sport-related concussion spurred the evolution of sports neuropsy-
chology. In the early 1980s, Barth and colleagues, including Macciocchi, Alves,
Rimel, and Jane and Nelson, began studying college football players who suf-
fered a concussion (Barth et al., 1989; Macciocchi, Barth, & Littlefield, 1998;
Macciocchi, Barth, Alves, Rimel, & Jane, 1996). Realizing the improbability of
multiple prospective participants for the study of brain injury in the general popu-
lation, Barth identified college football players as individuals at a significantly high
risk of brain injury. Neuropsychological tests were administered before the play-
ing season began, and were repeated for those players who suffered concussion as
well as for a non-concussed control group. From a medical, individual, and social
perspective, the results were optimistic in that they portrayed the typical sport con-
cussion in football as an event with transient neurocognitive impact. Much more
enduring in importance, however, the methodology of that study established for the
future a standard that has shaped the discipline. Specifically, Barth’s approach of
using the sport setting as a laboratory to study mild traumatic brain injury (sport
as a laboratory assessment model – SLAM) established prospective, longitudinal
methodology as the gold standard in the field (Barth, Harvey, Freeman, & Broshek,
2010). When athletes engage in rough, physical play there is an inevitability of
injury, including head injury. The notion of establishing baselines of neurocognitive
performance against which post-head injury performance could be compared rep-
resented a monumental improvement over the group, normative comparisons that
otherwise were the only choice. Moreover, along with pre-injury neurocognitive
testing, researchers also could collect information on premorbid physical and cog-
nitive symptomatology. Thus, the baseline assessment model greatly diminished the
variance inherent in making group normative comparisons. The remaining variance
associated with repeated testing, history, and maturation could be understood bet-
ter within the individual context. Though trained in using inferential statistics to
analyze group data, Barth’s training as a clinician, who concentrates on individual
behavior, led him to recognize early on that both scientific and applied advances
would occur only if an elaborated single-subject methodology was employed. The
enormity of his undertaking must be understood in the context of early 1980s
neuropsychology. There were no computerized tests. Rather, the process of neu-
ropsychological testing was elaborate and time consuming, necessitating hours of
186 F.M. Webbe
one-on-one interaction. The difficulty of convincing high-level athletes and their
coaches to dedicate hours of time before any injury had occurred cannot be overes-
timated. If wagers were placed upon the future of this approach, the smart money
would have predicted that baseline assessment of sufficient depth undertaken with
entire teams would be a very transient, self-limiting phenomenon. Nonetheless,
Barth and his colleagues persevered long enough for the results of their early studies
to be published. The first comprehensive report in 1989 (Barth et al., 1989) created
a storm of interest that empowered the continuation of the prospective, baseline
approach to concussion management. Fortunately also, the 1990s saw the develop-
ment of the first computerized neurocognitive screening measures (to be discussed
shortly), which removed the luxury label from baseline measurement and made the
methodology more widely applicable.
What has not been eliminated – indeed it has been enhanced – is the finding
of considerable i ndividual differences in such critical and basic areas as (a) dif-
ferences in the severity of outcome between individuals who receive apparently
similar head insults, (b) differences between individuals in duration of recovery
from concussions of apparently similar magnitude, (c) differences between individ-
uals in ultimate recovery from concussion such that they can resume their previous
activities, (d) effects of recurrent concussions on neurocognitive performance, and
(e) effects of sub-concussive blows on neurocognitive performance (Webbe & Barth,
2003).
Management Programs for Sport-Related Concussion
Baseline Testing
Best practice calls for preseason neurocognitive baseline testing to establish a
player’s premorbid level of functioning. Most professional leagues have imple-
mented such testing. For example, in the National Football League (NFL) and
National Hockey League (NHL), such testing is mandatory. Following several hor-
ror stories of the recent past, neuropsychologists such as Mark Lovell, Mickey
Collins, and Ruben Echemendia have been successful in creating an entire network
of qualified practitioners who are ready and accessible to test players who have
suffered a head injury (Lovell, Echemendia, & Burke, 2004). Common neuropsy-
chological tests employed to assess baseline cognitive performance are shown in
Table 11.4.
Trauma Testing
In sport-related concussion management, neuropsychological assessments are usu-
ally given within 24–48 h after the traumatic event to document acute effects
of the head injury and to compare level and pattern of performance to base-
line. Assessments are repeated 3–5 days later and again at 7–10 days. If
11 Sport Neuropsychology and Cerebral Concussion 187
Table 11.4 Common
neuropsychological tests used
in sports neuropsychology
Test category Test
Learning and
memory:
verbal/auditory
California verbal learning test
Hopkins verbal learning test
Rey auditory verbal learning test
Learning and
memory: visual
Brief visuospatial memory test-revised
(BVMT-R)
Processing speed Symbol digit modalities test
Trail making test
Controlled oral word association test
Paced auditory serial addition test
Wais-III digit symbol test
Executive function Stroop color word test
Tower of London–Drexel
Attention WAIS-III digit span
Word fluency Controlled oral word association test
(COWAT)
neuropsychological measures do not indicate a return to baseline functioning within
10 days, further observations may be conducted at weekly intervals thereafter. A
new baseline is then collected a minimum of 2 weeks following full recovery
(Webbe & Barth, 2003). With children, longer-term assessments may be needed to
determine whether mild head injury results in significant impairment in children’s
social or academic functioning as well (Yeates & Taylor, 2005).
Because of the large number of athletes who may have to be tested and the
limitations on hours of availability, neuropsychological batteries used in sports neu-
ropsychology are generally briefer than might be used in normal clinical practice.
Forty-five minutes to an hour is the typical time frame. The batteries consist of tests
that measure critical domains of functioning known to be at risk for impairment
following TBI. Thus, processing speed, memory, and executive functioning have
priority for assessment (Gronwall, 1989).
Measures of effort often are required in non-sport settings to insure validity of
outcome. Effort has generally not been considered a critical factor with athlete
examinees (Lovell et al., 2004). Instead, faking good is the more likely outcome
since most athletes aim to return to play as soon as possible. Thus, even with
nonstandard testing conditions, motivation is high. Nonetheless, with the advent
of high profile sport-concussion injuries and the undoubted potential for liabil-
ity claims, good practice suggests that assessment of effort (either through direct
testing or a process approach) should become commonplace in the sport neu-
ropsychology setting. A greater historic problem in the post-concussion testing
is that of repeated measurement. Following a suspected concussion, anywhere
from one to five assessments may occur within a 2-week time frame. Thus, it is
most important to understand the re-test validity of the measures selected, and to
make provision for positive change as a function of testing. As an example, sim-
ply scoring near the baseline level in post-trauma follow-up testing may represent
impaired performance once the expected improvement due to practice is factored
188 F.M. Webbe
into the equation. Sport neuropsychology researchers have contributed considerably
to studies of reliable change in computerized testing and laid the groundwork for
clinical interpretation of repeated tests (Parsons, Notebaert, Shields, & Guskiewicz,
2009).
Computerized Instruments for Concussion Management
Computer-based and Web-based neurocognitive assessments, such as Immediate
Measurement of Performance and Cognitive Testing (ImPACT; Maroon et al.,
2000), Automated Neuropsychological Assessment Metrics (ANAM; Reeves,
Kane, & Winter, 1995; Reeves, Thorne, Winter, & Hegge, 1989), CogSport (Collie,
Darby, & Maruff, 2001), and the Concussion Resolution Index (CRI; Erlanger et al.,
2001), have made possible the baseline approach with entire teams and leagues.
Computerized assessments sample domains of brain functioning such as reaction
time, speed of processing information, attention and concentration, memory, and
cognitive flexibility. Computerized testing allows examiners to assess common
sequelae of concussion, including subtle changes in processing speed to the millisec-
ond. Computerized measures also reduce the impact of practice effects by providing
multiple equivalent forms of the test. The reliability and validity of these tests
for use in the concussed athlete have been established (Schatz & Zillmer, 2003).
The complicated task for the sport neuropsychologist is determining whether cur-
rent measures of performance represent deviation from baseline, and with children,
whether the measures are within a normal range for a developing brain.
Resolution of Symptoms: The Normal Recovery Curve
and Complications
Length of Recovery
Barth et al. (1989) showed that the majority of college-aged individuals who suf-
fered mild head injury showed complete resolution of cognitive symptoms after
5–10 days. However, individuals who had sustained multiple concussive or even
sub-concussive blows had a slower recovery from post-concussive symptoms. In
a follow-up study, Alves (1992) i ndicated that physical symptoms of concussion
usually diminished with time, and completely resolved by 3–6 months post injury.
Most studies in the ensuing 20 years have supported the original Barth et al. (1989)
findings, suggesting that 85% or so of concussed athletes likely recover cognitively
within 1–2 weeks (Webbe & Barth, 2003). Nonetheless, the 15–30% or more of indi-
viduals who take longer to recovery represent a significant minority, and frequently
this group may be forgotten within the usual generalization (Ruff, Camenzuli, &
Mueller, 1996; Sterr, Herron, Hayward, & Montaldi, 2006). For children, the picture
is even cloudier. Moser & Schatz (2002) showed that post-concussive symptoms
11 Sport Neuropsychology and Cerebral Concussion 189
persisted for weeks or months in some youth athletes who had suffered multiple
concussive or sub-concussive blows.
The factors that predict which athlete will have a quick versus long symptom res-
olution have not been identified clearly and unambiguously. Two athletes who suffer
similar mild head trauma may differ widely in their recovery and return to play
despite no obvious differences in injury mechanics, diagnostic imaging, and side-
line symptoms including presence or absence of LOC and PTA. Some of the factors
that have been identified as important in understanding duration of recovery and
(development and resolution of post-concussion syndrome) include (a) history of
previous concussions (Moser et al., 2005), (b) premorbid learning disorders (Collins
et al., 1999), (c) psychological and emotional distress (Bailey, Samples, Broshek,
Freeman, & Barth, 2010; Hutchison, Mainwaring, Comper, Richards, & Bisschop,
2009; Ruff et al., 1996), (d) genetic characteristics such as the APoE-e4 allele
(Kutner, Erlanger, Tsai, Jordan, & Relkin, 2000), and (e) number and recency of
previous concussions (Erlanger, Kutner, Barth, & Barnes, 1999; Guskiewicz et al.,
2003; Macciocchi, Barth, Littlefield, & Cantu, 2001). In the absence of clear phys-
ical data, the severity and duration of symptom involvement remains the clearest
estimate of severity.
Return to Play
Severity of concussion is often a post hoc determination based upon the persistence
of concussion-related symptoms. The earliest definitions and conceptualizations of
concussion concluded that recovery was quite rapid. The vestige of that concept
remains in sport when players attempt to reenter games as quickly as possible or
deny any recurring symptoms that might persuade others to keep them from return-
ing to play. Similarly, coaches want their players t o resume training and playing as
soon after concussion as possible. Studies of athletic head injuries most typically
report on immediate, short-term, and long-term outcomes for recovery of normal
cognitive function and resolution of physical symptoms such as headache and nau-
sea. Assuming that an immediate, sideline judgment has been made that a player
suffered a concussion, then subsequent neurocognitive assessments are typically
initiated within 24 h in this model. If symptoms still are present, then additional
measurement is likely after 3–5 days, and again at 7–10 days. If symptoms and/or
cognitive functions have not returned to normal within 10 days, it would be common
to make further observations at regular intervals until symptoms have resolved and
the player returns to his/her baseline neurocognitive function. The somatic symp-
toms that predominate during t hese times are headache, confusion or disorientation
(often called fogginess), which are reported by about 50–75% of the athletes (Barth
et al., 1989; Macciocchi et al., 1996; McCrea et al., 2003). Thus, what remains
to be fine-tuned is to correlate the underlying functional and/or structural changes
with recovery from concussion. Although it is tempting to speculate, for exam-
ple, that hypometabolic alterations are responsible for symptoms in the short-term,
and that lasting cytoarchitectural alterations and/or cellular morbidity controls more
190 F.M. Webbe
Table 11.5 Zurich conference consensus recommendations for graduated return to play protocol
(McCrory et al., 2009)
Rehabilitation
stage
Functional exercise at each stage
of rehabilitation Objective of each stage
1. No activity Complete physical and cognitive rest Recovery
2. Light aerobic
exercise
Walking, swimming or stationary cycling
keeping intensity 70% MPHR; no resistance
training
Increase HR
3. Sport-specific
exercise
Skating drills in ice hockey, running drills in
soccer; no head impact activities
Add movement
4. Non-contact
training drills
Progression to more complex training drills,
e.g., passing drills in football and ice hockey;
may start progressive resistance training
Exercise, coordination,
and cognitive load
5. Full contact
practice
Following medical clearance, participate in
normal training activities
Restore confidence and
assess functional
skills by coaching
staff
6. Return to play Normal game play
persisting symptoms, data from human studies still are insufficient to support such
conclusions.
So, when should athletes return to play? The conservative approach is the most
followed, which dictates, (a) resolution of physical symptoms as determined by
self-report and informant observation, (b) clear neurological examination results,
(c) neurocognitive test data showing return to or maintenance of premorbid func-
tioning, and (d) balance testing and additional other protocols showing baseline
performance. The Zurich Conference in 2007 also provided the following guide-
lines and objectives, shown in Table 11.5, for a graduated return-to-play once the
above conditions are met. Previous return-to-play guidelines such as those described
alongside and including ratings of concussion severity (Cantu, 1986, 2001; Kelly &
Rosenberg, 1997) combined the severity grade with previous concussion history and
resolution of symptoms to determine when it was safe to return. The main problem
with those earlier systems was that loss of consciousness was a primary factor in
determining severity or grade of concussion. More recent studies have demonstrated
that LOC occurs in fewer than 10% of the instances of sport-related concussion
(Sosin et al., 1996), and that LOC is not overly related to severity, or predictive of
symptom resolution ( Lovell, Iverson, Collins, McKeag, & Maroon, 1999; McCrea,
Kelly, Randolph, Cisler, & Berger, 2002).
Effects of Repetitive Head Trauma
With alarming frequency over the past several years, reports of cumulative effects
of repetitive sub-concussive and concussive events in current and former ath-
letes have been headline news. In 2007, the professional wrestler Chris Benoit
11 Sport Neuropsychology and Cerebral Concussion 191
murdered his wife and son and then hanged himself. In 2006, 12 years after retir-
ing from professional football, defensive back Andre Waters shot himself in the
head. Twenty-one-year-old University of Pennsylvania linebacker Owen Thomas
was found dead on April 27, 2010, another suicide victim. Hall of fame center Mike
Webster died homeless in 2002, a victim of progressive dementia before he was 50.
Former English Premier League soccer player Jeff Astle died in January of 2002 at
age 59. “Astle couldn’t remember anything about the game he loved, or even the
names of his grandchildren. A coroner ruled: ‘it was heading the soccer ball that
had killed him’” (Wallace, 2002). What may link the tragic end of all these athletes
was a history of repetitive head trauma. This link cannot determine that brain injury
was responsible for the abnormal behavior, but the likelihood of a link appears more
than hypothetical.
McKee, Stern, and colleagues at Boston University, the Bedford Veterans
Administration Medical Center, and the Sports Legacy Institute, have reported a
part of the data from their developing brain bank of athletes from contact sports
(including Benoit, Waters, and Thomas). The outcomes that they have reported
have been surprising and disturbing. First was the report that former professional
athletes, primarily from football, exhibited a brain pathology, chronic traumatic
encephalopathy (CTE), that was consistent with that seen in former boxers and oth-
ers who had known histories of repetitive head injuries (McKee et al., 2009). As
the brain bank grew, these findings were extended to a few athletes who had com-
peted only in college football as well as to other professionals from the sports of
wrestling, hockey, and soccer. Even more recently, newer analyses have reported
that a majority of these subjects also had a proteinopathy distributed widely in
their brains, which further involved spinal cord and brain stem motor neurons,
and which correlated with the symptoms of primary progressive motor neuron dis-
eases such as amyotrophic lateral sclerosis (ALS; McKee et al., 2010). McKee,
Stern, and their colleagues point to the repetitive brain trauma suffered by these
athletes as the likely cause of the brain pathologies. Although there are a smatter-
ing of athletes representing sports other than ice hockey and American football,
only those two sports have sufficient participants in t he brain bank for prelimi-
nary conclusions to be drawn. Moreover, the self-selection bias present both in
volunteers and in postmortem donations by family members must be accounted
for ultimately with better-controlled methodologies. Even accounting for selection
bias, however, the consistency of findings clearly raises alarms regarding r epeti-
tive head insults and their life changing potentiality. Much is yet to be learned
about the extent of such pathological changes, for example, when they begin, if
they are common to all participants in sports where the brain is constantly banged
around, and if there are genetic or other idiopathic interactions. However, enough
appears to be known to issue caution to participants t o possibly alter their style
of play, and to administrators t o consider changing rules of play. Assisting ath-
letes in actually changing their playing behavior is a difficult and complex task
since pathogenic behavior may also be behavior that produces success on the field
or court.
192 F.M. Webbe
Educational Approaches in Sport Neuropsychology
In its “Heads Up” series, the Centers for Disease Control (CDC; 2010)haveled
the way in stimulating education on concussion for sport participants, parents,
physicians, and youth/high school coaches. More r ecently, the National Academy
of Neuropsychology and the National Athletic Trainers Association (NATA) have
taken the lead among professional organizations in devoting time and resources to
public education on concussion in general, and sport-related concussion in particu-
lar. In both written materials and in educational DVDs, these organizations, with the
sponsorship of the NHL, the NHL Players Association, and the NFL have carried
this educational message to youth, adult, and professional players, coaches, trainers,
team management, and the general public (NAN, 2009). Such efforts are critical to
increasing the recognition of concussions when they occur, and also in recognizing
and preventing conditions that produce concussions. These efforts also are key in
bringing the discussion and the science down to amateur and youth levels, the least
regulated and loosest organized sport entities. Moreover, although the i mpact of
concussive brain injuries on children may often be more pronounced than in adults,
with longer times to recovery (e.g., Moser & Schatz, 2002), guidelines for returning
youth athletes to play are mostly nonexistent (Moser et al., 2007).
In addition to actual and proposed governmental legislation, key athletic orga-
nizations and associations also have begun proaction in the area of concussion
in sport. Both the NFL and the NHL have ramped up their existing concussion
monitoring programs. The NFL, in particular, has adopted a new attitude whereby
concussion is not a topic to be mentioned in whispers. Locker room posters, infor-
mational literature for players and families, and stricter policies on removal and
return-to-play have all appeared in 2010. Similarly, the National Collegiate Athletic
Association ( NCAA) now has mandated that its member institutions develop con-
cussion management plans that include preseason baseline testing for sports where
concussion risk is significant, documented return-to-play guidelines in the event of
a concussion, and a mandatory education program for student-athletes and coaches
(NCAA, 2010).
These public policy changes are both exciting and gratifying for neuropsychol-
ogists who have been publicizing the possible downside of playing contact sports
without proper safeguards and monitoring of participants. However, in the vein of
being careful of what one wishes for, there now may be more pressure on the neu-
ropsychology discipline not only to provide the testing services required by these
policies, but to demonstrate their worth. A very real opportunity for enhancing edu-
cation and prevention of concussion is readily available at the level of youth sports.
Youth organization administrators would benefit from education about the preven-
tion of head injuries so that they would understand the importance of allocating
funding toward acquiring qualified trainers to monitor youth games in the event that
concussions take place. Due to the limited availability of athletic trainers at youth
games currently, referees and game officials are frequently relied upon to identify
injuries. Unfortunately, concussion has many subtle signs and symptoms that offi-
cials may not be aware of; therefore, they may not stop game play or appropriately
11 Sport Neuropsychology and Cerebral Concussion 193
remove these youth from play. Thus, education and training specific to concus-
sion should be targeted at youth referees. Safety information would also extend
to the environment in which these youth athletes are playing in order to ensure that
equipment and other hazards are taken out of the sports arena.
Because children may report many different symptoms after a head injury, it
is important for neuropsychologists to also educate coaches and parents about
concussion-like symptoms in order to be alert for an early identification of
injury. Parents and coaches may have an incomplete knowledge of common post-
concussion symptoms, expecting only self-evident problems such as amnesia,
confusion, headache, and dizziness. It is also important to explain emotional reac-
tivity and other more subtle symptoms that may arise from rather mild head impacts
such as heading a soccer ball as well as other more serious injury sources. This is
critical as McLeod, Schwartz, and Bay (2007) highlighted several misconceptions
that youth coaches have regarding sports-related concussion. They found that 42%
of coaches in their sample (N = 250) believed that loss of consciousness was needed
for a concussion to have happened, 32% did not believe that a Grade 1 concussion
required removal from a game, and 26% reported that they would let a symptomatic
athlete return to play. The CDC’s Heads Up in Youth Sports toolkit mentioned earlier
is a free resource provided by the CDC that is useful in disseminating concussion
related information to parents, players, and coaches. It includes educational mate-
rials such as a video, wallet card listing signs/symptoms, posters, fact sheets, and
other concussion-related resources.
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Chapter 12
Aggression in Competitive Sports: Using
Direct Observation to Evaluate Incidence
and Prevention Focused Intervention
Chris J. Gee
The term aggr ession has developed into something of an umbrella construct, in both
its social and academic applications (Widmeyer, Dorsch, Bray, & McGuire, 2002).
For example, pushy and persistent salespeople are often referred t o as aggressive, as
are baseball players who run the bases exceptionally hard and sacrifice their bodies
for the betterment of their teams. Unfortunately, neither of these examples reflects
the academic conceptualization of the term. As such, before moving further into
this chapter, I want to clarify the meaning of aggression within the context of the
behavioral sciences.
Within the sport psychology literature, aggressive behavior is defined as “any
overt act (verbal or physical) that has the capacity to cause psychological or physi-
cal injury to another. The act must be purposeful (non-accidental) and chosen with
the intent of causing harm” (Stephens, 1998, p. 277). These behaviors for the most
part fall outside of the formal rulebook (as most sports penalize intentionally harm-
ful behavior), meaning that tackling in rugby and football, and body-checking in
ice hockey, are not the primary behaviors of interest. Rather, aggressive behaviors
reflect those actions that go above and beyond the strategic physical contact allowed
by many sports and are reflected in those behaviors in which the transgressor
intentionally tries to harm their opponent.
Apropos the preceding discussion, ice hockey is frequently heralded as the gold
standard sport through which sport-specific aggression is studied and understood.
Yet despite numerous and multidisciplinary research endeavors concerned with
hockey aggression, our current understanding of the etiology of these behaviors
is still incomplete and unreliable (Coulomb & Pfister, 1998; Gee & Sullivan, 2006;
Gee, 2010a; Kirker, Tenenbaum, & Mattson, 2000; Stephens, 1998). Many of the
criticisms directed toward the sport aggression literature have been methodologi-
cal in nature and have subsequently forced academics to reevaluate the utility and
validity of how aggressive behavior is currently being operationalized. This process
of critical reflection has spawned several methodological alternatives that address
many of these perceived limitations.
C.J. Gee ( B)
Department of Exercise Sciences, University of Toronto, Toronto, ON, Canada M9W 5Z8
e-mail: [email protected]
199
J.K. Luiselli, D.D. Reed (eds.), Behavioral Sport Psychology,
DOI 10.1007/978-1-4614-0070-7_12,
C
Springer Science+Business Media, LLC 2011
200 C.J. Gee
As is indicated by the title of this chapter, behavioral observation is useful for
studying sport-related aggressive behavior. In the following sections, I review obser-
vational methodologies for studying sport-specific aggression and comment about
their strengths and contribution to behavioral sport psychology.
Previous Methodologies Employed to Study
Sport-Specific Aggression
Overwhelmingly, research concerned with the etiology of aggressive behavior in
sport has been carried out using one of two methodologies: self-report or archival
(penalty records). Both methodologies have strengths as well as limitations.
Self-Report Methodology
Studies utilizing self-report instruments (i.e., questionnaires) have predominantly
been concerned with assessing athletes’ attitudes and legitimacy perceptions as they
relate to the use of aggressive behavior in sport (Stephens, 1998). Some of the
most widely used instruments are (1) Sport Behavior Inventory (SBI; Conroy, Silva,
Newcomer, Walker, & Johnson, 2001), (2) Judgments About Moral Behavior in
Youth Sport Questionnaire (JAMBYSQ; Stephens, Bredemeier, & Shields, 1997),
and (3) Bredemeier Athletic Aggression Inventory (BAAGI; Bredemeier, 1978).
The implicit assumption underpinning self-report research on aggressive behavior
is that an athlete’s responses on these assessments reflect validly how they would
actually behave during competition. Unfortunately, this assumption is not supported
empirically, as many of these instruments have never been tested for construct valid-
ity (Gee, 2010b; Stephens, 1998). In those few instances where a valid measure of
within-competition behavior has been included (e.g., penalty records), researchers
have reported weak, and even negative, correlations between athletes’ self-reported
aggressive tendencies and their actual within-competition behaviors (Gee, 2010b;
Loughead & Leith, 2001; Worrell & Harris, 1986). Consequently, the validity of
employing self-report instruments to study aggressive behavior in sport, especially
as the sole dependent variable, has been routinely questioned and criticized (Gee &
Sullivan, 2004; Gee & Sullivan, 2006; Kirker et al., 2000; Sheldon & Aimar, 2001;
Stephens, 1998).
Poor ecological validity associated with self-report instruments may explain
why there is a disconnect between athlete’s self-report scores and their actual
overt behavior (Bredemeier & Shields, 1984, 1986a, 1986b; Gee & Sullivan,
2006; Stephens, 1998). Predominantly, these research studies have queried ath-
letes about their aggressive beliefs and/or attitudes at home or during a practice
situation, as to not interfere with a team’s competitive preparation. However, as
Bredemeier and Shields (1986a) state, “patterns of moral reasoning in sport dif-
fer from the patterns of moral reasoning that are used in most other aspects of life.
Movement into the sport world involves a transformation of moral meaning” (p. 20).
12 Direct Observation of Aggression 201
These authors speculate that the normative moral code present within competitive
hockey, and other competitive sports, coupled with other stimuli that are specific
to this social context (e.g., emphasis on athletic competence, “win-at-all-costs”
mentality, hypermasculine ideals), are all determinants of sport-specific aggressive
behavior and cannot validly be replicated in other environments. As a result, it is
quickly becoming consensus within the academic community that studies concerned
with the etiology of aggressive behavior must include these and other ecological
influences in order to obtain a valid and reliable assessment of a particular athlete’s
competitive deportment (Gee & Sullivan, 2006; Gee, 2010a, 2010b; Kirker et al.,
2000). As such, studies that continue to employ self-report assessments as the sole
dependent measure should, at the very least, have participants complete the assess-
ment within the context of interest. From a best practices perspective, self-report
assessments should be i ncluded as part of a mixed methods approach whereby they
are used to assess attitudinal and perceptual constructs, but are not the dependent
proxy measure of aggression.
In summary, self-report instruments allow for large populations to be assessed
expeditiously, and they provide researchers with insight into the aggressive
thoughts, attitudes, and perceptions of athletes. Unfortunately, due to the unique
climate of competitive sport, self-report data by athletes outside of this context may
not accurately reflect their overt aggressive behavior. Accordingly, when self-report
data are used as a proxy dependent measure of sport-specific aggression, the validity
and reliability of the research findings must be questioned.
Archival Methodology
By far the most widely employed methodology for studying sport-specific aggres-
sion has been official penalty records during games. Aggressive behaviors within
this framework are operationalized as those acts that violate the formal rules of the
game and are subsequently punished by trained game officials. Using this approach,
an athlete’s actual within-competition behaviors are now being directly measured,
which subsequently reduces concerns about the ecological or external validity of
the results as compared to self-report data. This final point is extremely important
because of the comments made earlier about the unique contributing factors present
within the context of competitive sport. Consequently, because competitive sport is
such a unique social context, it is important that the behavioral criterion of interest
be observed and extrapolated directly from this environment (Russell & Russell,
1984; Vokey & Russell, 1992).
Studies that have relied on archival penalty records have also been heavily cri-
tiqued on ecological grounds (Gee & Sullivan, 2004; Kirker et al., 2000; Sheldon
&Aimar,2001). However, unlike self-report studies that have been criticized based
on the generalizability of the research context, archival designs have been targeted
for a different violation of ecological validity (Schmuckler, 2001). For example,
because of the speed of competitive sports such as ice hockey, coupled with the
multiple responsibilities allocated to each game official (e.g., issue penalties, line
202 C.J. Gee
infractions, judge legitimacy of goals), it has been widely argued that a large pro-
portion of aggressive behaviors go unnoticed during a competitive contest (Bar-Eli
& Tenenbaum, 1989; Gee & Sullivan, 2006; Kirker et al., 2000; Mark, Bryant, &
Lehman, 1983; Sheldon & Aimar, 2001). Moreover, it has also been suggested that a
game official’s decisions to penalize particular behaviors may be influenced by such
factors as the game location, score differential, crowd reaction, and time remain-
ing in the contest (Greer, 1983; Jones, Bray, & Olivier, 2005; Nevill, Balmer, &
Williams, 2002). It is not uncommon, for instance, to hear that the refs have “put
away their whistles” or that they “are letting the teams play” when an important
game is close in score. In such situations, game officials do not want to become the
deciding factor, and therefore, they penalize only those behaviors that are flagrant
or potentially injurious (Gee & Sullivan, 2006). Nevertheless, several behaviors that
adhere to the conceptual definition of aggression (e.g., possess the intent to cause
harm) occur during these periods of non-penalization, which subsequently detracts
from the representativeness of the sample of behaviors collected using archival
methodology.
In contrast, certain acts of aggression are volitionally overlooked by game offi-
cials, as they are considered “typical” within the sport and part of the “unwritten
rulebook.” As an example, in ice hockey a player who digs for the puck once the
goalie has it covered will routinely be pushed or punched by several defensive play-
ers after the whistle. Such reciprocation has become part of the game and has come
to be expected as punishment for poking at a t eam’s goalie.
When combined, the result of missed and overlooked infractions by game offi-
cials has the potential to significantly distort our understanding of the frequency
and distribution of aggressive behavior in sport. As an illustration, Gee and Sullivan
(2006) found that 69% of the infractions among Junior B (15–18 years old) hockey
players met the operational criteria in a study of aggressive behavior but ultimately
went unsanctioned by game officials. So when two-thirds of the observations com-
prising the dependent variable fail to be accounted for, one has to be highly critical
of the validity and reliability of the eventual findings. In effect, very different
conclusions could be drawn from a competitive sport event depending upon the
methodology that was used to collect the behavioral data.
Direct Observation as Method for Obtaining a Valid Measure
of Aggressive Behavior
In an attempt to overcome the limitations cited in the previous section, researchers
have sought viable methodological alternatives. Behavioral observation is one
method that has received recent empirical attention. It is a descriptive technique
in which participants’ behaviors are observed and coded within their natural setting
(Thomas & Nelson, 2001). With respect to aggression, Kirker et al. (2000) stated,
“the observation of game behavior in real time and the context in which it occurs
provides the best opportunities to understand the dynamics of aggressive behavior in
12 Direct Observation of Aggression 203
sport” (p. 376). Consequently, direct observation has several features for capturing
valid, reliable, and ecologically valid behavioral data.
First, direct observation can be conducted from videotape, allowing researchers
to stop, rewind, and pause the competitive action and isolate specific behaviors. This
more static, thorough, and objective process has the potential to significantly add to
the validity of the overall assessment process and thus to the comprehensiveness
of the overall sample of behaviors measured. Many direct observation techniques
within sport have actually opted to use multiple cameras in order to investigate sev-
eral influential factors simultaneously (Gee & Sullivan, 2003; Kirker et al., 2000;
Teipel, Gerisch, & Busse, 1983), as well as having multiple coders analyze the data.
This methodology has the potential of giving a more holistic account of aggres-
sion during competition. Furthermore, it permits researchers to assess interrater
agreement, a feature not present within previous archival research (Gee, 2010a).
By having multiple independent coders analyzing the data through video analysis,
behavioral observation can more effectively document the high frequency of missed
and overlooked calls that currently plague the archival approach.
One of the most dramatic improvements in measuring aggressive behavior with
direct observation is its ability to encompass all aggressive infractions, especially
those that have become part of the “unwritten rulebook.” As several of these actions
adhere to the conceptual and operational criteria of aggression, including them is
necessary for ensuring the validity of the dependent measure. The ability for the
researcher to code the aggressive behaviors outside of the competitive atmosphere
certainly aids in this pursuit. As was mentioned earlier, some of the concerns sur-
rounding the accuracy of archival data pertain to the pressures and environmental
influences that often impact game officials’ decisions. Referencing Gee and Sullivan
(2006) again, their research found that a large number of aggressive behaviors dur-
ing ice hockey games were overlooked when the score differential was relatively
small (e.g., teams are one or two goals apart), presumably because the game offi-
cials did not want to be the deciding factor in the competitive contest. Moreover,
game officials must also consider the potential crowd reaction to their within-game
decisions and thus balance adherence to the rulebook with overall game and crowd
control. Finally, a practical concern for game officials is that they must stop the game
in order to assess a particular penalty infraction. Consequently, if a game official was
to penalize all behaviors that violated the formal rulebook, it is plausible that some
sporting events would take days, rather than hours, to complete. This simple real-
ity introduces a source of motivation to avoid calling infractions and could detract
from the validity of the sample of behaviors collected. Direct observation allows
researchers to be more objective about whether or not a certain infraction should be
included, as they do not face the same consequences or environmental factors as do
game officials. Overall, when aggressive behaviors are recorded through videotaped
observation, the sample of behaviors should be more valid and generalizable (Gee
& Sullivan, 2006; Gee, 2010a).
Another methodological advantage of direct observation is being able to ana-
lyze and incorporate verbal aggression. As Kirker et al. (2000) concluded in their
observational study of men’s basketball and ice hockey, negative verbalizations
204 C.J. Gee
directed toward opponents and game officials are the most frequent form of within-
competition aggressive conduct. Verbal aggression, in fact, may act as a catalyst
for more severe, overt acts of aggression routinely observed within competitive
sport. Previous methodologies have not included verbal aggression, making such
assessment a fruitful line of research for the future. Consequently, a comprehensive
understanding of verbal aggression within the competitive environment is desirable.
Overall then, there are two primary advantages associated with the direct obser-
vation of sport-related aggression. First, researchers can assess athletes’ aggressive
behavior within the unique social context of competitive sport. Doing so maximizes
ecological validity. Second, direct observation has the potential to yield a more valid
and reliable measure of aggression.
Behavioral Observation and Measuring “Intent”
As was mentioned in the introduction, the defining characteristic of an aggressive
infraction is the transgressor’s “intent to harm.” Previous methodologies have failed
to properly measure an athlete’s intent, opting instead to infer intent through other
cognitive or behavioral measures. Intent is obviously a cognitive construct, and
therefore, it cannot be readily observed by a third party. As such, similar to criti-
cisms levied against the archival approach (Kirker et al., 2000; Sheldon & Aimar,
2001), current observation methodologies also possess an inherent inferential bias.
Nevertheless, there are adjunct methodologies that, when employed as part of a
mixed methods approach alongside behavioral observation, could synergistically
address this limitation in the future.
First, direct observation allows the researcher to follow particular players across
the span of a game. In doing so, researchers become privy to circumstances and
incidents within the game that provide insight into or context to events that transpire
later in the game (Katorji & Cahoon, 1992). This feature is particularly important
as Widmeyer et al. (2002) suggest that provocation and rivalries are two impor-
tant determinants of athlete aggression. This contextual information can reveal a
player’s behavioral intentions, when understood and evaluated within the context of
the larger game. This strategy is not a comprehensive solution to measuring intent,
but it highlights how direct observation moves us closer to this pursuit.
Secondly, researchers have recently begun to use retrospective stimulated recall
interviews following observational assessments in an attempt to directly assess ath-
letes’ intent (Kirker et al., 2000; Shapcott, Bloom, & Loughead, 2007). Players are
shown a clip of their aggressive behavior and asked to explain why they engaged
in the particular act. This approach advances the study of aggressive behavior
and reflects yet another advantage associated with using a videotaped observation
methodology. Notably, stimulated recall interviews will be especially beneficial with
“gray area” infractions where an athlete’s intent cannot be readily inferred from
his/her overt actions.
Finally, creating customized lists of athletes’ self-reported aggressive behav-
iors is an adjunct strategy worthy of future attention (Gee, 2010b). Similar to the
12 Direct Observation of Aggression 205
methods employed by Widmeyer and Birch (1984) and Widmeyer and McGuire
(1997), where coaches, referees, and players were asked to list the behaviors that
they employed with the intent to harm an opponent, researchers could under-
take a similar process at the level of the individual athlete. When combined with
direct observation, researchers could assess each player’s within-game deport-
ment, while only including those infractions that the athlete reported as aggressive
(see Widmeyer & Birch, 1984, for a more detailed description of this a priori
methodology). As with all of the previously cited strategies, some degree of error
exists in trying to control for intent. Ultimately, however, methods and future
designs should strive toward accounting for and controlling for this error whenever
possible.
Direct Observation as a Method for Studying the Acquisition
of Aggressive Behavior
Up to this point I have primarily discussed behavioral observation as a data
collection tool for capturing within-competition incidents of aggressive behav-
ior. However, the unique methodological properties and rich qualitative insight of
behavioral observation also make it a strong choice when attempting to address
broader lines of research concerned with the acquisition of aggressive behavior. In
this section, I review how direct observation can be used to study the socialization of
aggressive behavior and how researchers can address perceived barriers to obtaining
valid information from parents and sport coaches.
A primary line of research in the study of sport-specific aggression is how
athletes learn and ultimately exhibit such behavior. By focusing on an athlete’s pri-
mary social group within the athletic domain (i.e., parents, coaches, teammates),
researchers have sought to establish how these important socializing agents trans-
mit information about aggressive behavior and subsequently how athletes behave
accordingly. Research to date suggests that many athletes believe that their parents
and coaches approve of aggressive conduct within the context of competitive sport
(Faulkner, 1974; Smith, 1979; Vaz & Thomas, 1974). However, this research has
not explained how these perceptions are ultimately formed. Unfortunately, we do
not fully understand how and why parents and coaches teach athletes to be aggres-
sive (both inside and outside of the sporting domain) and, subsequently, how and
why they reinforce persistent aggressive behavior.
I believe that in order to answer the aforementioned questions, a candid look
inside the dressing room, behind the bench, in the stands, and in the car ride home
is likely required (Gee, 2010b). Michael Smith, a prominent scholar in Canada,
undertook a large-scale qualitative study into the etiology of aggressive behavior
in Canadian youth ice hockey in the late 1970s. Through this project Dr. Smith was
able to gain access to several of these “behind the scene” areas and was subsequently
privy to the types of conversations believed to be central to our understanding
of the socialization process. For example, Smith (1979) overheard a bantam-level
(13–14-year-olds) hockey coach state the following:
206 C.J. Gee
Look, if this character starts anything, take him out early. We can’t have him charging
around hammering people. Somebody’s going to have to straighten him out. Just remem-
ber, get the gloves off and do it in a fair fight. If you shake him up early he can’t keep it
up. Besides, it’s best to take penalties early in the game before we get too tired to kill them
effectively (p. 108).
While in the lobby after another youth hockey game, Smith (1979) overheard one
parent say to another, “Boy! little Ian isn’t afraid to hit” (p. 80). When little Ian
emerged from the dressing room, his father remarked, “looks like we have a little
Tiger Williams on our hands” (p. 80). Unfortunately, Smith’s research was predom-
inantly qualitative, and therefore, even though it provided great insight into the
content of these behind-the-scene messages, it failed to examine their effect over
athletes’ actual within-competition behavior. Consequently, future research should
study the actions and verbalizations of these primary socializing agents, but do so
in conjunction with other key intrapersonal factors and ultimately against athletes’
within-competition use of aggression.
One of the distinct advantages that observation methodologies have in the above
pursuit, especially if they are conducted without the participants’ knowledge, is
overcoming social desirability. Previous investigations concerned with the influence
of coaches and parents on athletes’ aggressive behavior have frequently reported
results contrary to common hypotheses. These findings, or the lack thereof, are
believed to reflect the tendency for adult participants queried about aggressive
behavior to give “appropriate” responses (Gee, 2010b; Givvin, 2001; White, 2007).
With the negative media stereotypes depicting the “crazed sporting parent” and the
“win-at-all-costs coach,” it is understandable why these adult participants might
respond in a self-effacing manner. Nevertheless, this source of error makes it dif-
ficult to obtain valid and reliable data from these groups. I recommend that direct
observation should focus on how parents and coaches behave within the competitive
context, especially prior to and following aggressive transgressions.
The Role of Behavioral Observation in Prevention-Based
Intervention
This final section describes how behavioral observation can inform future
prevention-based intervention, including recommendations for parents, coaches,
game officials, and athletes.
Coaches/Parents
Currently within amateur sport, there are initiatives directed toward parents/coaches
aimed at curbing aggressive conduct. These initiatives oftentimes revolve around
educating parents and coaches about their influence on young athletes’ behavior.
In many cases, parents and coaches are mandated to attend educational seminars
before the season and asked to sign “Good Behavior” contracts. Unfortunately, as
I stated earlier, there appears to be a disconnect between what parents and coaches
12 Direct Observation of Aggression 207
say they will do and what actually transpires when the competitive whistle blows.
In line with Bredemeier and Shields’s (1986a) assertion around the contextual
nature of morality, parents and coaches often denounce aggressive conduct when
queried about it in a classroom setting, yet send very different messages about
the legitimacy of aggressive behavior when immersed within the competitive cli-
mate (Goldstein & Iso-Ahola, 2008). Again, there appears to be something quite
unique about competition (e.g., emotion, competition, different norms, and moral
code) that causes parents and coaches to reinforce and legitimize behaviors that
they would not otherwise reinforce in other circumstances (Bryant, 1989; Bryant,
Zillman, & Raney, 1998; Raney, 2006). To reiterate, these behaviors should be mea-
sured directly within the social context of interest. This, of course, is where an
observational method could prove to be effective. All of us have seen ourselves
on camera and thought “Is that how I behave/sound/look like to other people”? This
reflective process can affect the way people see themselves and ultimately their
future behavior, especially if the images caught on tape are less than flattering (Tice
& Wallace, 2003). Therefore, by employing video surveillance during youth sport-
ing events, sporting administrators would have not only a method for monitoring
spectator and coach behavior, but also a methodology for addressing incidents post
hoc with those individuals involved. Using actual camera footage of the incident
could have a substantial impact on the learning and eventual behavioral change
that takes place following a particular incident. Moreover, having actual footage
of the incident would remove any of the hearsay and ambiguity that potentially
cloud the reprisal process. The cameras would clearly show what transpired, mak-
ing the punitive process much more “cut and dry.” Placing cameras at youth sporting
events would also likely serve as a deterrent, forcing spectators and coaches to be
more cognizant of their behavior when attending these events. Similar observational
methodologies have been employed with coaches in the past; however, these tapes
were subsequently used for coaching development purposes (Trudel, Cote, & Danz,
1996). Nevertheless, the individualized nature of the approach has advantages when
it comes to changing future behavior.
Game Officials
Observational methodologies may also have a place in educating game officials.
Actual game tapes could be used to exemplify particular circumstances or trends that
have been shown to facilitate violent or aggressive episodes, allowing officials to see
in real time how these events transpire. Moreover, similar to the process employed
in many professional sports, videotape analysis can be used to show individual game
officials where certain mistakes were made throughout the game. In accordance with
the statements made in the previous subsection, having these events presented in a
video format removes much of the hearsay involved with these situations and can
often facilitate quick and long-lasting behavioral changes. Previous research into
the etiology of aggressive behavior has identified the quality and consistency of
officiating as a potential determinant of aggressive behavior (Pascall, 2000). It is
208 C.J. Gee
hypothesized that game officials have the ability to set the tone for the game, but
can also introduce unwanted frustration and emotion into the competitive contest
by being inconsistent in the way they penalize infractions. Including video analysis
may help ensure consistency in the way penalties are called and can also be used for
educational purposes at the individual level.
Athletes
Direct observation may also play an important role in curbing the use of aggressive
behavior at the level of the athlete. For example, in the early 2000s, in response to
a perceived spike in spinal cord and neck injuries among Canadian youth hockey
players, a l arge-scale educational campaign was launched to remove the proposed
determinants of these injuries (e.g., dangerous body checks from behind). A primary
component of this campaign was mandatory educational sessions for all athletes,
which included both on- and off-ice materials. Off-ice materials included video anal-
ysis of what constitutes a legal and illegal check moving forward, leaving very little
in the way of ambiguity. These lessons were subsequently reinforced during the on-
ice sessions through practical demonstrations. The literature suggests that athletes
often do not understand what is “too much” or illegal within their sport, in part
because officials call penalties inconsistently. As Gee and Potwarka (2007) suggest,
“a line between what is acceptable and what is not needs to be clearly defined ...
[and] athletes need to understand that there is an upper limit to what is acceptable
within the confines of the competitive atmosphere.” Consequently, video analysis
could be used as an educational tool to help athletes clearly understand what is
acceptable and unacceptable deportment. Moreover, if these parameters are consis-
tently reinforced by game officials, and the negative consequences associated with
rule violations are severe enough, profound behavioral changes would be projected
to follow. Such procedures have been used by professional sports when new rule
changes have been introduced during the off-season.
Summary and Conclusions
Direct observation, used for data collection and intervention planning, addresses
many concerns previously encountered in the study of sport-specific aggression.
Specifically, direct observation overcomes limitations with an archival approach,
including the large number of infractions that go unseen or uncalled by game offi-
cials. Consequently, by offering the opportunity to slow down, rewind, and pause
the competition action, while also allowing multiple independent coders to assess
the content, observation produces a more valid sample of aggressive behavior.
Moreover, direct observation gives researchers the opportunity to assess the com-
petitive climate in a more holistic fashion, assessing spectator, coach, and player
variables simultaneously. The ability to move the study of aggressive behavior from
the micro to the macro, as well as from the artificial to the ecological, represents a
significant step forward for this area of inquiry.
12 Direct Observation of Aggression 209
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Chapter 13
Behavioral Effects of Sport Nutritional
Supplements: Fact or Fiction?
Stephen Ray Flora
The pages of fitness, health, sport, and sport enthusiast (biking, running,
bodybuilding, etc.) magazines are filled with advertisements for various nutritional
supplements, all claiming to significantly improve the athletic performance of the
supplement user. These advertisements often include dramatic “before and after”
pictures showing greatly changed (improved) physiques that supposedly occurred as
a result of taking the advertised supplement. The advertisements may also include
pictures of individuals with impressively muscled physiques or pictures of famous
athletes who claim to have achieved great results with the advertised supplement.
However, “before and after” pictures are often altered. In some cases the pictures
may even be taken on the same day and then altered on the computer (widening
of the chest and shoulders, narrowing of the waist, airbrushing; Bell, 2008). In
advertisements, muscular models might take the advertised supplement, but they
also may ingest illegal steroids, use other products, and engage in other physique-
altering activities (Bell, 2008). Furthermore, athletes providing testimonials for
products are likely to be taking other nutritional supplements as well as participat-
ing in other performance-enhancing activities (physical training, getting proper rest)
that may account more for performance outcomes than does taking the advertised
supplement.
Deceptive advertising practices such as airbrushing pictures, and confounds
including effects of other consumed supplements, training habits, and nutritional
practices of athletes taking supplements, make it difficult to evaluate the effec-
tiveness, if any, of most nutritional supplements. A particular supplement might
enhance performance, have no effect, or in fact contribute to performance decre-
ments. Without controlled experimental analysis of the questioned supplement’s
effect on performance, it is impossible to know what effect, if any, the supplement
has on performance.
Another difficulty in evaluating the possible effectiveness of supplements is
that many of them contain ingredients and “proprietary blends,” making it nearly
impossible to ascertain which, if any, of the ingredients in a supplement have a
S.R. Flora (B)
Youngstown State University, Youngstown, OH, USA
e-mail: srfl[email protected]
211
J.K. Luiselli, D.D. Reed (eds.), Behavioral Sport Psychology,
DOI 10.1007/978-1-4614-0070-7_13,
C
Springer Science+Business Media, LLC 2011
212 S.R. Flora
performance-enhancing effect. Products use the cover of “proprietary blends” both
to hide the exact mixes and amounts of ingredients from potential copycat product
competitors and to blind consumers to the often miniscule amounts of the ingredi-
ents actually used. A general rule for the consumer of supplements is to avoid any
supplement that lists a “proprietary blend,” and if supplements are consumed, take
only supplements that list exact amounts of all ingredients that are purchased from
a r eputable source.
The ability to ascertain the effects of supplements is further hampered in the
United States as a result of the Dietary Supplement Health and Education Act of
1994 (DSHEA). The DAHEA put supplements in a separate category, limited the
Food and Drug Administration (FDA)’s ability to regulate supplements, gave sup-
plement companies wide latitude in the (often unsubstantiated) claims that could
be made about products, and exempted manufactures from having to submit safety
information before marketing products (Barrett, 2007). Furthermore, since products
are called “dietary supplements” rather than “drugs,” the FDA cannot guarantee
that the products are safe, much less effective. “Despite their sometimes-potent
pharmacological effects, dietary s upplements are now classified as foods and are
presumed to be safe unless the government can prove otherwise. Drugs, on the
other hand, must be proven s afe by the manufacturers before going on the market”
(Mencimer, 2001, p. 5). The DSHEA allows manufactures of dietary supplements
to advertise “statements of support” that claim benefit and describe well-being that
occurs from taking the products without any objective way to evaluate the claims
(Barrett, 2007).
The free rein given to producers of dietary supplements by the DSHEA occurred
primarily as the result of the work of U.S. senator Orrin Hatch from Utah, who was
the “chief architect and sponsor” of the DSHEA (Mencimer, 2001). Not surprisingly,
supplement manufactures are the largest political contributors to Hatch, and his
home state of Utah accounts for about 20% of the nation’s multi-billion dollar sup-
plement business. Despite the Hatch-supported, industry-friendly DSHEA, many
of Hatch’s supplement manufacturer donors including Nu Skin, Herbalife, Rexall
Sundown, and Sunrider have nevertheless violated federal and state regulations of
various sorts ( Mencimer, 2001).
A marginally regulated supplement industry is not without consequence. Many
deaths have resulted from taking dietary supplements that contained powerfully
harmful ingredients. Note, too, that Olympic athletes who did not take steroids
have tested positive for steroids because they took “dietary supplements” that,
while not technically steroids, nevertheless physiologically functioned as steroids
and produced the metabolic profile of steroid ingestion and a positive steroid blood
test (Barrett, 2007; Mencimer, 2001). A minor improvement for public protection
came with the 2006 passage of the Dietary Supplement and Nonprescription Drug
Consumer Protection Act. “However, public protection is only slightly increased
because other parts of DSHEA make it very cumbersome for the FDA to ban dietary
supplements and herbs” (Barrett, 2007).
13 Behavioral Effects of Sport Nutritional Supplements: Fact or Fiction? 213
Behavioral Effects of Sport Nutritional Supplements:
General Considerations
The athlete, trainer, or coach cannot rely on the product manufacturer or on the
government for accurate information about the effectiveness or safety of any supple-
ment. Instead, they must rely on science, on objective experimentation, to evaluate
such supplements. Unfortunately, such information is often lacking, the outlets (e.g.,
peer-reviewed scientific journals) of the science may be unavailable, or the reader
might not know how to evaluate and analyze the methodology and results of such
reports. Nonbiased scientific research on conditioning factors such as training reg-
imens and diet is often lacking. Additionally, the research that does exist typically
compares only the difference between large group averages of multiple athletes
recorded at one point in time. But as Kinugasa, Cerin, and Hooper (2004) advised,
“at the elite level, applied conditioning research requires a focus on an individual
athlete rather than on groups of ‘average’ athletes to make a confident assessment of
the effect of an intervention (e.g., a specific training method [or a specific nutritional
supplement]) on the performance of an individual athlete” (p. 1036). In fact, many
sports scientists have called for an increase in behavioral, single-subject research in
sports science:
“applied conditioning research would greatly benefit from single-subject research designs.
Single-subject research designs allow us to find out the extent to which a specific condi-
tioning regimen [or supplement] works for a specific athlete ...Sports scientists should use
single-subject research designs in applied conditioning research to understand how well an
intervention (e.g., a training method [or supplement]) works and to predict performance for
a particular athlete” (Kinugasa et al., 2004, p. 1035).
Chapter 4 in this book should be referenced for an overview of single-case
evaluation designs in behavioral sport psychology.
In sum, despite the existence of scientifically effective methodological tech-
niques and research designs, the evaluation of supplements with behavioral research
designs is hampered by a relative lack of behavioral research on supplement effec-
tiveness. Another constraint is that some elite athletes may refuse to participate in
potentially meaningful behavioral research. Lastly, the often shadowy world of sup-
plement manufactures does not lend itself to evaluation. Indeed, many supplement
producers have a financial stake in not having their products objectively evaluated
least they be found to be ineffective and unsafe.
I noted previously that the definitions of, and distinctions between, drugs, dietary
supplements, and nutritional supplements are vague and overlapping. If a product is
labeled a drug, it falls under FDA regulations, but if the same product (or a func-
tionally equivalent product) is called a dietary supplement, then it is subject only to
the DSHEA. Confusing the matter further, how, or if, a product is isolated and con-
centrated may affect its label. Caffeine, for example, occurs naturally in coffee and
cocoa beans (chocolate), tea leaves, and many other natural plants. Caffeine is also
isolated, concentrated, and sold in drugstores, and may be added to other products
214 S.R. Flora
as part of pills and powders for pain relief or as an ingredient in diet products. So is
caffeine a drug, a dietary supplement, a naturally occurring compound, all of these,
or none? You get the point: How caffeine is defined depends on who is selling it and
who is buying it.
As referenced in this chapter, a drug will be considered to be any product that
is either artificially created by humans or isolated and concentrated by humans to
strengths seldom seen in nature and that when introduced internally to the physio-
logical organism (via ingestion, intravenously, subcutaneously, or otherwise) affects
either physiological functioning or behavior by mechanisms other than the typical
manner by which water and foodstuffs affect functioning or development. Use of
performance-enhancing drugs is generally illegal in competitive sports (either by
national state governments or by governing bodies of sport), and, with the exception
of caffeine, they are not recommended. A dietary supplement is something that is
added to complete or strengthen one’s diet, and a nutritional supplement is a supple-
ment that nourishes, where nourish refers to the Latin nutrire, to nurse, to provide
material necessary for growth and sustenance. While all these definitions overlap,
it is difficult to state that a drug nourishes, and a nutritional supplement is a dietary
supplement, but a dietary supplement, such as a diuretic, may not nourish.
Behavioral Effects of Sport Nutritional Supplements:
Research Support
Despite the many regulatory, research and definitional problems with evaluating
supplements, there exists sufficient evidence to make recommendations for sev-
eral sport supplements that have been extensively researched. Research-supported
recommended supplements include sports drinks, whey protein, creatine, and
caffeine. Nonsupported sport-enhancing supplements include antioxidant vitamin
supplements and testosterone prohormones. The following discussion will focus
on behavioral effects of sport nutritional supplements and be as nontechnical as
possible.
Sports drinks. An athlete must be well hydrated and have sufficient energy for
optimal performance. Dehydration degrades performance. Exercise requires energy
and generates metabolic heat that must be dissipated to maintain body temperature
within narrow limits. Heat is dissipated primarily through sweat (even in cool tem-
peratures), resulting in fluid and sodium (Na) loss and, to a much lesser degree,
potassium (K) loss. Increased blood flow to the skin may also assist in vital heat
dissipation. Dehydration not only compromises the body’s ability to cool, but also
lowers blood plasma volume, making it more difficult to transport vital nutrients and
oxygen to working muscles and organs. Extreme dehydration can result in disability
or death (Shirreffs, Armstrong, & Cheuvront, 2004).
Even fluid losses as small as 1–2% of body weight can dramatically hurt per-
formance. For example, “A 1% reduction in body weight due to water loss may
evoke an undue stress on the cardiovascular system accompanied by increases in
heart r ate and inadequate heat transfer to the skin and the environment, increase
13 Behavioral Effects of Sport Nutritional Supplements: Fact or Fiction? 215
plasma osmolality, decrease plasma volume, and may affect the intracellular and
extracellular electrolyte balance” (von Duvillard, Braun, Markofski, Beneke, &
Leithauser, 2004, p. 651). In track races of distances of 1.5, 5, and 10 km, a dehy-
drating 2% body weight fluid loss (via a diuretic) increased times by 3.4, 6.6, and
6.7%, respectively (Armstrong, Costill, & Fink, 1985). This decline in performance
is dramatic considering that in highly competitive races, an increased time of less
than one-half of 1% can be the difference between winning and finishing last. “It is
well documented that even small body water deficits, incurred before or during exer-
cise can significantly impair exercise performance, especially in the heat” (Shirreffs,
Armstrong & Cheuvront, 2004, p. 57).
Drinks containing between 4% and 10% carbohydrate (and a smaller portion
of protein) and sodium (the critical ingredients in sport drinks) consumed during
exercise help prevent dehydration and prevent performance declines. But consuming
plain water does not prevent performance declines and does not prevent dehydration
as well as sports drinks.
In a well-controlled experiment by Dougherty, Baker, Chow, and Kenney (2006),
elite adolescent male basketball players first exercised (treadmill and stationary
cycling) for 2 h i n bouts of 15 min in a warm environment to result in either a
2% weight loss (dehydration) or consumed either water to prevent fluid weight
loss or a 6% carbohydrate–sodium sport drink to prevent dehydration. Each player
participated in each condition in random order separated by 1 week. Following
a 1-h recovery period, players completed basketball drills to stimulate game per-
formances. At the end of the “game” when the players were either dehydrated or
hydrated with water, they felt more lightheaded and fatigued compared to when
they were hydrated with sports drink. Shooting percentage and sprint times were
significantly impaired by dehydration and significantly improved by sports drink
hydration compared to water hydration. Dougherty et al. (2006) concluded, “This
degree of improvement [achieved by drinking a sports drink] is important in a sport
where subtle changes in skill performance could be the difference between winning
and losing” (p. 1657). Other studies have found that compared to hydration with
water, ingestion of a carbohydrate–sodium sports drink “has been shown to improve
tennis stroke performance at the end of prolonged play, results in faster 20-m sprint
times, increases the number of sprints performed during a soccer game, delays
time to fatigue during intermittent, high-intensity cycling, and improves endurance-
running capacity during prolonged intermittent exercise” (Dougherty et al., 2006,
p. 1657). In short, “development of sports drinks with appropriate and adequate con-
centrations of electrolytes [Na] and CHOs [carbohydrates] promotes maintenance of
homeostasis, prevents injuries, and maintains optimal performance” (von Duvillard
et al., 2004, p. 651).
Carbohydrates are necessary both to provide energy – glucose in the blood
stream, stored as glycogen in the liver – and to promote hydration. According to
UK researcher R. J. Maughan (1998, p. 16, emphasis added):
Carbohydrate ingested during exercise will enter the blood glucose pool ... [and] exercise
capacity should be improved when carbohydrate is consumed. Several studies have shown
that the ingestion of glucose during prolonged intense exercise will prevent the development
216 S.R. Flora
of hypoglycemia by maintaining or raising the circulating glucose concentration ...A sub-
stantial part of the carbohydrate ingested during exercise is available for oxidation [energy],
but there appears to be an upper limit of about 1 g/min to the rate at which ingested carbo-
hydrate can be oxidized, even when larger amounts are ingested ...As well as providing an
energy substrate for the working muscles, the addition of carbohydrate in ingested drinks
will promote water absorption in the small intestine.
High concentrations of carbohydrate are not only unnecessary because there is a
limit to the rate at which they can be utilized, but also can actually be counterpro-
ductive, delaying gastric emptying. In fact, “if the concentration is high enough ...
net secretion of water into the intestine will result, and this will actually increase
the danger of dehydration” (Maughan, 1998, p. 17). Despite advertisement claims
promoting various formulations of carbohydrates in sports drinks, with the pos-
sible exception of fructose, which is more likely to cause gastrointestinal upset,
the form(s) of carbohydrate in sports drinks – glucose, sucrose, long-chain glu-
cose polymers, maltodextrins – does not matter (Coombes & Hamilton, 2000;
Maughan, 1998).
In addition to glucose, sodium (Na) is the other critical component in sports
drinks for events lasting 1 h or more, and even more vital for events lasting over
4 h. In addition to water, Na is lost in s weat (and to a lesser degree K). Na is vital to
every organ in the body, including every muscle cell and every neuron in the nervous
system. If large amounts of Na are lost through sweat, Na will leave cells to equal-
ize osmotic pressure resulting in intracellular dehydration, a dangerous situation
slowing down cellular processes. Therefore, Na lost during sweat must be replaced.
Additionally, “Na will stimulate sugar and water uptake in the small intestine and
will help maintain extracellular fluid volume” (Maughan, 1998, p. 18). Consumption
of carbohydrate and Na sports drink after dehydration results in greater blood vol-
ume restoration and greater hydration than does consumption of water alone (e.g.,
Costill & Sparks, 1973; Gonzalez-Alonso, Heaps, & Coyle, 1992). While there is a
very small amount of K lost during sweat, in events lasting less than 4 h, evidence
does not support the need for it to be added to sports drinks.
Assuming the athlete is well hydrated, well nourished, or “glycogen sufficient”
before exercise, research (for example, Coombes & Hamilton, 2000) supports the
following generalizations and recommendations. First, if the athlete is well hydrated
and glycogen sufficient, then there is no need to drink a sports drink prior to exer-
cise or competition. However, because many athletes and fitness participants are
in a state of mild dehydration prior to exercising, consuming approximately 0.5 l
of sports drink 1 h prior to exercising may provide “insurance” against premature
exercise-induced performance-degrading dehydration. For moderate exercise last-
ing1horless,consuming sports drinks is not necessary. For intense exercise lasting
1 h or less, the evidence is mixed as to whether or not a sports drink will be bene-
ficial. In the case of prolonged intermittent exercise, “studies strongly suggest that
consumption of a carbohydrate beverage can improve performance during intermit-
tent exercise” (Coombes & Hamilton, 2000, p. 192). For prolonged exercise lasting
between 1 and 4 h, 23 of 36 studies reported “significant ergogenic benefit” of con-
suming a sports drink during exercise (Coombes & Hamilton, 2000, p. 192). When
13 Behavioral Effects of Sport Nutritional Supplements: Fact or Fiction? 217
prolonged exercise lasts more than 4 h, consuming fluid, carbohydrates, and Na
(the critical ingredients of sports drinks) is vital f or performance. In fact, depend-
ing on the temperature and exercise-generated heat, additional Na supplementation,
beyond that of sports drinks, may be necessary.
Protein in sport drinks? Some studies have found that the addition of 1 part pro-
tein to 4 parts carbohydrate in sports drinks consumed during exercise improves
performance beyond the benefit from consuming carbohydrate-only sports drinks.
For example, male cyclists cycling to exhaustion cycled 40% longer when con-
suming a carbohydrate–protein drink every 15 min than they did when drinking a
carbohydrate beverage at the same rate (Saunders, Kane, & Todd, 2004). However,
the carbohydrate–protein drink contained more calories. When a carbohydrate–
protein drink was compared to a carbohydrate-only drink having equal calories,
times to exhaustion were not different (Van Essen & Gibala, 2006).
While additional follow-up studies failed to find that consumption of the
carbohydrate–protein beverage during exercise improved performance to a level of
statistical significance over the effects of consuming a carbohydrate-only beverage,
the average time to exhaustion was several minutes longer in the carbohydrate–
protein condition. Consuming either beverage significantly improved endurance
compared to consuming a placebo (noncaloric) beverage (Romano-Ely, Todd,
Saunders, & St. Laurent, 2006; Valentine, Saunders, Todd, & St. Laurent, 2008).
Furthermore, indices of muscle damage (e.g., creatine kinase) were lower in the
carbohydrate–protein condition, and post-exercise leg extensions (a measure of
muscle strength) 24 h later were greater after consuming carbohydrate–protein
drink than after consuming the carbohydrate drink (Valentine et al., 2008). These
results suggest that consuming a carbohydrate–protein sports drink instead of a car-
bohydrate sports drink (both would contain Na) during exercise may not improve
performance during that exercise session much, or at all. Nevertheless, consuming a
carbohydrate–protein drink during exercise sessions instead of a carbohydrate-only
sports drink may increase performance over many exercise sessions.
In sum, the research suggests that a person should consume approximately
100 ml (4 ounces) sports drink containing 6–10% carbohydrate, Na, and 1 part pro-
tein for 4 parts carbohydrate every 15–20 min f or intense exercise and all exercise
sessions lasting 1 h or longer. Any commercially available sports drink will work.
For sports drinks that do not contain protein, a small amount of whey protein may
be mixed into the drink. In fact, effective sports drinks can be made with ingredi-
ents that are common to almost every kitchen (except whey protein, which is widely
available). For every 100 ml (just under 4 ounces) of water, add8g(approximately
1/2 tablespoon) of sugar, 57 mg (a pinch) of salt, and 2 g (1/4 tablespoon) of whey
protein and add flavoring if desired (a few drops of lemon juice). For a 16 oz drink,
multiply portions by 4.
Recovery drinks. Without going into excessive physiological detail, if nutri-
ents, particularly protein and carbohydrate, are consumed as soon as possible
after exercise (ideally within half an hour), glycogen synthesis, muscle repair,
and strengthening will be maximized. Consequently, as compared to the effects of
consuming these nutrients at other times, future athletic performances and fitness
218 S.R. Flora
will be much greater if these nutrients are consumed immediately after exercise:
“The resynthesis of glycogen between training sessions occurs most rapidly if
carbohydrates are consumed within 30 min to 1 h after exercise” (Karp et al.,
2006, p. 78).
In a review of the available research, Manninen (2006) concluded that post-
exercise consumption of easily digested, high-glycemic carbohydrate and high-
quality and easily digested protein is vastly superior to consumption of carbohy-
drates alone (which is vastly superior to consumption of water alone). For example,
after cycling for 2 h, male cyclists consumed either a carbohydrate sports drink
or a carbohydrate–protein recovery drink and then4hlatercycledtoexhaustion.
The cyclists rode 55% longer if they consumed the carbohydrate–protein drink
(Williams, Raven, Fogt, & Ivy, 2003). Drinking the carbohydrate–protein bever-
age also resulted in a 17% greater plasma glucose response, a 92% greater insulin
response (responsible for muscle cells taking in glucose and protein that maxi-
mizes recovery and strengthening), and 128% greater storage of muscle glycogen
compared to the effects of a carbohydrate sports drink.
Again, timing is critical. Elderly men (average age 74 years) who took protein
immediately after resistance training for 10 weeks saw increases in muscle fiber,
but those who consumed the protein drink 2 h after resistance training, did not.
Additionally, isokinetic strength increased by 46% in the immediate protein intake
condition but increased only 15% in the 2-h post-workout protein intake condition
(Esmarck et al., 2001). According to Manninen (2006):
... post-exercise recovery drinks containing these nutrients [quality protein and simple
carbohydrates] in conjunction with appropriate resistance training may lead to increased
skeletal muscle hypertrophy (growth) and strength. If so, such post-exercise supplements
would be of considerable benefit not only to athletes but also to anyone who has lost muscle
function through disease – for example, Duchenne muscular dystrophy (p. 904).
Just as sports drinks can be made with common kitchen ingredients, an effective
carbohydrate–protein recovery drink is available in most kitchens in the form of
milk or, particularly, low-fat chocolate milk. Plain milk has a fairly high fat content
that is not necessary, but low-fat chocolate milk has less fat and more simple sugars.
Both have sufficient Na, and both have been shown to be fairly effective recovery
drinks (e.g., Karp et al., 2006; Shirreffs, Watson, & Maughan, 2007).
Whey protein. While milk or low-fat chocolate milk may function as effective
low-cost, post-exercise recovery drinks (assuming one is not lactose intolerant),
whey protein (a milk by-product) has conclusively been shown to be a highly effec-
tive sports nutritional supplement. The Romano-Ely et al. (2006) and Valentine et al.
(2008) carbohydrate–protein supplement studies on cycling performance (discussed
previously in this chapter) used whey protein as the protein source. Any athlete
attempting to gain maximum physical performance should consume a small amount
of whey protein prior to exercising and larger amounts immediately after exercise.
Compared to other sources of protein (egg, soy, red meat, poultry, fish), whey is the
most easily digestible and complete protein available. Moreover, it is one of the best
sources of essential amino acids (the protein molecules that the human body cannot
13 Behavioral Effects of Sport Nutritional Supplements: Fact or Fiction? 219
form on its own, so must be obtained from food) and branched-chain amino acids
(BCAA – the amino acids that are the most responsible for rapid muscle synthesis).
Consumption of whey not only has been found to increase lean muscle mass, but
may also assist in weight loss (Baer et al., 2006).
Young men exercised one leg and rested the other and then consumed either
a whey protein–carbohydrate beverage (containing 10 g of whey) or a carbohy-
drate beverage. Muscle protein synthesis was greater in the exercised legs than in
the rested legs, and the rate of muscle protein synthesis was greater in the whey
condition than in the carbohydrate condition (Tang et al., 2007). Compared to
carbohydrate or placebo consumption, pre- and post-exercise whey supplementa-
tion i ncreases muscle size and strength by several methods, including increasing
skeletal muscle glycogen recovery, maximizing muscle protein synthesis, activating
key enzymes (Morifuji, Kanda, Koga, Kawanaka, & Higuchi, 2010), and possibly
altering gene expression (Humi, Kovanen, Selanne, & Mero, 2008).
Whey is widely available in many products and sold separately in health food
stores, grocery stores, and in “big box” department stores. There is no advantage of
consuming higher priced name brand whey. In fact, at least one “big box” brand of
whey is packaged in the same size and colored package as a more expensive name
brand whey, and its label states it is produced in the same town with the same zip
code as the more expensive brand. This suggests that two products are identical with
the only difference being a different label and higher price for the name brand whey.
Creatine. Like whey, any athlete attempting to gain maximum physical per-
formance should consume creatine. The liver and kidneys produce about 2 g of
creatine each day, and some creatine is obtained from food sources (primarily meat).
Creatine was initially popular, and still is, with bodybuilders who found that creatine
supplementation could increase muscle size. Early myths about creatine supplemen-
tation included beliefs that while it might increase muscle size, it did not increase
muscle strength, that it caused bloating and water retention, that it would cause
dehydration and/or cramps in endurance athletes, and that it could cause kidney
damage. Research has shown all of the myths to be unfounded (similar myths
about whey protein are also unfounded). In reality, the safety and efficacy of cre-
atine have been so well established that it has been recommended as a “training
enhancer” by the popular Bicycling magazine (June 2006, p. 121), and The Center
for Science in the Public Interest has recommended it, particularly for seniors, in its
Nutrition Action Healthletter (Schardt, 2009). Among other evidence, the newslet-
ter cited research that found that, compared to 70-year male men who resistance
trained three times a week for 12 weeks and drank a placebo beverage, those
elderly men who resistance trained and consumed creatine had greater increases
in strength, endurance, and average power (Chrusch, Chilibeck, Chad, Shawn, &
Burke, 2001). Creatine supplementation has been shown to increase strength, power,
and functional performance in older women as well (Gotshalk, Kraemer, Mendoca
et al., 2008).
Of course, creatine is not just for seniors. In an earlier study, men trained for
12 weeks during which time they took either creatine supplements or a placebo.
After 12 weeks, compared to the placebo group, the men taking creatine had
220 S.R. Flora
significant increases in bench press strength, squat strength, and much greater
increases in both fast-twitch (strength) and slow-twitch (endurance) muscle fibers
(Volek, Duncan, & Mazzetti, 1999).
Inside the individual muscle cell, at the molecular level, energy lasting a few sec-
onds is produced when adenosine triphosphate (ATP) loses a phosphate molecule,
becoming adenosine diphosphate (ADP). Creatine phosphate gives its phosphate
molecule to ADP, changing it back into ATP. In short, more creatine phosphate
results in more ATP, which gives the muscle more potential energy and the poten-
tial to work harder. Creatine supplementation greatly increases levels of creatine
phosphate in the muscle.
In a review of over 500 studies on the effects of creatine supplementation on
physiology and/or performance, Kreider (2003, p. 89) concludes,
... supplementation has typically been reported to increase total creatine content by 10–
30% and phosphocreatine stores by 10–40%. Of the approximately 300 studies that have
evaluated the potential ergogenic [performance enhancing] value of creatine supplemen-
tation, about 70% of these studies report statistically significant results while remaining
studies generally report non-significant gains in performance. No study reports a statistically
significant ergolytic [performance degrading] effect.
Along with gains in other areas, particularly high-intensity exercise, supplementa-
tion increases maximal power 5–15%, improves single sprint performance 1–5%,
and improves repetitive sprint performance 5–15%.
Likewise, in a review of studies published since 1999, Bemben and Lamont
(2005) found that “creatine does significantly impact force production regardless
of sport, sex or age” and that “when performance is assessed based on intensity and
duration of the exercises, there is contradictory evidence relative to both continu-
ous and intermittent endurance activities. However, activities that involve jumping,
sprinting or cycling generally show improved sport performance following creatine
ingestion” (p. 107).
As with whey, creatine is widely available in many outlets, and similarly, there
is no need to by high-priced name brand creatine, which may be spiked with unnec-
essary additional ingredients. “Big box,” department, or grocery store brands work
just as well as any other brand.
Caffeine. While not a nutritional supplement, caffeine has conclusively been
shown to enhance athletic performance. In a recent review, Davis and Green (2009,
pp. 813, 814) reported,
... recent studies incorporating trained subjects and paradigms specific to intermittent
sports activity support the notion that caffeine is ergogenic to an extent with anaerobic exer-
cise. Caffeine seems highly ergogenic for speed endurance exercise ranging in duration form
60 to 180 s ... studies employing sport-specific methodologies (i.e., hockey, rugby, soc-
cer) with shorter duration (i.e., 4–6 s) show caffeine to be ergogenic during high-intensity
intermittent exercise.
In a study on caffeine supplementation on multiple sprint running performances,
compared to physically active men who took a placebo, men who took a caffeine
capsule significantly improved their sprint times compared to baseline showing that
“caffeine has ergogenic properties with the potential to benefit performance in both
13 Behavioral Effects of Sport Nutritional Supplements: Fact or Fiction? 221
single and multiple sprint sports” ( Glaister et al., 2008, p. 1835). Beyond sprint
sports, “caffeine improves physical and cognitive performance during exhaustive
exercise” (Hogervorst et al., 2008, p. 1841). In randomized counterbalanced order,
24 trained cyclists consumed either a carbohydrate bar, a carbohydrate bar contain-
ing caffeine, or a placebo beverage before cycling 2.5 h and again after 55 and
115 min into the exercise, followed by a ride to exhaustion. The cyclists were
also given cognitive tests during and after the ride. When they consumed caffeine,
they completed the cognitive tests faster with no decline in accuracy compared
to the carbohydrate-only condition, which was better than the placebo condition.
When the cyclists consumed caffeine, time to exhaustion was longer, relative to the
carbohydrate-only condition, which was better than the placebo condition. Thus,
caffeine “can significantly improve endurance performance and complex cognitive
ability during and after exercise. These effects may be salient for sports performance
in which concentration plays a major role” (Hogervorst et al., 2008, p. 1841).
Caffeine affects physiological functioning in several ways, and the physiologi-
cal mechanisms by which caffeine enhances sport performance is not conclusively
known (Davis & Green, 2009). Clearly, caffeine is a central nervous system (CNS)
stimulant (primarily as an adenosine inhibitor). Since adenosine is a CNS inhibitor,
inhibiting adenosine activates the CNS. Caffeine increases the release of adrenaline,
may increase blood glucose, and “from [its] inhibitory effects on adenosine, [it]
leads to modified pain perception while sustaining motor unit [ muscle cell] firing
rates and neuro-excitability. This then is the leading hypothesis for the ergogenic
effect of caffeine on performance” (Davis & Green, 2009, p. 823).
There are two caveats when considering caffeine as a sports supplement. First,
studies reporting beneficial effects tend to use well-trained athletes. Studies using
untrained individuals have more inconsistent results (Davis & Green, 2009). It may
be that novice athletes have too many variables affecting their performance and
physiological adaptation to training to see any effect of caffeine. Second, ingesting
caffeine from coffee does not improve athletic performance. When subjects con-
sumed caffeine capsules and then ran to exhaustion 1 h later, they ran 7.5–10 min
longer than when they ran an hour after drinking coffee, drinking decaffeinated cof-
fee, drinking decaffeinated coffee with caffeine added, or after taking a placebo
capsule. “Endurance was only increased in the caffeine capsule trial: there were no
differences among the other four tests. One cannot extrapolate the effects of caf-
feine to coffee. There must be a component(s) of coffee that moderates the actions
of caffeine” (Graham, Hibbert, & Sathasivam, 1998, p. 883).
Conditioned compensatory responses may be the mechanism that moderates the
effect of caffeine in coffee. Conditioned compensatory responses are conditioned
responses that counteract the effects of an unconditioned stimulus allowing the
organism to maintain a homeostatic state (Siegel, 2005). For example, one uncon-
ditioned effect of caffeine is increased salivation, but coffee stimuli produce a
compensatory decrease in salivation (Rozin, Reff, Mark, & Schull, 1984). The sight
and smell of coffee may function as condition stimuli that also elicit compensatory
conditioned responses to counter the stimulatory effects of coffee and eliminate
any possible sport-enhancing effect of consuming caffeine in coffee. Regardless,
222 S.R. Flora
the research suggests that to obtain a sport performance–enhancing effect, caffeine
should be consumed in a form other than in coffee. Or more generally, caffeine
should be consumed in a form different from the source that the athlete typically
gets caffeine from, be it coffee, tea, or so-called energy drinks to avoid conditioned
compensatory responses.
Nonsupported supplements: Antioxidant vitamins. Free radicals, reactive oxy-
gen species (ROS), may be one component that contributes to muscle damage after
exercise. Therefore, it might be expected that supplementation with antioxidant
vitamins may minimize this damage and promote recovery. However, in a compre-
hensive review of the available data, it was found that neither acute, pre-exercise,
or post-exercise supplementation of either vitamin C, E, or their combination had
any protective effect against muscle damage (McGinley, Shafat, & Donnelly, 2009).
McGinley et al. (2009) posit, “Given that antioxidants do not appear to be benefi-
cial in protecting against muscle damage, and that vitamin E in particular may in
fact be potentially harmful, the casual use of large doses of antioxidants should be
curtailed,” and further, “Of greater relevance to athletes and other sports persons,
antioxidant supplementation may not only fail to protect against EIMD [exercise
induced muscular damage], but could in fact interfere with the cellular signal-
ing functions of ROS. Therefore, in ingesting antioxidant vitamins in an attempt
to enhance muscle performance, these individuals may actually be retarding the
adaptive processes to exercise” (p. 1029).
Testosterone prohormone supplements. High levels of the “male” hormone
testosterone (females do produce very small amounts) are associated with strength,
power, winning, high social status, and aggression. Testosterone supplementation is
illegal. Prohormones are chemical precursors to testosterone – supplementing with
them is claimed to increase testosterone and in turn muscular strength and athletic
performance. Baseball superstar Mark McGwire said that he used the prohormone
androstenedione during the season he broke the major league home run record (sub-
sequent reports from his brother suggest he was using other illegal substances as
well). With the passage of the Anabolic Steroid Control Act of 2004 that defined
anabolic steroids as “any drug or hormonal substance, chemically and pharmacolog-
ically related to testosterone,” most prohormones and other steroids are now illegal,
including androstenedione (Brown, Vukovich, & King, 2006). Nevertheless, despite
their possible illegality these substances or highly similar substances are still heavily
advertised in weightlifting and bodybuilding magazines.
In a review of the available scientific research on testosterone prohormone sup-
plements, Brown et al. (2006) concluded, “Contrary to marketing claims, research
to date indicates that the use of prohormone nutritional supplements (DHEA,
androstenedione, androstenediol, and other steroid hormone supplements) does not
produce either anabolic or ergogenic effects in men. Moreover, the use of pro-
hormone nutritional supplements may raise the risk for negative consequences”
(p. 1451). Instead of raising testosterone in men, these supplements simply result
in “either reduced absorption, enhanced clearance, or enhanced metabolism of the
ingested substance” (p. 1452). Generally the research shows that improvements that
might occur in men only occur for those with initially low levels of testosterone;
13 Behavioral Effects of Sport Nutritional Supplements: Fact or Fiction? 223
otherwise resistance training with placebo works as well as resistance training with
androstenedione. The popular prohormone DHEA works no better: “It appears that
DHEA does not promote fat loss or muscle gain or augment adaptations to resistance
training in healthy men” (Brown et al., 2006, p. 1547). Furthermore “herbal extracts
do not alter the fate of ingested androstenedione” (Brown et al., 2006, p. 1457).
Still more concerning is that “many androgenic supplements are contaminated with
hormones, caffeine, ephedrine or other banned substances not listed on the product
label” (Brown et al., 2006, p. 1458).
While “Intake of 100–200-mg doses of androstenedione or androstenediol,
or up to 1,600 mg of DHEA, does not increase serum testosterone concentra-
tions in men ... It is clear that ingesting androstenedione [and chronic intake
of DHEA] increases serum testosterone concentrations in women” (Brown et al.,
2006, p. 1457). But the potential performance-enhancing effects for women must
be weighed against the side effects including “insulin resistance, increased inci-
dence of acne, facial hair, and other symptoms of hirsuitism [excessive hairiness in
women]” (Brown et al., 2006, p. 1458).
Testosterone levels may be raised without steroids or prohormone supplements.
Both estrogen and testosterone are synthesized in the body from cholesterol. Those
wanting to insure that they have sufficient “basic material” for increased testos-
terone could simply eat a few more eggs (also a good source of quality protein
in addition to dietary cholesterol). While excessive cholesterol in t he cardiovascular
system may not be healthy, it is necessary to consume some cholesterol for hormone
production, and it is unclear as to whether high cholesterol intake or high saturated
fat intake is responsible for high cardiovascular levels of cholesterol. Additionally,
when professional rugby players engaged in a session of resistance training, their
testosterone levels rose. When they trained and consumed caffeine, their testos-
terone levels were higher still. Coupled with the training stimulus, the more caffeine
ingested, the greater the rise in the athletes’ testosterone levels (Beaven et al., 2008).
Testosterone prohormone supplements are neither recommended nor necessary.
Conclusion
If an athlete eats a balanced diet including plenty of protein, carbohydrates, fruits,
and vegetables; gets proper rest; and trains appropriately, then the athlete will have
improved performance without taking any supplements. Conversely, if the athlete
does not train appropriately, does not have a good diet, or consumes excessive alco-
hol, then performance gains will be subpar or nonexistent even if the athlete is
taking many supplements. Without the proper training stimulus, all supplements are
worthless for sport performance. Many supplements, herbs, and aids are advertised
without sufficient support for their performance-enhancing claims. Some supple-
ments (e.g., antioxidant vitamins and prohormones) have shown to be ineffective if
not dangerous.
Conversely, if consumed at the proper time and in proper amounts, carbohydrate–
protein–Na sports drinks, whey protein, creatine, and caffeine all have been shown
224 S.R. Flora
to be safe performance-enhancing sport supplements. Again, optimal performance
can be best achieved with dedicated training, rest, diet, and nutritional supplementa-
tion. What further unites these products (sports drinks, whey protein, creatine, and
caffeine) is that they are all widely available, not patented, and low cost. Most criti-
cal is that their effectiveness and safety as sports supplements have been researched
and documented in an open manner at academic research universities across the
globe.
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Chapter 14
Cognitive–Behavioral Coach Training:
A Translational Approach to Theory,
Research, and Intervention
Ronald E. Smith and Frank L. Smoll
The science of psychology exists as a sprawling domain within which behavioral
phenomena are studied at multiple and complementary levels of analysis in the
search for biological, psychological, and environmental causal factors. One con-
sequence of this approach is an increased emphasis on translational research,
which involves the application of theories, constructs, measurement approaches,
research results, and intervention techniques across psychological domains. For
example, Tashiro and Mortensen (2006) have shown how constructs and empirical
results from traditional social psychological research might inform the diagno-
sis, prevention, treatment, and service delivery in clinical psychology and how, in
turn, knowledge of mental illness can result in enhanced understanding of social
psychological processes.
The most fundamental translational concept is found in the distinction between
basic and applied s cience. Basic science is commonly defined as the development of
theories and the discovery of knowledge for its own sake, whereas applied science
involves the applications of knowledge derived from basic science for the solution of
practical problems. However, this dichotomy begins to blur when we consider rela-
tions among theory development and testing, empirical research, and interventions
designed to have practical impact. These relations do not simply involve a one-way
causal path from knowledge or theory to application. Instead, they involve recip-
rocal interactions between theory, research, and interventions, meaning that each
of the three facets has a causal impact on the others and is, in turn, influenced by
them. Thus, our theories deepen our understanding and guide our research, and our
research, in turn, is t he most important influence on theory development and test-
ing. Our t heories also affect the interventions we develop, but the success of these
interventions reflects, in part, the adequacy of our theory and may prompt theory
revisions. Finally, the link connecting intervention and research is also a recipro-
cal one. Our research provides leads for intervention, and sound outcome research
allows us to assess the efficacy of the interventions and, perhaps, to identify the
aspects of the intervention that are responsible for its success. In turn, the nature
R.E. Smith (B)
University of Washington, Seattle, WA, USA
e-mail: resmith@uw.edu
227
J.K. Luiselli, D.D. Reed (eds.), Behavioral Sport Psychology,
DOI 10.1007/978-1-4614-0070-7_14,
C
Springer Science+Business Media, LLC 2011
228 R.E. Smith and F.L. Smoll
of the intervention dictates the outcome variables that we focus on and the way
the evaluation is conducted. Attending to these three reciprocal linkages helps us
ensure that our conceptual frameworks, research, and applied activities will support
one another and advance our field as a scientific and applied discipline. Where inter-
ventions are concerned, adherence to the model helps ensure that they are based on
firm theoretical and research foundations (frequently from other domains of scien-
tific inquiry) and that they will be evaluated in a manner that conforms to standards
of scientific accountability.
In this chapter, we describe the evolution of a program of research on coach-
ing behaviors and interventions that has spanned more than three decades and has
resulted in an empirically supported coach training program. We describe the man-
ner in which a theoretical model derived from the areas of learning, social and
personality psychology, and developmental psychology has helped guide a program
of basic and applied research. Research results derived from the basic and applied
research has guided the intervention program’s development, and research results
from basic research and program evaluations address important theoretical issues.
Phase 1: Basic Research on Coaching Behaviors
Theoretical Underpinnings
In the early 1970s, recognition of the potential impact of coaches on young athletes’
psychological welfare prompted several scientific questions that we felt were worth
pursuing. For example, what do coaches do, and how frequently do they engage in
such behaviors as encouragement, punishment, instruction, and organization? What
are the psychological dimensions that underlie such behaviors? And, finally, how
are observable coaching behaviors related to children’s reactions to various aspects
of their organized athletic experiences? Answers to such questions are not only a
first step in describing the behavioral ecology of the youth sport setting, but also pro-
vide an empirical basis for the development of psychologically oriented intervention
programs, which did not exist at the time.
To begin to answer such questions, we carried out a systematic program of basic
research over a period of several years. The project was guided by a mediational
model of coach–athlete interactions, the basic elements of which are represented as
follows: Coach behaviors → athlete perception and recall → athletes’ evaluative
reactions. This model, inspired by the “cognitive revolution” that was occurring at
the time and the contributions of social cognitive theory (Bandura, 1969; Mischel,
1973), stipulates that the ultimate effects of coaching behaviors are mediated by
the meaning that athletes confer on them. We assumed that how the athletes per-
ceive and what they remember about their coach’s behaviors affect the way that
athletes feel about the coach and evaluate their sport experiences. Furthermore, a
complex interaction of cognitive and affective processes is involved at this media-
tional level. The athletes’ perceptions and reactions are likely to be affected not only
by the coach’s behaviors, but also by other factors, such as the athlete’s age, what
14 Cognitive–Behavioral Coach Training: A Translational Approach to Theory, ... 229
Fig. 14.1 A model of adult leadership behaviors in sport, showing hypothesized relations among
situational, cognitive, behavioral, and individual difference variables (adapted from Smoll &
Smith, 1989)
he/she expects of coaches (normative beliefs and expectations), and certain person-
ality variables, such as self-esteem and anxiety. Eventually, the basic three-element
model was expanded to reflect these factors (Smoll & Smith, 1989). The elabo-
rated model, shown in Fig. 14.1, specifies a number of situational factors as well
as coach and athlete characteristics that could influence coach behaviors and the
perceptions and reactions of athletes to them. Using this model as a starting point,
we have sought to determine how observed coaching behaviors, athletes’ perception
and recall of the coach’s behaviors, and athlete attitudes are related to one another.
We have also explored the manner in which athlete and situational characteristics
might serve to affect these relations.
Measurement of Coaching Behaviors
Within behavioral psychology, behavioral assessment techniques were proving to
be reliable and valid measures of naturalistic behaviors (Komaki, 1986; White,
1975). In order to measure leadership behaviors, we developed the Coaching
Behavior Assessment System (CBAS) to permit the direct observation and cod-
ing of coaches’ actions during practices and games (Smith, Smoll, & Hunt, 1977).
The CBAS contains 12 categories divided into two major classes of behaviors.
230 R.E. Smith and F.L. Smoll
Reactive (elicited) behaviors are responses to immediately preceding athlete or team
behaviors, while spontaneous (emitted) behaviors are initiated by the coach and are
not a response to a discernible preceding event. Reactive behaviors are responses
to either desirable performance or effort (Reinforcement, Nonreinforcement), mis-
takes and errors (Mistake-contingent Encouragement, Mistake-contingent Technical
Instruction, Punishment, Punitive Technical Instruction, Ignoring Mistakes), or
misbehaviors on the part of athletes (Keeping Control). The spontaneous class
includes General Technical Instruction, General Encouragement, Organization, and
General Communication (unrelated to the current situation). The system thus
involves basic interactions between the situation and the coach’s behavior. Use of
the CBAS in observing and coding coaching behaviors in a variety of sports by
us and by other research teams has shown that (a) the scoring system is suffi-
ciently comprehensive to incorporate the vast majority of overt leader behaviors,
(b) high interrater reliability can be obtained, and (c) individual differences in
behavioral patterns can be discerned (Smith, Smoll, & Christensen, 1996). Factor
analyses of the CBAS have revealed three major factors that account for about
75% of the behavioral variance: supportiveness (comprised of Reinforcement and
Mistake-contingent Encouragement), instructiveness (General Technical Instruction
and Mistake-contingent Technical Instruction versus General Communication and
General Encouragement), and punitiveness (Punishment and Punitive Technical
Instruction).
The theoretical model assumes that the effects of coaching behaviors will be
mediated by athletes’ perceptions and recall of the behaviors. Accordingly, we con-
structed the CBAS Player-perceived Behavior Scale (CBAS-PBS) on which athletes
are given descriptions of each of the CBAS behaviors and asked to indicate on
7-point scales how frequently their coach behaved in that fashion.
Behavioral Signatures
A major advance within social cognitive theory was the demonstration that although
behaviors may show marked inconsistency across situations (a finding that at one
time was seen as a major challenge to the viability of personality), the consistency
implied by a concept of personality is to be found in stable individual differences in
situation–behavior relations. These patterns are called behavioral signatures (Shoda,
Mischel, & Wright, 1994). The discovery of behavioral signatures in conduct-
disordered children prompted us to reanalyze a large body of our behavioral data,
and our new analyses revealed that most coaches do indeed show individualized
patterns of behavior in response to certain situations, in this case, whether the team
was winning, losing, or in a tie game at the time (Smith, Shoda, Cumming, & Smoll,
2009). For example, some coaches consistently responded when losing with an
increased rate of punitive behaviors and a reduced rate of instructional behaviors,
whereas others decreased in punitiveness and become relatively more supportive
or instructive under this situational condition. Moreover, the high-profile stability
coefficients (often exceeding 0.90) we discovered indicate that these differences in
behavioral patterning occurred consistently in the three classes of situations and
14 Cognitive–Behavioral Coach Training: A Translational Approach to Theory, ... 231
were not merely random fluctuations. This demonstration of behavioral signatures
in translational research on youth sport coaches thus offers valuable support to
the social cognitive theoretical model. Moreover, it advances our understanding of
coaching behaviors, for, as we shall see, behavioral signatures are more predictive
of athletes’ attitudes toward the coach than are the CBAS behaviors considered in
isolation.
Coaching Behaviors and Children’s Evaluative Reactions
Following development of the CBAS, a field study was conducted to establish rela-
tions between coaching behaviors and several athlete variables specified in the
conceptual model (Smith, Smoll, & Curtis, 1978). Fifty-one male Little League
Baseball coaches were observed by trained coders during 202 complete games.
A total of 57,213 individual coaching behaviors were coded into the 12 categories,
and a behavioral profile based on an average of 1,122 behaviors was computed for
each coach.
Data from 542 players were collected after the season during individual inter-
views and questionnaire administrations carried out in the children’s homes.
Included were measures of their recall and perception of the coach’s behaviors (on
the same scales as the coaches had rated their own behavior), their liking for the
coach and their teammates, the degree of enjoyment they experienced during the
season, and their general self-esteem.
Relations between coaches’ scores on these behavioral dimensions and player
measures indicated that players responded most favorably to coaches who engaged
in higher percentages of supportive and instructional behaviors. Players on teams
whose coaches created a supportive environment also liked their teammates more.
A somewhat surprising finding was that the team’s won–lost record was essentially
unrelated to how well the players liked the coach and how much they wanted to
play for the coach in the future. This finding that coaching behaviors were far
more important predictors of liking for the coach than was won–lost record was
replicated in another large study involving youth basketball (Cumming, Smoll,
Smith, & Grossbard, 2007). It is worth noting, however, that winning assumed
greater importance beyond age 12, although it continued to be a less important
attitudinal determinant than coaching behaviors.
Another important finding concerns the degree of accuracy with which coaches
perceive their own behaviors. Correlations between CBAS observed behaviors and
coaches’ ratings of how frequently they performed the behaviors were generally
low and nonsignificant. The only significant correlation occurred for punishment.
Children’s ratings on the same perceived behavior scales correlated much more
highly with CBAS measures than did the coaches’ r atings! It thus appears that
coaches have limited awareness of how frequently they engage in particular forms
of behavior and that athletes are more accurate perceivers of actual coach behav-
iors. This finding suggested that any effective intervention would need to increase
coaches’ self-awareness of their behavior.
232 R.E. Smith and F.L. Smoll
Situational and Personality Moderators of Behavior–Attitude
Relations
The theoretical model shown in Fig. 14.1 posited a number of situational and
individual difference variables that were expected, on theoretical grounds, to mod-
erate relations between coaching behaviors and athletes’ evaluative responses to the
coach.
Game situation. One situational variable shown in the figure involves how suc-
cessful the team is currently or was in the past. The demonstration of behavioral
signatures by Shoda and his coworkers (1994) prompted us to reanalyze our behav-
ioral data, revealing the existence of behavioral signatures. During the baseball study
described above, we had coded the score of the game at the end of each half-inning,
enabling us to determine how individual coaches behaved when their teams were
winning or losing, or when the game was tied or with a one-run differential. We
then related factor scores on the supportiveness, punitiveness, and instructiveness
dimensions while the team was winning or losing or in a close contest with athletes’
liking for the coaches and found substantial differences between the correlations
as a function of game situation. Rate of supportive behaviors delivered while the
team was winning correlated highly with liking, whereas supportive behaviors while
losing bore no relation to liking for the coach. The opposite occurred for punitive
behaviors, which were strongly and negatively related to liking when delivered in
losing situations, but were only weakly related when given during winning situ-
ations. Instructiveness was not differentially affected by the score at the time it
occurred (Smith, Shoda et al., 2009). Instructiveness during losing half-innings was
negatively related to liking and positively related to athlete liking during winning
half-innings, but the correlations were less extreme. These relations became clear
when the situation-behavior profiles of the best-liked and least-liked coaches were
compared (see Fig. 14.2).
Thus, the nature of the situation in which a class of coaching behaviors occurred
moderated the behavior’s relation with liking for the coach, particularly for sup-
portive and punitive behaviors. These relations accounted for between 14% and
25% of the variance in athletes’ liking for the coach, whereas less than 4% of the
variance in liking scores was accounted for by the behaviors when situation was
not taken into account. This finding exemplifies the manner in which decontextu-
alized behavior aggregates may mask important relations that appear only when
situation-behavior units are analyzed, a key tenet of the social cognitive model
(Mischel & Shoda, 1998). From a translational perspective, the results add support
from a unique naturalistic setting to laboratory-based results in cognitive psychol-
ogy that address mood-congruent memory and judgment. One possible explanation
for the behavior–attitude relations may be found in previous research on mood con-
gruence (Bower & Forgas, 2000). Attitude data were collected from athletes at the
end of the season, so these measures may be regarded at least in part as indirect
memory measures reflecting recalled affective responses to the coach during the
season. Mood congruence may affect such recall, so that the impact of positively
valenced supportive behaviors in an affectively pleasant winning situation may be
14 Cognitive–Behavioral Coach Training: A Translational Approach to Theory, ... 233
Best Liked Coaches
–0.30
–0.20
–0.10
0.00
0.10
0.20
0.30
0.40
0.50
0.60
Losing Close Winning
Support
Instruct
Punish
Behavior Rate z -Score
Least Liked Coaches
Losing Close Winning
Support
Instruct
Punish
–0.40
–0.30
–0.20
–0.10
0.00
0.10
0.20
0.30
0.40
0.50
0.60
Behavior Rate z -Score
Fig. 14.2 Intraindividual
behavioral signatures of
best-liked and least-liked
youth sport coaches across
three game situations,
showing situation-specific
patterning of supportive,
instructive, and punitive
behaviors (Smith, Shoda
et al., 2009)
better encoded and later remembered. In like fashion, negatively valenced punitive
behaviors that occurred during losing situations while the athletes were feeling bad
may have been encoded more deeply and affected retrospective recall about how the
athletes felt about their coach during the season. Such encoding could well influence
global postseason attitudes toward the coach.
Motivational climate. Achievement goal theory has had a major influence on
sport psychology over the past two decades (Duda, 2005; Roberts, Treasure, &
Conroy, 2007). Achievement goal theory focuses on understanding the function
and meaning of goal-directed actions, based on how participants define success
and how they judge whether or not they have demonstrated competence (Ames,
1992b; Dweck, 1999). The two central constructs in the theory are individual
goal orientations that guide achievement perceptions and behavior, and the moti-
vational climate created within adult-controlled achievement settings. In a mastery
motivational climate, success is self-referenced, defined in terms of giving maxi-
mum effort, enjoyment of the activity, and personal improvement. An ego-oriented
climate defines success in social-comparison terms, emphasizing outperforming
others, winning, and those who perform best get special attention.
234 R.E. Smith and F.L. Smoll
A large body of research indicates that in sports, as in other achievement settings,
mastery achievement goals and a mastery-oriented motivational climate are asso-
ciated with salutary effects on athletes (Chi, 2004). Compared with ego-oriented
students and athletes, those high in mastery orientation report higher feelings of
competence, greater enjoyment of the activity, and higher intrinsic motivation and
effort (Duda, 2005). A mastery orientation (particularly in combination with a low
ego orientation) is also related to lower levels of cognitive trait anxiety and pre-
event state anxiety (Newton & Duda, 1999; Papaioannou & Kouli, 1999). Finally, a
mastery goal orientation is related to a variety of adaptive achievement behaviors,
such as exerting consistent effort, persistence in the face of setbacks, and sustained
and improved performance (Ames, 1992a; Dweck, 1999). Although an ego orien-
tation has at times been linked to high levels of achievement, it also has a number
of less-desirable correlates, such as inconsistent effort, higher levels of performance
anxiety, reduced persistence or withdrawal in the face of failure, decreased intrin-
sic motivation for sport involvement, and a willingness to use deception and illegal
methods in order to win (Duda, 2005; Roberts et al., 2007).
A mastery motivational climate counters the “win at all costs” philosophy that
is all too common in youth s ports. In such a climate, students and athletes tend
to adopt adaptive achievement strategies such as selecting challenging tasks, giv-
ing maximum effort, persisting in the face of setbacks, and taking pride in personal
improvement. In contrast, an ego-involving climate promotes social comparison as a
basis for success judgments. When coaches create an ego climate, they tend to give
differential attention and positive reinforcement to athletes who are most compe-
tent and instrumental to winning, and skill development is deemed more important
to winning than it is to personal improvement and self-realization. They are also
more likely to respond to mistakes and poor performance with punitive responses.
Several studies conducted in physical education classes have shown that motiva-
tional climate is a stronger predictor of such outcomes as intrinsic motivation and
voluntary activity participation than are students’ achievement goal orientations
(Roberts et al., 2007). Within youth sports, a mastery climate is associated with more
positive attitudes toward the coach, whereas an ego climate is negatively associated
with athletes’ attitudes toward the coach. Moreover, won–lost percentage accounts
for far less attitudinal variance than does the motivational climate created by the
coach (Cumming et al., 2007).
Our research has shown that coach-initiated motivational climate has an effect on
athletes’ achievement goal orientation over the course of a sport season. In a longi-
tudinal study involving 50 youth basketball teams, we used a motivational climate
scale created for young athletes to predict changes in young athletes’ achievement
goal orientations over the course of a season. Coach-initiated mastery climate scores
predicted increases in mastery goal orientation and decreases in ego orientation. Ego
motivational climate scores were associated with increases in ego goal orientation
(Smith, Smoll, & Cumming, 2009).
An individual difference factor: Athlete self-esteem. The conceptual model shown
in Fig. 14.1 also specifies coach and athlete individual difference variables that are
hypothesized to influence coaching behaviors and their effects. Originating within
humanistic conceptions of personality (James, 1890; Rogers, 1959), self-esteem has
14 Cognitive–Behavioral Coach Training: A Translational Approach to Theory, ... 235
Fig. 14.3 Mean evaluations
of coaches by athletes as a
function of athletes’
self-esteem and
supportiveness of the coach
(Smith & Smoll, 1990)
proven to be a personality variable of major importance (Brown, 1998). Of particular
interest within our research program, how people feel about themselves influences
how they respond to the behaviors of other people (Brown, 1998). We therefore
hypothesized that level of s elf-esteem would moderate relations between coaching
behaviors and children’s attitudes toward themselves, the coach, and other aspects
of their s port experience. In the Little League Baseball study described above, anal-
ysis of the children’s attraction responses toward the coaches revealed a significant
interaction between coach supportiveness (the tendency to reinforce desirable per-
formance and effort and to respond to mistakes with encouragement) and athletes’
level of self-esteem (Smith & Smoll, 1990). As shown in Fig. 14.3, children with
low self-esteem were especially responsive to variations in supportiveness in a man-
ner consistent with a self-enhancement model of self-esteem (Swann, 1990). This
finding is consistent with the results of other studies that, collectively, suggest that
self-enhancement motivation causes people who are low in self-esteem to be espe-
cially responsive to variations in supportiveness because of their greater need for
positive feedback from others (Tesser & Campbell, 1983). We therefore concluded
that children who are low in self-esteem are especially in need of a positive sport
experience and that coaches can help provide that experience.
Phase 2: Translating Basic Research Findings
into a Coach Intervention
Data from our basic research indicated clear relations between coaching behaviors
and the reactions of youngsters to their athletic experience. Along with findings
from research inspired by achievement goal theory, these relations provided a foun-
dation for developing a set of coaching guidelines that formed the basis for an
236 R.E. Smith and F.L. Smoll
intervention that was initially called Coach Effectiveness Training (Smith, Smoll, &
Curtis, 1979). With the later emergence of achievement goal theory and the wealth
of research it inspired, we incorporated its principles into an evolved program called
the Mastery Approach to Coaching (MAC) that explicitly focuses on the develop-
ment of a mastery motivational climate. This emphasis is highly consistent with
principles (particularly our conception of success as doing one’s best and striving to
maximize one’s potential) that have been emphasized in CET from the beginning.
The MAC program incorporates information on goal orientations and motiva-
tional climate and includes specific guidelines on how to create a mastery climate.
An overview of MAC content and procedures for its implementation is now pre-
sented. A more comprehensive discussion of cognitive–behavioral principles and
techniques used in conducting psychologically-oriented coach training programs
appears elsewhere (Smoll & Smith, 2010).
Five key principles are emphasized in a MAC workshop, and behavioral guide-
lines are presented for implementing each principle (see Smith & Smoll, 2002). The
empirically derived guidelines (i.e., coaching do’s and don’ts) serve two important
functions: ( a) they allow us to conduct MAC as an information-sharing rather than
speculative enterprise, and (b) the scientific origin of the guidelines increases their
credibility with coaches. By presenting a workshop as informational in nature, we
can play to coaches’ desire to provide the best possible experience for youngsters
(the prevailing motivation for most volunteer coaches). We are not telling them what
they “should do,” but rather what research has shown to be effective in helping them
meet their goals and how they can incorporate these findings into their own coaching
style. We have always found coaches receptive to this approach.
The first MAC principle deals with a developmentally oriented philosophy of
winning. Coaches are urged to focus on athletes’ effort and enjoyment rather than
on success as measured by statistics or scoreboards. They are encouraged to empha-
size “doing your best,” “getting better,” and “having fun” as opposed to a “win at
all costs” orientation (Smoll & Smith, 1981). This principle attempts to reduce the
ultimate importance of winning relative to other prized participation motives (e.g.,
skill development and affiliation with teammates) and takes into account the inverse
relation between enjoyment and competitive anxiety (Scanlan & Lewthwaite, 1984;
Scanlan & Passer, 1978, 1979). Moreover, coaches are instructed to help promote
separation of athletes’ feelings of self-worth from game outcomes or won–lost
records. Although formulated prior to the emergence of achievement goal theory,
this principle is clearly consistent with the procedures designed by Ames (1992a,
1992b), Dweck (1999), and Epstein (1988, 1989) to create a mastery learning
climate in the classroom.
Our second principle emphasizes a “positive approach” to coaching (see Smith,
2010). In such an approach, coach–athlete interactions are characterized by the lib-
eral use of positive reinforcement, encouragement, and sound technical instruction
that help create high levels of interpersonal attraction between coaches and athletes.
Punitive and hostile responses are strongly discouraged as they have been shown to
create a negative team climate and to promote fear of failure in athletes. We empha-
size that reinforcement should not be restricted to the learning and performance
14 Cognitive–Behavioral Coach Training: A Translational Approach to Theory, ... 237
of sport skills. Rather, it should also be liberally applied to strengthen desirable
responses (e.g., mastery attempts and persistence, teamwork, leadership, sportsman-
ship). MAC also includes several “positive approach” guidelines pertaining to the
appropriate use of technical instruction. For example, when giving instruction, we
encourage coaches to emphasize the good things that will happen if athletes exe-
cute correctly rather than focusing on the negative things that will occur if they do
not. This approach is designed to motivate athletes to make desirable things happen
(i.e., helps develop a positive achievement orientation) rather than building fear of
making mistakes.
The third coaching principle is to establish norms that emphasize athletes’ mutual
obligations to help and support one another. Such norms increase social support
and attraction among teammates and thereby enhance cohesion and commitment
to the team, and they are most likely to develop when coaches (a) are themselves
supportive models and (b) reinforce athlete behaviors that promote team unity. We
also instruct coaches in how to develop a “we’re in this together” group norm. This
norm can play an important role in building team cohesion, particularly if the coach
frequently reinforces athletes’ demonstrations of mutual supportiveness.
A fourth principle is that compliance with rules of conduct is most effectively
achieved by involving athletes in decisions regarding team rules and by reinforcing
compliance with them rather than by using punitive measures to punish noncompli-
ance, a principle consistent with guidelines for shared decisional responsibility in
the mastery-oriented motivational climate of the classroom (Ames, 1992a, 1992b;
Dweck, 1999; Epstein, 1988, 1989). By setting explicit guidelines that the ath-
letes help formulate and by using positive reinforcement to strengthen desirable
responses, coaches can foster self-discipline and often prevent athlete misbehaviors
from occurring.
A fifth principle is that coaches should become more aware of their own behavior
and its consequences. To enhance awareness, MAC coaches are taught the use of
behavioral feedback and self-monitoring, which are described below.
During a 75-min MAC workshop, behavioral guidelines are presented verbally
with the aid of animated PowerPoint slides and cartoons illustrating important
points. Additionally, a mastery climate is explicitly described, its creation is strongly
recommended, and a list of established salutary effects derived from research is pre-
sented. The didactic presentation of MAC principles is augmented by modeling both
desirable and undesirable methods of responding to specific situations (e.g., athlete
mistakes, reinforcing good performance and effort). Coaches are also invited to role
play desired responses.
To reinforce the didactic portions of the workshop, coaches are given a 34-page
booklet, which highlights the advantages of a mastery motivational climate and
provides behavioral guidelines for creating one (Smoll & Smith, 2008). It also sup-
plements the guidelines with concrete suggestions for communicating effectively
with young athletes, gaining their respect, and relating effectively to their parents.
A notable finding from our basic research was that coaches had very limited
awareness of how often they behaved, as indicated by low correlations between
observed and coach-rated behaviors (Smith et al., 1978). Similar findings occurred
238 R.E. Smith and F.L. Smoll
in another youth sport observational study (Burton & Tannehill, 1987). Thus,
an important goal of MAC i s to increase coaches’ awareness of what they are
doing, for no change is likely to occur without it. MAC coaches are taught the
use of two proven behavioral-change techniques, namely, behavioral feedback
(Edelstein & Eisler, 1976; Huberman & O’Brien, 1999) and self-monitoring (Crews,
Lochbaum, & Karoly, 2001; Kanfer & Gaelick-Buys, 1991). To obtain feedback,
coaches are encouraged to work with their assistants as a team and share descriptions
of each other’s behaviors. Another feedback procedure involves coaches soliciting
input directly from their athletes.
With respect to self-monitoring, the workshop manual contains a brief Coach
Self-Report Form, containing nine items related to the behavioral guidelines (see
Smoll & Smith, 2008, p. 24). On the form, coaches are asked how often they
engaged in the recommended behaviors in relevant situations. For example, “When
athletes gave good effort (regardless of the outcome), what percent of the time did
you respond with reinforcement?” MAC coaches are instructed to complete the
form immediately after practices and games, and they are encouraged to engage
in self-monitoring on a regular basis in order to achieve optimal results.
MAC also includes discussion of coach–parent relationships and provides
instructions on how to organize and conduct a sport orientation meeting with par-
ents. Some purposes of the meeting are to inform parents about their responsibilities
for contributing to the success of the sport program and to guide them toward
working cooperatively and productively with the coach (see Smoll & Cumming,
2006).
Phase 3: Outcome Research
We now s ummarize the results of five CET and MAC outcome studies conducted
by our research group, by Conroy and Coatsworth (2004), and by Sousa, Smith, and
Cruz (2008). Collectively, these studies have assessed the effects of the intervention
on a host of behavioral, attitudinal, motivational, and personality variables.
Coaching Behaviors
The CET/MAC intervention is designed to influence observed and athlete-perceived
coaching behaviors, and these changes, in turn, are thought to mediate other effects
of the training on young athletes. A major goal of the intervention is to increase
supportive behaviors and reduce aversive coach behaviors to create a more positive
and enjoyable sport experience for young athletes.
Because of the labor-intensive demands of CBAS training and data collection,
observable coaching behaviors have served as outcome variables in only three stud-
ies. In the first randomized trial of CET, we compared 18 trained Little League
Baseball coaches with 13 untrained coaches, collecting a total of 26,413 behav-
iors over four games, an average of 813 behaviors per coach (Smith et al., 1979).
14 Cognitive–Behavioral Coach Training: A Translational Approach to Theory, ... 239
A stepwise discriminant analysis of the behavioral differences indicated that in
accordance with the CET behavioral guidelines, the trained coaches exhibited more
reinforcement and mistake-contingent encouragement behaviors and fewer puni-
tive and control-keeping responses. However, the group difference was significant
only in the case of positive reinforcement. Positive reinforcement differences were
also reported by Conroy and Coatsworth (2004) in a smaller study involving four
swimming coaches trained in CET principles and three untreated control coaches.
However, the small scope of the s tudy precluded a meaningful test of statistical
significance. Finally, a replicated single-subject approach was used by Sousa et al.
(2008) to assess the effects of coach training on four youth soccer coaches. CBAS
data were collected on the coaches at baseline, and the coaches were then exposed
to CET principles via a DVD. They were provided with behavioral profiles of their
baseline CBAS behaviors and allowed to set individual goals for behaviors they
wanted to increase or decrease. Although the specific behavioral goals varied, the
coaches uniformly wanted to become more supportive and/or less punitive in their
behaviors. Follow-up CBAS observations showed that two of the coaches achieved
positive changes in all three of their target behaviors, and a third coach improved
on two of the targeted behaviors. The fourth coach showed no change. Although
more research is needed, it does appear that the intervention produces changes in
observed coaching behaviors that are consistent with the behavioral guidelines.
Athlete-perceived behavioral ratings also provide strong evidence for positive
behavioral differences linked to the CET intervention. In two large-scale outcome
studies, athletes who played for CET-trained coaches reported that their coaches
were more highly and consistently reinforcing and encouraging rather than punitive
in response to mistakes, and gave higher levels of instruction than did untrained
coaches (Smith et al., 1979; Smoll, Smith, Barnett, & Everett, 1993). The inter-
vention thus promotes both observed and athlete-perceived behaviors that are in
accordance with the CET/MAC principles and behavioral guidelines.
Athlete Attitudes
The positive behaviors encouraged by the CET/MAC research-derived guidelines
would be expected to be reflected in more positive attitudes on the part of athletes
to their coaches and other aspects of their sport experience. Significant differences
in athlete attitudes favoring trained coaches have been found in two large experi-
mental studies involving 49 youth baseball coaches, and 477 athletes have shown
significant postseason differences in liking for the coach, increased liking for the
coach over the course of the season, enjoyment of their sport experience, liking for
teammates, evaluation of the coach’s teaching ability, and desire to play for the
coach in the f uture (Smith et al., 1979; Smith, Smoll, & Barnett, 1995). These
differences cannot be attributed to differences in won–lost records. In the latter
study, the athletes also believed that their trained coaches liked them more. In nei-
ther study did they increase their liking for the sport itself. Unfortunately, Conroy
and Coatsworth (2004) did not assess athlete attitudes in their study. To this point,
240 R.E. Smith and F.L. Smoll
however, it appears that the training program is associated with positive differences
both in coaching behaviors and in athletes’ evaluative responses to the coach and
other aspects of their sport experience.
Self-Esteem
We have been interested in self-esteem as both a moderator of athletes’ responses to
their coaches and to the effects of the intervention on athletes’ feelings of self-worth.
Our expectation (based in part on the moderator effect shown in the basic research
phase) was that children low in self-esteem are particularly in need of a positive
sport experience and that the supportive and instructive behaviors r ecommended by
the behavioral guidelines would be especially well received by them. This hypothe-
sis was strongly s upported in the first experimental trial (Smith et al., 1979), where a
significant main effect of training status on athletes’ overall evaluation of the coach
was qualified by a significant interaction involving level of self-esteem. As in the
basic research, effects of the intervention were especially large for children with
low self-esteem.
We have also assessed the effects of the intervention on changes in self-esteem.
Specifically, we found that children who played for the trained coaches showed a
significant increase in global self-esteem from the previous year to the end of the
season following training, whereas the control group athletes showed no change
(Smith et al., 1979). In a later study tracking self-esteem from the beginning of the
season to the end, positive changes in self-esteem occurred f or children who were
below the median self-esteem score at preseason (Smoll et al., 1993). The magnitude
of the increase in self-esteem moved the average low-esteem child who played for
a trained coach from approximately the 25th to the 50th percentile of the preseason
distribution. This change could not be attributed to regression to the mean, for the
low-self-esteem children who played for an attention-placebo control group showed
no increase in self-esteem scores.
Achievement Goal Orientation
Both the MAC and its historical CET predecessor are explicitly designed to produce
a coach-initiated mastery climate and to discourage a “win at all costs” ego climate.
A major effect of a mastery climate is to promote the development of a mastery
achievement goal orientation in young athletes. A mastery goal orientation has a
wide range of salutary effects identified in achievement goal theory research in both
sport and educational settings (see Ames, 1992b; Roberts et al., 2007).
The effects of the MAC intervention on achievement goal orientations was
assessed in a study involving 37 basketball teams with 225 boys and girls
(Smoll, Smith, & Cumming, 2007a). Coach-initiated motivational climate was
assessed using the Motivational Climate Scale for Youth Sports (MCSYS; Smith,
Cumming, & Smoll, 2008). Mastery and ego goal orientations were measured at the
14 Cognitive–Behavioral Coach Training: A Translational Approach to Theory, ... 241
2
2.2
2.4
2.6
2.8
Ego Orientation
4
4.2
4.4
4.6
4.8
Time of Measurement
Mastery Orientation
Preseason Late season
Time of Measurement
Preseason Late season
MAC Trained
Control
MAC Trained
Control
Fig. 14.4 Changes in AGSYS achievement goal orientation scores from preseason to late-season
in children who played for either MAC-trained coaches or untrained coaches (Smoll et al.,
2007a)
beginning and end of the season using the Achievement Goal Scale for Youth Sports
(AGSYS; Cumming, Smith, Smoll, Standage, & Grossbard, 2008). Both measures
have fourth-grade reading levels that make them appropriate for athletes down to
ages 8–9 years. The MAC intervention resulted in significantly higher mastery-
climate scores and lower ego-climate scores compared with the control condition.
Moreover, multilevel analyses revealed that athletes who played for the trained
coaches exhibited significant increases in mastery goal orientation scores and signif-
icant decreases in ego-orientation scores across the season, whereas control group
participants did not (see Fig. 14.4). These results applied to both boys and girls
teams. Although additional experimental trials are needed, this study indicates that
the MAC intervention has its intended impact on both coach-initiated motivational
climate and athletes’ achievement goal orientations.
Performance Anxiety
Our initial interest in developing a coach intervention was stimulated by concerns
that adult-organized youth sports can place inappropriate pressures on children,
thereby promoting needless stress and anxiety. The behavioral guidelines of the
original CET intervention were designed to create a positive athletic environment
that would enhance enjoyment and promote positive psychosocial consequences of
participation. The emphasis on a mastery climate promotion in both CET and MAC
programs that evolved over time also is consistent with the negative relations found
between anxiety and both mastery climate and mastery goal orientation (Chi, 2004).
242 R.E. Smith and F.L. Smoll
23.5
24
24.5
25
25.5
26
26.5
27
27.5
Preseason Late-season
Time of Measurement
Mean SAS-2 Total Scores
MAC Trained
Control
Fig. 14.5 Changes in SAS-2
performance trait anxiety
scores from preseason to
late-season in children who
played for either
MAC-trained coaches or
untrained coaches (Smith
et al., 2007)
Two studies provide evidence that the CET/MAC intervention helps reduce anx-
iety. In a study of youth baseball teams, we found that children who played for
CET-trained coaches showed a significant decrease in two different trait measures of
sport performance anxiety, whereas children who played for coaches in an attention-
placebo condition exhibited no change in anxiety (Smith et al., 1995). In the more
recent MAC-evaluation study cited above in relation to achievement goal orienta-
tion, the children were also administered the revised Sport Anxiety Scale-2 (SAS-2;
Smith, Smoll, Cumming, & Grossbard, 2006), which has a fourth-grade reading
level that makes it more appropriate for youth sport research than the measures
used in the 1995 study. As shown in the significant multilevel groups × time
interaction presented in Fig. 14.5, children who played for the untrained coaches
exhibited an increase in SAS-2 total score as competitive pressures increased over
the course of the season, whereas those whose coaches received the MAC inter-
vention showed a decrease in anxiety. This pattern was also exhibited on the
somatic anxiety, worry, and concentration disruption subscales of the SAS-2 (Smith,
Smoll, & Cumming, 2007).
Dropout Rate
Attrition is a major problem in youth sport programs, where approximately 35% of
athletes drop out each year (Gould, 1987). Sport attrition research shows that a major
reason why children say they drop out of programs is aversive coaching behaviors
and pressures to win. If children who play for trained coaches like their coach and
teammates more, feel more positively about themselves, and have more positive and
adaptive mastery achievement goals, they should be more likely to continue their
sport participation. Barnett, Smoll, and Smith (1992) followed up children who had
14 Cognitive–Behavioral Coach Training: A Translational Approach to Theory, ... 243
played for trained and untrained coaches to assess attrition (defined as total with-
drawal from sports participation the following year). They found that 26% of the
children who had played for untrained coaches had dropped out of sport, compared
with only 5% of those who played for coaches trained in CET/MAC principles.
Conclusion
Although more research is clearly needed, particularly by other investigators, evi-
dence for the efficacy of the CET and MAC interventions has been provided by three
different research groups. Our research group has done three large-scale experimen-
tal outcome studies, plus a smaller MAC intervention study within an inner-city
African American basketball program that also produced a significant increase in
mastery goal orientation scores and a significant decrease in ego-orientation scores
on the AGSYS relative to a control condition (Smith, Smoll, Cumming, & DeCano,
2006). It appears that the empirically derived behavioral principles can be read-
ily applied by coaches and that their application has salutary effects on a range of
psychosocial outcome variables in boys, girls, and minority populations.
Phase 4: Dissemination
Given the ever-expanding nature of organized athletics for children and adoles-
cents, the need for effective coach training programs is obvious. Likewise, the large
coach turnover from year to year creates a continuing demand for intervention. Our
experience in offering coaching workshops has shown that youth sport coaches are
committed to providing a positive experience for youngsters. It is also reassuring
to note that coaches are willing to spend time to acquire additional information,
and they do take advantage of the availability of workshops. Indeed, more than
25,000 coaches have participated in some 500 CET and MAC workshops in the
United States and Canada. Workshops have been presented to volunteer coaches in
a variety of sport-specific organizations (e.g., Little League Baseball, US Soccer
Federation, Minnesota Hockey) and multisport organizations (e.g., Catholic Youth
Organization, YMCA, community recreation departments). The program has also
been offered as in-service training for physical education teachers and coaches in
public school districts.
The need for effective dissemination of evidence-based treatments has been rec-
ognized within the fields of medicine (Institute of Medicine, 2001) and clinical
psychology (McHugh & Barlow, 2010). But the impact of an intervention, no mat-
ter how promising, is limited if a means cannot be found to make it accessible to its
target population. Given the body of evidence that has accumulated for the effica-
cious and economical MAC intervention, we believe that it is ready for widespread
dissemination to youth sport organizations.
There are obvious limitations in the number of workshops that can be conducted
by the program’s developers, indicating the need for a mechanism to provide wider
244 R.E. Smith and F.L. Smoll
dissemination. In order to maximize the distribution of MAC, we have transformed
the workshop into a self-instructional format, consisting of a DVD and a 32-page
manual, the content of which is linked to the DVD (Smoll & Smith, 2009a, 2009b).
This provides a fully-integrated instructional package. The 66-min DVD presents
video-recorded segments of a live workshop and incorporates several educational
procedures (lecture, dynamic interaction, modeling, and role playing). It is specif-
ically designed to teach the mastery-oriented principles with the aid of animated
coach–athlete cartoons, photos, and embedded videos. Additionally, a 12-min video
has been produced that presents an overview of the DVD content. The demonstra-
tion video can be viewed on our Youth Enrichment in Sports project website (www.
y-e-sports.com).
In recognition of the importance of educating youth sport parents as well as
coaches, we have also developed a 1-h workshop titled the Mastery Approach to
Parenting in Sports (MAPS). Similar to our coach training intervention, MAPS
applies positive influence and mastery climate principles to parenting young ath-
letes. This allows both coaches and parents to be “on the same page” in the sport
experiences that they provide. An initial experimental study presenting the MAC
and MAPS workshops in a youth basketball league resulted in significant reductions
in athletes’ performance anxiety over the course of the season, whereas a no-
treatment control condition was associated with increased anxiety (Smoll, Smith, &
Cumming, 2007b). As for MAC, we have transformed the companion sport–parent
workshop into self-instructional DVD format (Smoll & Smith, 2009c). A descrip-
tion of the 45-min MAPS DVD and a 12-min video preview of it are available on
our project website.
Given a product that is appropriate in content and format to widespread dis-
semination, how is this to be accomplished? We do not believe that direct sales
to individual coaches and parents would result in the desired level of dissemina-
tion. Therefore, we are currently working to find corporate and foundation sponsors
to deliver the training – free of charge – to youth sport organizations nationwide
(Munsey, 2010). Hopefully, sponsors will financially support production of signifi-
cant quantities of the MAC and MAPS programs, and they will be shipped to youth
sport organizations on behalf of the sponsors. The organizations will then distribute
the educational materials to their coaches and parents at no cost.
General Conclusion
Our translational approach has drawn upon methods, constructs, and the research
literature in a variety of fields to develop a conceptual model of coaching behaviors
that has guided our basic research. The basic research, in turn, has provided an
empirical basis for the development of behavioral guidelines that have proven their
efficacy in program evaluation studies. Finally, a number of our basic and applied
research results have definite translational value for other domains of psychology,
such as personality and social psychology and cognitive psychology.
14 Cognitive–Behavioral Coach Training: A Translational Approach to Theory, ... 245
Coach training has become a large-scale commercial enterprise in the United
States – most notably the American Coaching Effectiveness Program, the
National Youth Sports Coaches Association, and the Positive Coaching Alliance.
Unfortunately, however, virtually nothing is known about what effects these pro-
grams have on coaches and athletes and how well they achieve their objectives.
The absence of empirical attention is understandable, as developers of existing pro-
grams have been focused primarily on development and dissemination, rather than
evaluation, and they have not had the benefit of research grant support to guide
their work. Yet evaluation research is not only desirable but also essential. In the
words of Lipsey and Cordray (2000), “... the overarching goal of the program
evaluation enterprise is to contribute to the improvement of social conditions by
providing s cientifically credible information and balanced judgment to legitimate
social agents about the effectiveness of interventions intended to produce social
benefits” (p. 346). In concluding this chapter, it is appropriate to state our firm
belief that efforts to improve the quality and value of coach training programs
are best achieved by means of well-conceived and properly conducted evaluation
research.
Acknowledgments Preparation of this chapter and much of the research reported herein was
supported in part by Grant #1529 from the William T. Grant Foundation. Early phases of the
research program were supported by Grant RO1 MH24248 from the National Institute of Mental
Health.
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Chapter 15
Conclusions and Recommendations:
Toward a Comprehensive Framework
of Evidenced-Based Practice with Performers
Gershon Tenenbaum and Lael Gershgoren
In the search of laws, which govern human behavior, Skinner (1969) wrote “Science
is, of course, more than a set of attitudes. It is a search for order, for uniformities,
for lawful relations among the events in nature. It begins, as we begin, by observing
single episodes, but it quickly passes on to the general rule, to scientific law. If we
could not find some uniformity in the world, our conduct would remain haphazard
and ineffective” (p. 13). Our intent in this concluding chapter is to summarize chap-
ters in the book by identifying several salient themes that impact behavioral sport
psychology assessment, intervention, and research. In doing so, we present a frame-
work of evidence-based practice and associated recommendations that expand upon
many of the suggestions offered by chapter authors.
Measurement Issues
Research regarded as scientific must start with the smallest unit of analysis.
Consequently, the initial method of searching for truth and laws in human behavior
was termed Single Subject Research (SSR) and is also termed single case, inten-
sive, within-subject, repeated measures, and time series experimental design (Eldar,
2005, p. 543). Martin and Thomson (Chapter 1), referring specifically to sport psy-
chology, state that behavioral sport psychology relies on the principles of behavior
analysis and techniques, which are aimed at enhancing performance and satisfac-
tion. Briefly, they state that a target behavior must be articulated and then reliably
measured as prerequisite to examining the efficacy of any intervention. Thus, when
one seeks an evidenced-based practice, the behavior must be well defined, and the
tools should measure it as reliably as possible. They then assume that the treat-
ment or intervention follows the principles of Pavlovian and operant conditioning,
and state that cognition i s involved in the entire process. From a practical perspec-
tive, an intervention must be socially validated in that both coaches (and parents
G. Tenenbaum (B)
Florida State University, Tallahassee, FL, USA
e-mail: [email protected]
249
J.K. Luiselli, D.D. Reed (eds.), Behavioral Sport Psychology,
DOI 10.1007/978-1-4614-0070-7_15,
C
Springer Science+Business Media, LLC 2011
250 G. Tenenbaum and L. Gershgoren
if necessary) and athletes must share their thoughts about meeting goals, qual-
ity of the intervention, and its consequences. To satisfy these requirements (e.g.,
identification of target behaviors, identification of causes to behaviors, selection of
interventions, and evaluating interventions’ outcomes; Martin & Pear, 2011), Martin
and Thomson recommend using common inventories (or checklists) for measuring
general and sport-specific inventories, which have proved to be both valid and reli-
able. Furthermore, these requirements can be satisfied by self-instructional manuals
that can be used without the involvement of an expert, though more research is
needed to test the utility and effectiveness of these sport-specific, “easy to use”
measures.
Measuring overt and, especially, covert behaviors presents a major challenge to
scientists and practitioners alike. Nonetheless, we propose that the psychology of
performance must share the same accountability that the biology and physiology of
performance do. To what degree do the measures advocated by Martin and Thomson
in the first chapter of this book provide sufficient data on the performer’s psycholog-
ical state? How confident can the practitioner and the scientist be in the performer’s
response to these introspective measures? If evidenced-based practice is indeed a
requirement in the application of behavioral analysis and applications, then a robust
approach must be applied to the measurement component within the practice of
performance psychology.
We strive to use psychological measures that provide the practitioner with reli-
able indicators of the performer’s status in relation to his or her qualities before
any intervention was applied. We wish to have a yard-stick that consists of three
basic requirements: an origin (i.e., a zero point), a consistent unit of measurement,
and a linear continuum. In the absence of these requirements, the measures we use
to elicit quantities of variables such as anxiety, confidence, and goal orientation
among performers pre- and post-intervention are of limited value, and, thus, their
accountability is questionable at best.
An additional concern in the measurement of psychological state testing is the
inability to measure athletes’ thoughts and feelings during their performance, thus
relying mainly on retrospective measures, reflections, and observations post per-
formance. Tryon (Chapter 2) mentions devices such as touch-pads designed for
swimmers or high-speed cameras for analyzing movements and decision-making,
as well as counting energy expenditure. These are objective methods, which are
used to operationalize overt behaviors. Heart rate monitors and core temperature
are used as indirect measures of energy expenditure. Other measures of energy
expenditure consist of drinking water laced with stable isotopes of hydrogen and
oxygen, and measuring the loss of these i sotopes over time from saliva, urine, or
blood – a gold standard that is directly proportional to activity level. Tryon further
describes pedometers and actigraphs as direct measures of energy expenditures but
states, “It is important to know how much variability is associated with efforts that
people make to reproduce behaviors in the same way, because this level of variabil-
ity limits our ability to detect change such as improvements due to training.” All
instrumented measures of human activity level in applied contexts such as sports
necessarily confound instrument unreliability with human biomechanical, neural,
15 Conclusions and Recommendations: Toward a Comprehensive Framework ... 251
and psychological limits and will necessarily be more variable than instrument
reliability suggests. It is important for trainers and athletes to repeatedly measure
performances that they feel are the same and compare them with measurements of
behaviors that they feel are different.
Direct measurement of critical behaviors is at the heart of single-case evaluation
design according to Luiselli (see Chapter 4). Performance measures must be defined
in behavior-specific terms so that they can be recorded accurately and not be influ-
enced by observer bias. He further states, “Behaviors selected for intervention also
must change in a desirable direction, with minimal variability, and at a level that is
clinically significant. One of the standard guidelines when conducting a single-case
evaluation design is changing only one independent variable at a time per interven-
tion phase.” He advocates using video technology to increase the reliability of the
measured variable.
The assessment of cognitive processes is also a concern for Donohue, Dickens,
and Del Vecchio in Chapter 5. They recommend in vivo assessment for better
remembering cognitions that may be triggered in specific locations where a per-
formance is expected to occur. This kind of an assessment allows an “on-going
examination of cognitive restructuring exercises that are often assigned during inter-
vention phases. When in vivo cognitive assessment is not possible, we encourage
clients to bring videos of their performance to the office, and subsequently instruct
them to report cognitions they remember having during key events and activities.”
Assessment in their view is dependent on an interview that precedes the applied
measurement process. “Once the initial target cognitions are identified, we uti-
lize behavioral observation procedures to more specifically examine how thoughts
are related to actions in performance scenarios. Observations should occur in both
competitive and practice settings. Structured self-monitoring exercises may assist
in gaining an accurate representation of problems interfering with performance...
Athletes may be instructed to record the frequency of cognitions that occur within
a prescribed time frame (e.g., number of positive self-evaluation statements dur-
ing a 2-h block) and setting (e.g., practice, game, team launch). Alternatively,
specific thoughts and ratings of intensity can be recorded during critical points
of performance. As in behavioral observation, the antecedent stimuli (e.g., being
criticized) and consequences (e.g., threw ball away) of monitored thoughts should
be recorded to assist in understanding etiological factors maintaining the respec-
tive cognitions. Incorporating measurement and observations are functional in that
these determine which environmental stimuli and thought pattern affect behaviors in
real-life situations eliciting perceptions of pressure, and possibly negative emotions,
which are not necessarily facilitative. Soliciting evidence from the performer’s close
environment is an additional step to assure accountability.”
As one can notice, behavioral researchers, practitioners, and analysts draw much
attention to the measurement and assessment tools they use. No less important is
the reliance on several sources of observations and the use of various methods
required to elicit a reliable observation of the performer’s state to ensure that the
change in behavior is attributable to the intervention process (e.g., true variance)
and not to measurement unreliability (e.g., error variance). The concern of long
252 G. Tenenbaum and L. Gershgoren
and short introspective measures, however, remains problematic. It is commonly
believed that introspective measures must contain many items that represent the
variable we intend to quantify. The psychometric literature is overloaded with pro-
cedures describing methods of reliability and validity, which are needed to produce
sufficient measure. Surprisingly enough, the measures that share the highest ecolog-
ical validity are one-item scales such as the ones designed to measure self-efficacy
(Bandura, 2006), and rate of perceived exertion (RPE; Borg, 1998). Short and well-
defined one-item scales have been found to be easier t o use and of high predictive
validity in situations where rapid changes occur, such as momentum shifts, high
stakes events, and other situations and conditions performers encounter. Long intro-
spective measures can be effective for measuring traits and dispositions. However,
when one wishes to measure changes in state of either cognitive, perceptual, or emo-
tional states, short single-item measures are preferable (see Tenenbaum, Kamata, &
Hayashi, 2007, for detailed summary of this issue).
Several authors in this book have noted that behavioral analysis, which takes
place during a designated time frame, must be evident through a triangulation of
measures, observations, and reflections. This process is illustrated in Fig. 15.1.A
practitioner starting work with an athlete or a team must first identify and diagnose
the psychological and social components of interest (see Chapters 1, 2, 4, 5, 11, 12,
and 14 in this book). Once these components are defined, the practitioner selects the
measurement methods and tools to establish a baseline measure. More than one rel-
evant variable is usually selected. Regardless of applying any intervention, during
the practitioner’s involvement with the performer/team, evidenced-based practice
must be accompanied by the use of valid and reliable measures that are appropri-
ate to the given situation. Such measures include observations (in practices and
Process
Measure
Outcome Goal
Baseline
O
M
R
Intervention A
Intervention B
O
M
R
O
M
R
O
M
R
O
M
R
O
M
R
O
M
R
O
M
R
Planned path
Observed path
Fig. 15.1 Observation (O), measure (M), and reflections (R) during a given time frame.
Adjustment of intervention depends on the evidence gathered through O, M, and R
15 Conclusions and Recommendations: Toward a Comprehensive Framework ... 253
competitions), introspective questionnaires during competition when possible (or
retrospectively while watching footage using video), and reflections, which are trig-
gered and probed by the practitioner. Collecting relevant data through an ongoing
triangulated process enables one to make needed changes and adjustments in order
to achieve goals.
One should assume that the structural components of human performance,
such as emotional processes (i.e., feelings, mood), cognitive processes and struc-
tures (e.g., knowledge architecture, long-term working memory), motor processes
(coordination, endurance), and the neurophysiological basis of these structural
components (i.e., activation of cortical areas), have been studied independently.
Tenenbaum and Land (2009) postulated that
every action made by humans is a consequence of response selection, whether intentional
or unintentional. By definition, response selection indicates adaptive behavior based upon
the capacity to solve problems. Cognitive processes and mental operations underlie this
‘‘behavioral effectiveness.’’ The effectiveness of these processes consists of the richness
and variety of perceptions processed at a given time; that is, the system’s capacity to encode
(store and represent) and access (retrieve) information relevant to the task being performed
...Under pressure, changes in each functional component may occur. These changes can
affect the perceptual components, continuing with the cognitive components, and ending
with the motor system ...To capture changes in the perceptual-cognitive–motor linkage
under varied conditions of pressure and evoked emotions, we must use research paradigms
that integrate the cognitive structure components and processes (cognitive appraisal), emo-
tional system, and the self-regulation structure (i.e., emotional control, motivation control,
attentional control, etc.) ...allowing for detection of a collapse in the perceptual-cognitive
linkage under altered emotional states, and their subsequent effects on the motor system
(pp. 251, 252).
Thus, an evidenced-based practice is one that captures all relevant components
simultaneously – not in isolation. Interventions aimed at controlling emotions or
solving problems, for example, must incorporate not only “emotional measures”
but also measures of visual-perceptual behaviors, information processing, atten-
tion and anticipation, as well as measures of the motor system. These must also
be observed during competition when pressure is evident and later reflected upon
by the performer. A conceptual framework of each task is necessary for defining its
components and observing them under varying conditions. One should keep in mind
that all types of information, including emotions’ primed cognition and actions,
are stored in long-term memory in the form of a mental representations hierarchy
(Ericsson & Kintsch, 1995). Choking under pressure, or alternatively performing
optimally, depends on the extent to which appropriate neural schemas are retrieved,
along with effective activation of the motor system. Evidenced-based practice must
develop the tools to capture the components that are enhanced when an intervention
takes place, including sensational-perceptual, cognitive, motor, or any other combi-
nation. These ideas are captured in Fig. 15.2, and further elaborated in Tenenbaum
et al. (2009) and Tenenbaum and Land (2009). In a recent publication, Schack (in
press) described how mental representations can be measured and how changes in
these structures can indicate the efficacy of an intervention. Once this concept is
applied to the behavioral analysis practice, effects of cognitive interventions such
254 G. Tenenbaum and L. Gershgoren
Process
Measure
Baseline
Intervention A
Intervention B
Anticipation Information
Processing
Measure
Anticipation Information
Processing
Measure
Anticipation Information
Processing
Measure
Pressure
Pressure
Fig. 15.2 Baseline measures of mental-representations (i.e., mental schemas/maps) at baseline
(no pressure) following intervention A (pressure causing schema impairment) and intervention B
(pressure unaffecting the original schema structure)
as self-talk and imagery can be more reliably elicited in integrating in vivo and
enhanced-memory techniques in practice (see Donohue, Dickens, & Del Vecchio in
Chapter 5).
An Innovative Idiographic Approach
One of the innovative ideas developed in the s port psychology domain was cap-
tured first by Hanin’s (2000) conceptualization of defining the Individual Zone of
Optimal Functioning. This idea was introduced several decades earlier, but was
enhanced 11 years ago when not only “anxiety” but also the entire spectrum of
emotions was believed to be associated with performance quality. The idea was that
each individual feels and thinks in a certain manner while “in the zone” and in
another manner while being out of the zone. Thus, the practitioner/performer can
first define the affect-related performance under which the performer is most likely
to be in or out of the zone. Once the zones are defined, an intervention strategy for
securing more occurrences of the former than the later can take place. A challenge,
however, is in measuring the affective variables and the performance variables, and
then contrasting them each against the other to define the zone. Hanin’s conceptu-
alization is based on retrospectively recalling emotions associated with outstanding
(i.e., optimal) and poor performances. The main shortcoming of this methodology
is that in retrospect, emotions are very much influenced by performance outcomes
and that similar emotions can be felt in all zones of functioning, and thus cannot
be considered deterministically. To better define the zones of functioning, Kamata,
Tenenbaum, and Hanin (2002) developed the probabilistic method for defining the
15 Conclusions and Recommendations: Toward a Comprehensive Framework ... 255
Individual Affect-Related Performance (IAPZ) for each performer. It consists of
measuring in real time (or while watching the performance on video, which is a
more biased methodology, but in most cases unavoidable) the affective valence
and intensity (using an affect grid), along with any subjective or objective mea-
sure of performance simultaneously. When repeating this process many times in
one competition, and across many competitions, affect and performance measures
are contrasted to each other via ordinal logistic regression. The resulting regres-
sion coefficients are then used as an input in a graphic algorithm, which defines
all affect-related performance zones for each single athlete. Each functional zone is
probabilistic in nature; i.e., it provides the range of the affective values associated
with poor performance and its probability to occur if the performer feels within this
range. Similarly, it provides the value range for moderate and optimal performance
zones – all probabilistic in nature. Once the IAPZs are defined, the practitioner can
draw the functional zone on a figure and then the state of the performer at each point
of observation time. Such an illustration is presented in Fig. 15.3.
As one can notice, after defining the probabilistic zones’ values, these values
are transposed onto the y-axis (see Fig. 15.3). The single-affect observations are
then inserted into the figure at the time they were taken, thus creating a profile
where shifts among the zones are illustrated. One should keep in mind that affec-
tive state can be replaced or accompanied simultaneously by other measures of
emotions, or physiological measures, such as heart rate (HR), heart rate variabil-
ity (HRR), breathing pattern (frequency and depth), galvanic skin response (GSR),
EEG measures, and others. As illustrated in Fig. 15.3, the ideal state would be when
the performer shows consistent behavior and stays in the optimal zone. However,
the illustrated performer showed intense arousal at the outset of the competition,
0
10
Observations During Competition
Arousal
Poor Above
Moderate Above
Optimal
Moderate Below
Poor Below
ZONES:
Fig. 15.3 An observational profile of a single performer in competition: Fluctuations among
individual affect-related performance zones (IAPZs)
256 G. Tenenbaum and L. Gershgoren
which placed him/her above the optimal zone of functioning; he/she returned to the
optimal zone and then continued to fluctuate among the high and moderate zones
above and below the optimal zone until achieving a relatively stable state within
the optimal zone. As the practitioner and the performer watch the competition on
a video, the practitioner may stop the film at each of the observation points and
ask the performer to reflect upon his feelings and thoughts in detail at this point.
When many reflections are gathered, some generalizations about the affective states
and performance can be made and linked into the environmental and social condi-
tions. In this way, when one designs an intervention, such a method can provide
platforms for detecting difficulties or successes in the implementation of the inter-
vention and enables modifications and changes in the intervention to be made. The
practitioner who wishes to systematically collect evidence of the intervention imple-
mented, whether it is through goal setting and performance feedback (see Ward
in Chapter 6), cognitive–behavioral strategies (see Brown in Chapter 7), detecting
behavioral markers of momentum shift (see Roane in Chapter 9), or examination
of fluency, efficiency, and automaticity of performance under pressure and evoked
emotions (see Martens & Collier in Chapter 10), is encouraged to integrate the IAPZ
method into the intervention paradigm so that a clear picture of what intervention
outcomes and process will emerge.
Using the Idiosyncratic Probabilistic Approach
Several practitioners and scholars have used the IAPZ idiosyncratic concept in both
research projects and practice using introspective and physiological measures of
arousal and defining the IAPZs of the performer. We briefly describe their work
below, according to sport type.
Golf. Van der Lei (2010) implemented a multimodal assessment approach in
which the probabilistic relationship between affective states and both performance
process and outcome measures was determined. Three male golfers of a varsity team
were observed during three rounds of competition. Introspective (i.e., verbal reports)
and objective (heart rate and respiration rate) measures of arousal were incorporated
to examine the relationships between arousal states and process components (i.e.,
routine consistency, timing) and outcome scores related to golf performance in com-
petition. Results revealed distinguishable and idiosyncratic IAPZs associated with
physiological and introspective measures for each golfer. The associations between
the IAPZs and decision-making or swing/stroke execution were strong and unique
for each golfer. While observing the golfers, Van Der Lei uncovered two pre-routine
time phases (e.g., information processing and confirmation) and two post-routine
time phase (evaluation and reorientation). Comparison of the temporal patterns
associated with the four functional time phases indicated more consistent time use
by the golfers during the confirmation and evaluation phases immediately preceding
and following the task execution (i.e., swing or stroke), respectively, compared to the
information-processing phase and the reorientation phase preceding and ensuing the
15 Conclusions and Recommendations: Toward a Comprehensive Framework ... 257
task execution (i.e., swing or stroke), respectively. Consequently, an hourglass per-
formance (HP) model for golf was developed to illustrate the relationship between
a golfer’s information-processing pattern and the functional performance phases
in golf.
Cohen, Tenenbaum, and English (2006) applied the probabilistic approach in a
study involving female collegiate golfers. They were interested in defining the rela-
tionship between two dimensions of affect (i.e., arousal level and pleasantness) and
functionality (i.e., how helpful the affective state was to performance) in relation
to objective and perceived performance levels. In addition, they examined how per-
ceived affect and golf performance change following a psychological skills training
(PST) intervention. Cohen et al. (2006) utilized a multiple case study format to
identify and refine individual IAPZs for the participants. The profiles and assess-
ment of psychological strategies employed during practice and competition were
used to develop a brief PST intervention. The PST intervention targeted the psycho-
logical and emotional strategies of self-talk, emotional control, imagery, relaxation,
activation, resistance to disruption, negative thinking, attention control, and auto-
maticity. Results indicated that the IAPZ concept was supported via probabilistic
estimations in that varying levels of affect were associated with different levels of
performance within and between the participants (i.e., each participant maintained
unique and idiosyncratic IAPZs). Additionally, the PST intervention resulted in the
participants’ attainment of optimal affective states via psychological and emotional
self-regulation strategies, which ultimately led to improved performance.
Race-car simulation. Edmonds, Mann, Tenenbaum, and Janelle (2006) con-
ducted an exploratory investigation of the IAPZ model by integrating perceived
affective states (i.e., arousal, pleasantness) and physiological measures of arousal
(i.e., heart rate and skin resistance) online in a competitive driving simulator.
Participants in the study were given the same race settings on the race simulator and
were required to drive a total of five race trials (i.e., one trial equaled four laps), with
the main goal to reach the finish line as fast as possible. Participants completed all
four laps of each trial in succession; however, between trials they were able to take
a 3-min break. Indicators of heart rate and skin conductance were taken simultane-
ously with the perceived measures of arousal pleasantness at three different stages
of a lap. Results indicated that athletes maintained idiosyncratic performance zones.
The distinct IAPZ profiles linking arousal and performance that were revealed in
the driving simulation are also indicative of relative changes in driver performance,
which were indexed by changes in physiological parameters. Furthermore, a driver’s
performance could be determined by the driver’s level of arousal or activation.
Edmonds, Tenenbaum, Mann, Johnson, and Kamata (2008) used the IAPZ prob-
abilistic method to verify the utility and effectiveness of a biofeedback intervention
by manipulating affective performance states in a race-car simulator. Nine males
completed five separate time trials of a simulated racing task and were then ran-
domly assigned to one of three arousal regulation treatment conditions: (1) optimal,
(2) poor, and (3) attention control. Following the biofeedback intervention, par-
ticipants underwent another series of race trials to determine the effectiveness of
the arousal regulation intervention. The results indicated relative similarities in the
258 G. Tenenbaum and L. Gershgoren
strength and direction of the perceived and physiological states between the partici-
pants; however, the subtle details of the participants’ unique performance zones and
the probability of achieving each zone were revealed to be unique among the par-
ticipants. The results also indicated that the biofeedback manipulation resulted in
the expected changes for each participant, though some large individual differences
among them were noted.
Tennis. Golden, Tenenbaum, and Kamata (2004) conducted a study utilizing
the probabilistic approach with collegiate female tennis players. They established
IAPZs for each player across an entire season of tennis. Additionally, through-
out the season after each match, they administered positive–negative affect scales
(PNA; Hanin, 2000) and flow state scales (FSS; Jackson & Marsh, 1996). Results
revealed that the IAPZs for each athlete were unique and distinct. In addition, they
found that when the athletes were performing at optimal or near-optimal levels, they
were experiencing elevated levels of flow. The results from the PNA revealed that
during optimal performances, pleasant emotions perceived to be helpful were most
prevalent. However, during moderate and poor performances, unpleasant emotions
perceived to be harmful became elevated.
Johnson, Edmonds, Tenenbaum, and Kamata (2007) extended the examination of
the IZOF model by utilizing the IAPZ model to determine IAPZs in male collegiate
tennis players. They observed the athletes across an entire season and found that
the linkage between affect and performance was of a dynamic nature, where affect
level during competition has an effect on performance, though this effect is unique
for each player. The athletes in the study demonstrated distinguishable and unique
IAPZs for the two affect dimensions (e.g., arousal and pleasantness) and function-
ality (e.g., how do affect and pleasantness are functional for t he performance). This
research is in line with Hanin’s (2000) IZOF model, which is built upon the pillar
that the affect–performance linkage is unique to each individual.
Archery. Johnson, Edmonds, Moraes, Filho, and Tenenbaum (2007)usedthe
probabilistic IAPZ method to explore the dynamic nature of within-competition
perceived affect-performance and heart-rate performance linkage in one world-
class archer. Multiple competitions at five different shooting distances (18, 30, 50,
60, and 70 m) were observed throughout the entire competitive international sea-
son. The findings illustrated the archer’s unique IAPZs at each shooting distance.
Furthermore, affective state fluctuations were noticed among the IAPZs during com-
petition, which necessitated the utilization of different self-regulatory methods when
these were noticed by the archer.
Filho, Moraes, and Tenenbaum (2008) applied the IAPZ method for s tudying the
link between affective states and athletic performance for the purpose of determin-
ing graphic profiles associated with optimal and nonoptimal performance in three
Brazilian male archers. Data were collected throughout a whole competitive sea-
son during competitions at different shooting distances. The archers reported their
perceptions of arousal and pleasure and had their heart rate responses recorded.
Results indicated that (a) the archers possess unique IAPZs for the different archery
shooting distances, (b) they fluctuated among their optimal and nonoptimal IAPZs
15 Conclusions and Recommendations: Toward a Comprehensive Framework ... 259
throughout the season, and (c) a consecutive optimal performance was not prevalent
following an initial optimal performance.
Tutorial on IAPZ. Practitioners and researchers interested in the use of the IAPZ
method are encouraged to read the development of the theoretical and mathemat-
ical components of it in Kamata et al. (2002). However, a clear and easy-to-read
and -capture tutorial of the IAPZ method can be found in the article of Johnson,
Edmonds, Tenenbaum, and Kamata (2009). The methodology described in this
tutorial consists of eight steps: (a) collecting data, (b) categorizing affect and perfor-
mance level, (c) converting the data, (d) performing logistical ordinal regressions,
(e) creating IAPZ curves, (f) creating IAPZ profile charts, (g) plotting within com-
petition states onto IAPZ profile charts, and (h) utilizing IAPZs to select, implement,
and evaluate performance enhancement strategies.
Linking the IAPZ to the Individual Psychological
Crisis Theory
In a recent publication Tenenbaum, Edmonds, and Eccles (2008) linked the IAPZ
to the Individual Psychological Crisis Theory (IPCT; Bar-Eli & Tenenbaum, 1989),
which is congruent with the concepts of behavioral analysis and evidenced-based
practice. The IPCT views the athlete as a dynamic and open system that responds
to environmental stimuli with certain probability levels. The athlete continuously
processes information and makes decisions (DM) aimed at maximal adaptation to
the environmental conditions via the reduction of event uncertainty. Physiological
arousal is viewed as the energizing component of motivation, with the cognitive
component providing its direction. Their relationship is viewed within a bilat-
eral transaction process (Nitsch, 1982). Physiological arousal may be unaffected
by cognitive directional mechanisms or, alternatively, controlled or regulated by
them. Continuous exposure to similar situations and conditions shifts the operational
mode of the system from intentional to an automated mode. According to Nitsch,
automaticity via the adaptation process reduces the vulnerability of the system to
“choking” under pressure and/or uncertainty. Therefore, performance expertise may
be determined through the extent to which an athlete has assimilated and accommo-
dated the arousal-coping strategies, which are linked through exposure to practice
and competition. In this respect, the acquisition of expertise in an anxiety-provoking
setting requires deliberate practice under conditions of high arousal and uncertainty.
The extent to which vulnerability to crisis depends on experience and expertise level
is related to the quality and quantity of cognitive mechanisms available to cope with
the emotional state of the athlete, the level of attained simplification and routine
of relevant responses, and the point of balance between damaging and contributing
effects resulting from the emotional state of the athlete.
The IPCT was the first theoretical concept to describe the emotion–performance
relationship in a probabilistic nature and a continuous time frame. In other words,
when a performer is in a given stressful situation, experiences high pressure and
anxiety, cannot pay attention to the task, and lacks self-regulatory mechanisms to
260 G. Tenenbaum and L. Gershgoren
reduce pressure, it is highly probable that he/she will choke or face a significant
performance decline. Though the IPTC referred merely to arousal/activation levels
of the athlete at a certain time point during competition, other emotions have simi-
lar relevance in the emotion–performance linkage. Relying on the classical inverted
“U” function, the athlete was considered to be in a phase of hypoactivation, optimum
activation, or hyperactivation at any moment in time. The probability of a psycho-
logical crisis occurring increases as the individual shifts away from the optimal state
toward the hypo- or hyperactivation states. However, these can be determined by the
IAPZ concept, as a descriptive method, the changes in cognitive schema (Schack,
in press), and the classical behavior analysis methods advocated in this book.
Additional Thoughts
We believe that the IAPZ method and the IPCT conceptual framework, along with
the concept of triangulation of measures, provide a sound framework for practi-
tioners who work with performers. An important issue, which was not addressed in
this chapter, is the quality and soundness of the interventions practitioners use to
enhance the mental, cognitive, and emotional states of the performer. Interventions
must share theoretical, scientific, and empirical soundness as one expects from mea-
sures used to quantify feelings, states, thoughts, and disposition. The scientist is
eager to explore the underlying mechanism of a phenomenon under investigation.
Medical interventions, for example, cannot be implemented without being able
to explain how and what changes have occurred in the biological and/or mental
states of the patient. Interventions with performers are aimed at changing a mental,
behavioral, emotional, or motor status quo, or alternatively maintaining a desired
state to secure high-level performance, a state that will allow mental schemas to
remain intact and retrievable. Consistent monitoring of outcome and process behav-
iors inherent in behavioral analysis is an important measure toward establishing an
evidenced-based intervention.
One of the most paramount approaches to the design of evidenced-based inter-
vention is presented in Chapter 14 by Smith and Smoll. In the outset of their chapter,
Smith and Smoll write, “In this chapter, we describe the evolution of a program of
research on coaching behaviors and interventions that has spanned more than three
decades and has resulted in an empirically-supported coach training program. We
describe the manner in which a theoretical model derived from the areas of learning,
social and personality psychology, and developmental psychology has helped guide
a program of basic and applied research. Research results derived from the basic and
applied research have guided the intervention program’s development, and research
results from basic research and program evaluations address important theoretical
issues.” The approach Smith and Smoll have taken is based on an integrated theoreti-
cal framework, which they used for designing measures and interventions. However,
they believed that without first designing reliable and valid measurement tools, they
would have been unable to validate or refine their theoretical framework. Each tool
that was developed underwent a rigorous process of experimentation using large
15 Conclusions and Recommendations: Toward a Comprehensive Framework ... 261
samples of young athletes and coaches. Only at this s tage they concluded, “Data
from our basic research indicated clear relations between coaching behaviors and
the reactions of youngsters to their athletic experience. Along with findings from
research inspired by achievement goal theory, these relations provided a foundation
for developing a set of coaching guidelines that formed the basis for an intervention
that was initially called Coach Effectiveness Training. With the later emergence of
achievement goal theory and the wealth of research it inspired, we incorporated
its principles into an evolved program called the Mastery Approach to Coaching
(MAC) that explicitly focuses on the development of a mastery motivational cli-
mate. This emphasis is highly consistent with principles (particularly our conception
of success as doing one’s best and striving to maximize one’s potential) that have
been emphasized in CET from the beginning.” When this stage was accomplished,
an extensive outcome research was planned and performed to refine all aspect of the
interventions design for coach education. Since then, 500 CET and MAC workshops
have been provided to more than 25,000 coaches.
There is no doubt that three decades of experimentation, practice, and devel-
opment of a sound intervention is a life-long job of the scientist-practitioner who
wishes to be accountable and helpful to performers. It is not always the desire of the
practitioner who must provide immediate and reliable intervention to a performer
or a team. However, the practitioner should have in mind that both the interventions
and the measurement tools he/she uses must be related to a sound theoretical frame-
work that was proven to be accountable and reliable. He or she must also follow
a scientific reasoning when implementing interventions and measuring overt and
covert behaviors. We attempted to introduce such a framework in this concluding
chapter.
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Index
Note: Letters ‘f’ and ‘t’ following the locators refers to figures and tables cited in the text.
A
AAN, see American Academy of Neurology
(AAN)
AASP, see Association for Applied Sport
Psychology (AASP)
ABA, see Applied behavior analysis (ABA)
A-B-A-B design, 65–68
A-B-A-B-B+C-A-B+C design, 66f
A-B+C-A-B+C-B-A-B design, 66f
hypothetical data, 65f
one-day reversal effect, 67f
research examples, 67–68
ABC model, 89
Acceleration-deceleration force, 180, 184
Achievement Goal Scale for Youth Sports
(AGSYS), 241, 243
Acrophase, 39
Across-sport behavioral checklist
athletic coping skills inventory-28, 15
post-competition evaluation form, 15
psychological skills inventory for sport, 15
ACSM, see The American College of Sports
Medicine (ACSM)
Actigraphs
advantages
proportionality, 37
time-locked repeated measurements, 37
circadian applications, 38–39
diurnal activity, 39–40
sleep, importance, 37–38
sleep, improvement, 39
vendors and devices, 28t–29t
Actigraphy
application to sports, 34–40
actigraphs, 37–40
pedometers, 34–37
human activity, measurement, 25–34
direct methods, 26
–30
indirect methods, 25–26
methodological issues, 30–34
ActiTrainer Solution Package, 39
A/D, see Analog-to-digital (A/D) converters
Aggressive behavior
conceptual and operational criteria of, 203
self report, athlete, 204–205
See also Direct observation method;
Methodologies, sport-specific
aggression; Prevention-based
intervention
Alternating treatments design (ATD), 75–77,
76f
behavior-altering effect, 75
research examples, 76
American Academy of Neurology (AAN), 179
The American Academy of Sleep Medicine, 37
The American College of Sports Medicine
(ACSM), 35
Amyotrophic lateral sclerosis, 191
Analog-to-digital (A/D) converters, 30
Analysis of variance (ANOVA), 31, 62
ANAM, see Automated Neuropsychological
Assessment Metrics (ANAM)
ANOVA, see Analysis of variance (ANOVA)
APoE-e4 allele, 189
Application, major areas, 8–16
athletes, decreasing problem behaviors,
10–11
athletic performance, managing emotions,
11–12
confidence and concentration, maximizing,
14–15
decreasing persistent errors, 10
motivating practice and fitness training,
8–9
263
J.K. Luiselli, D.D. Reed (eds.), Behavioral Sport Psychology,
DOI 10.1007/978-1-4614-0070-7,
C
Springer Science+Business Media, LLC 2011
264 Index
Application, major areas (cont.)
new sport skills, 9–10
self-talk/imagery training, 12–14
user-friendly behavioral assessment tools,
development, 15–16
user-friendly sport psychology manuals,
development, 16
Applied behavior analysis (ABA), 7–8, 106
Archery, 258
Archival methodology, 201–202
Arena Football League, 50
Arousal management, 81–82
anxiety and self-confidence, relation, 82
influencing factors, 81
optimum, 82
Association for Applied Sport Psychology
(AASP), 8, 77, 117
ATD , see Alternating treatments design (ATD)
Athlete
cognitive–behavioral attitudes, 239–240
coping skills inventory-28, 15
self report, 204–205
decreasing problem behaviors, 10–11
interaction, coach, 228
perceived behavioral ratings, 239
performance managing emotions, 11–12
self-esteem, 234–235
See also ABC model; Prevention-based
intervention; Proficiency techniques
Athlete-perceived behavioral ratings, 239
Athletic Achievement Motivation Scale, 86
Athleticism, 115
Attitudes
help follow-through, 83
help-seeking, 83
Attrition, 242–243
Autocorrelation,
31
Automated Neuropsychological Assessment
Metrics (ANAM), 188
Automatic repertoires
in changing situations, 159
neuromuscular adaptations, 168
stretching or flexibility training,
171–172
See also Deliberate practice; Muscular
adaptations
B
Bag of tricks mentality, 119
The Baseball Economist: The Real Game
Exposed, 45
Baseball Is Played All Wrong, 44
Baseline assessment model, 185
Basic research on coaching behaviors
behavioral signatures, 230–231
coaching behaviors, measurement
behavioral assessment techniques, 229
coaching behaviors and children’s
evaluative reactions, 231
situational and personality moderators of
behavior–attitude relations
game situation, 232–233
motivational climate, 233–234
theoretical underpinnings
athlete self-esteem, 234–235
coach–athlete interactions, 228
cognitive revolution, 228
Basic science, 227
BDNF, see Brain-derived neurotrophic growth
factor (BDNF)
Behavioral coaching intervention
imitation, 67
modeling, 67
performance feedback, 67
positive and negative reinforcement, 67
systematic verbal instructions, 67
Behavioral goal, 121, 135, 239
Behavioral momentum
metaphor extension to other sports,
151–154
coaching tactics, 153
reinforcement rates, 152
momentum and behavior analysis, 146–147
evaluation, 146
low-probability vs. high-probability
tasks, 147
metaphorical application, 146
previous research, 147–151
commencing events, 148
conceptualization, 148
data analysis procedure, 150
data collection, 148–149
property, application, 143
psychological aspects, 144–146
complex models, 145
conceptualization and quantification,
145
multidimensional definitions, 144–145
technical application, 144
Behavioral momentum principle, 53
Behavioral observation
intent to harm, 204–205
role, in prevention based intervention,
206–208
See also Direct observation method
Behavior-altering effect, 75
Index 265
Behavior-reduction measure, 62
Between-group statistical analyses, 62
BMI, see Body mass index (BMI)
Body mass index (BMI), 35–36
Brain-derived neurotrophic growth factor
(BDNF), 128
Bredemeier Athletic Aggression Inventory
(BAAGI), 200
BuzzBee
TM
actigraph, 33
C
Calling a time-out strategy, 149, 151–153
The Canadian Society for Psychomotor
Learning and Sport Psychology, 3
CDC, see The Center for Disease Control
(CDC)
The Center for Disease Control (CDC), 129,
181, 192–193
Cerebral concussion
epidemiology, 181–185
acute effect of history, 183–184
animals vs. humans, 184
gender, 182–183
head injury, emergency room
admissions, 182t
physical forces, 184–185
soccer players vs. football players, 182
CET and MAC workshops in U. S and Canada,
243
CET/MAC intervention, 238–239, 242–243
CG, see Control group (CG)
Changing criterion design, 73–75
hypothetical data, 74f
research examples, 74–75
rewarding, 75
Chronic traumatic encephalopathy (CTE), 191
Church-based health-promotion intervention,
131
CINAHL, 36
Clinical repeatability, 33–34
Coach effectiveness training, 236, 261
Coaches
good behavior contracts, 206–207
Coaching Behavior Assessment System
(CBAS),
229–231, 238–239
Cochrane Library, 36
Coefficient of variation (CV), 32
Cognitive assessment
common factors affecting, 79–83
See also individual entries
empirically guided method, description,
83–91
ABC models for use in athletes, 89t
behavioral observation, 87–88
cognition, frequency recording, 89
collateral informants, 83–84
functional assessment and analysis,
89–90
functional hypotheses, testing, 90–91
observational effects, 88
psychometrically validated scales,
84–87
SARI sample items, 86t
self-monitoring, 88–89
setting, 83
SIC sample items, 85t
familial relationships, 82
primary focus, 82
Cognitive–behavioral coach training
basic research on coaching behaviors
coaching behaviors, measurement,
229–231
coaching behaviors and children’s
evaluative reactions, 231
situational and personality moderators
of behavior–attitude relations,
232–235
theoretical underpinnings, 228–229
outcome research
achievement goal orientation, 240–241
athlete attitudes, 239–240
coaching behaviors, 238–239
dropout rate, 242–243
performance anxiety, 241–
242
self-esteem, 240
translating basic research findings into
coach intervention
Coach Effectiveness Training, 236
MAC principles and goals, 236–238
“positive approach” to coaching,
236–237
Cognitive–behavioral strategies
alliance building, 115–117
clients and their language, relation, 116
and sport psychology, brief history,
116–117
behavioral interventions, 120
development, 117
evidenced-based self-talk, 122–123
goal setting, 120–122
imagery, 123–124
psychological advice, 114–115
session with college football player, 114
use by a thletes, 115t
theory and athlete, protection, 117–120
myth 1: collection of tricks, 118
266 Index
Cognitive–behavioral strategies (cont.)
myth 2: immediate effects, 118
myth 3: physical training, comparison,
118–119
myth 4: psychopathological conditions,
119
myth 5: competent professional,
119–120
Cognitive–behavior therapy, 7
Cognitive processes
experimentation, 118
practice, 118
restructuring, 118
self-monitoring, 118
Cognitive processes, assessment, 251
Cognitive tracking assignment, 88
CogSport, 188
Collision frequency, 181
Community-based program, 135
Competitive State Anxiety Inventory-2, 87
CompuSports
©
, 56
Computed tomography (CT), 180
Computerized neurocognitive screening
measure, 186
Computer simulation model, 184
Concentration, 12, 14–15, 17, 87, 171–172,
188, 215–216, 221, 223, 242
Concussion Resolution Index (CRI), 188
Conditioned seeing, 13
Confidence, 14–17, 34, 36–37, 81, 84, 87, 124,
250
Congruence method, 88
Consumer-driven evidence-based care, 91
Contact sports, 143
Contingent musical reinforcement, 109
Control group (CG), 38, 128, 136, 185,
240–241
Control theory (CT)
hierarchical organization, 132
key i ntervention components
feedback, 132
goal review, 132
goal setting, 132
self-monitoring, 132
loop functions, 132
meta-regression, 133
Cook-book methodology, 119
Coping Inventory for Sport, 87
Core body temperature, 26
Covert behaviors, 4, 7, 250, 261
CPSC, see The United States Consumer
Product Safety Commission (CPSC)
CRI, see Concussion Resolution Index (CRI)
Criterion, 73
Cronbach’s alpha, 32
Cross-fit programs, 171
40 CSA Model 7164 actigraph, 33
CT, see Computed tomography (CT); Control
theory (CT)
CTE, see Chronic traumatic encephalopathy
(CTE)
CV, see Coefficient of variation (CV)
D
Deceptive advertising practices, 211
Delayed onset muscle soreness (DOMS), 171
Deliberate practice
goals, 161–165
physiological concepts, 168
as proficiency technique, 167–168
RESAA, benefits of, 163–164
strategies, 160
See also Proficiency techniques
Department of Health and Human Services,
129
Descriptive and verbal feedback (DF + VF), 72
Descriptive (nonverbal) feedback (DF), 72
Desire for Sport Psychology Scale (DSPS), 84
The Developmental and Control of Behavior in
Sport and Physical Education, 3
Dietary and nutritional supplements, 213–214
Dietary Supplement and Nonprescription Drug
Consumer Protection Act, 212
Dietary Supplement Health and Education Act
of 1994 (DSHEA), 212–213
Dietary supplements, effects, 212
Difficult-to-complete instruction, 147
Digital step counters, 26–28
Direct observation method
acquisition of, aggressive behavior,
205–206
advantages of, 203–204, 206
as data collection tool, 205
multiple coders, 203
rule violation, athletes, 208
socialization process in, 205–206
validity measurement, 202–204
videotape, using of, 203
Disengagement-oriented coping, 87
Dispositional Flow Scale, 87
Distraction-oriented coping, 87
Dose–response effect, 128
Doubly labeled water, 26
DSPS, see Desire for Sport Psychology Scale
(DSPS)
Index 267
E
Early development, 3–4
Easy-Scout XP Plus
©
, 56
Easy-Scout XP Professional
©
, 56
Ecological validity, 102, 105, 200–201, 204
Electrophysiological technique, 180
EMBASE, 36
Enhanced-memory techniques, 254
Environmental–behavioral relations, 46
ERIC, 36
Error variance, 251
Estrogen, 183, 223
ETG, see Evening training group (ETG)
Evening training group (ETG), 38
Evidenced-based practice with performers
additional thoughts, 260–261
Innovative Idiographic Approach, 254–256
linking IAPZ to Individual Psychological
Crisis Theory, 259–260
measurement issues, 249–254
using the Idiosyncratic Probabilistic
Approach, 256–259
Evidenced-based self-talk
cognitive errors, 123
intervention and discussion, 122–123
sample self-talk l og, 123t
vignette, 122
See also Cognitive–behavioral strategies
Exercise induced muscular damage (EIMD),
222
Exogenous vs. endogenous estrogen, 183
External validity, 63
F
Fact-to-face intervention, 136
Fear of failure (FF), 87, 236
The Feeling Good Handbook, 123
FF, see Fear of failure (FF)
Flow state scale (FSS), 87, 258
fMRI, see Functional MRI (fMRI)
Force–mass relationship, 183
Form training intervention,
70
Four-step strategy, 12
Freeze strategy, 106
FSS, see Flow state scale (FSS)
Functional MRI (fMRI), 180
Function-based behavior, 101
G
Gadd Severity Index, 184
Gender-discrepant incidence, 183
Goal-directed activity, 127
Goal setting
behavior, consequences, 102–103
combined with performance feedback, 103
commitment, gaining, 104
definition, 99–105
evidenced-based principles, 101–103
interventions principles without direct
assessment, 103–105
presentation statements, 100
definition from psychologist, 120
intervention and discussion, 121–122
brief interchange, example, 121
two-question test, 121
literature examination, 100–101
public goals vs. private goals, 103–104
as a rule, 100
single-subject design studies, 102
types
behavioral, 121
outcome, 121
performance, 121
vignette, 120
See also Cognitive–behavioral strategies
Gold standard criterion, 38
GraphPad Prism
R
, 57
GT3X model, 39–40
H
Hands-on method, 83
Hanin’s conceptualization, 254
Head Injury Criterion (HIC), 184–185
Heart rate, 26
Heel-toe transition, 26
HIC, see Head Injury Criterion (HIC)
Hourglass performance (HP) model, 257
Human performance, structural components,
253
Human physique changes, 118
I
IAPZ, see Individual Affect-Related
Performance (IAPZ)
IAPZ fluctuations, 255f
IBM SPSS Statistics Base
R
, 57
Idiosyncratic Probabilistic Approach, 256–259
Imagery, 81
controllability, 124
facilitating factors, 81
intervention and discussion, 124
kinesthetic movement, 81
positive experiences, 81
vignette, 124
vividness, 123
268 Index
Immediate Measurement of Performance and
Cognitive Testing (ImPACT), 188
ImPACT, see Immediate Measurement of
Performance and Cognitive Testing
(ImPACT)
Individual Affect-Related Performance
(IAPZ), 255–260
Individual-player basis, 152
Individual Psychological Crisis Theory (IPCT),
259–260
Individual Zone of Optimal Functioning, 254
In-game performance, 72
Injuiries, classification
grade I (mild), 179
grade II (moderate), 179
grade III (severe), 179
Innovative delivery mechanism, 136
behavior change principles, 136
Innovative Idiographic Approach, 254–256
Instrument reliability, 31–33
Instrument validity, 34
Interdisciplinary cybernetic control theory, 132
Internal validity, 63
The International Society of Sport Psychology,
3
Intervention techniques, 73
Inverted-U relationship, 12
IPCT, see Individual Psychological Crisis
Theory (IPCT)
iPod Touch
TM
, 137
Irritable bowel syndrome, 119
IZOF model, 258
J
Journal of Applied Behavior Analysis, 3, 77
Journal of Clinical Sport Psychology, 77
Journal of Quantitative Analysis in Sports, 45
Journal of Sport Behavior , 77
Judegments About Moral Behavior in Youth
Sport Questionnaire (JAMBYSQ),
200
JW200 pedometer engine, 33
K
KS10 pedometer engine, 33
L
Large N method, 61
LOC, see Loss of consciousness (LOC)
Los Angeles Times, 191
Loss of consciousness (LOC), 179, 189–190
Low-effort inexpensive intervention, 134–135
Lower-order goal, 132
M
MAC, see Mastery Approach to Coaching
(MAC)
Magnetic resonance imaging (MRI), 180
Major League Baseball (MLB), 43, 55
Manhattan Project, 44
Manpo-meter, 26
MAPS, see Mastery Approach to Parenting in
Sports (MAPS)
Marked-ball intervention, 76
Massive cellular excitation, 180
Mastery Approach to Coaching (MAC), 236,
261
intervention, 238, 240–243
principles, 236–237
Mastery Approach to Parenting in Sports
(MAPS), 244
Mastery climate promotion in CET and MAC
programs, 241
MBD, see Multiple baseline design (MBD)
Measor, 39
Measurement methods
duration, 62
event recording,
62
interval recording, 62
Measures of effort, 187
Medicine and Science in Sports and Exercise,
36
MEDLINE, 36
Mental imagery, strategies, 13–14
Mental practice, 13
Mental rehearsal, 13–14
Mental training, 16, 119
Methodologies, sport-specific aggression
archival, 201–202
behavioral observation, 204–205
direct observation, 202–206
self-report, 200–201
Microsoft Office Excel
R
, 56
12-Min demonstration video, 244
MIT Sloan Sports Analytics Conference,
44
MLB, see Major League Baseball (MLB)
Model attentional redirection, 130
Moneyball: The Art of Winning an Unfair
Game, 43
Mood congruence, 232
Morning training group (MTG), 38
MotionLogger
TM
actigraph, 33
Motivational Climate Scale for Youth Sports
(MCSYS), 240
MRI, see Magnetic resonance imaging (MRI)
MTG, see Morning training group (MTG)
Index 269
Multiple baseline design (MBD), 68–73
across behaviors, 68
hypothetical data, 69f–71f
research examples, 69–73
three coaching interventions across
individuals, evaluation, 72f
Multiple-schedule paradigm, 146
Multiple treatment interference, 76–77
Muscular adaptations
central nervous system (CPGs), role in, 168
exercise-limiting factors, 170
fatigue mechanisms and, 169
fiber recruitment and, 169
gender difference in, 171
SAID principle (Specific Adaptation to
Increased Demand) and, 169–170
skeletal plasticity in, 169–170
stretching excercises, benefits of, 171–172
training regimen, 170–171
N
NAN, see National Academy of
Neuropsychology (NAN)
NAT A, see National Athletic Trainers
Association (NATA)
National Academy of Neuropsychology
(NAN), 192
National Athletic Trainers Association
(NATA), 192
National Basketball Association (NBA), 44, 55
National Collegiate Athletic Association
(NCAA), 49–50, 70, 84, 162, 181,
192
National Council of Youth Sports (NCYS), 181
National Football League (NFL), 50–52, 55,
186, 192
National Health and Nutritional Examination
Survey (NHANES), 129
National Hockey League (NHL), 186, 192
National Women’s Football Association
(NWFA), 50–51
NBA, see National Basketball Association
(NBA)
NCAA, see National Collegiate Athletic
Association (NCAA)
NCYS, see National Council of Youth Sports
(NCYS)
Neurocognitive performance, 185–186
Newton’s second law of motion, 143
NFL, see National Football League (NFL)
NHANES, see National Health and Nutritional
Examination Survey (NHANES)
NHL, see National Hockey League (NHL)
NHL Players Association, 192
Non-concussed control group, 185
Non-contact sports, 143
The North American Society for the Psychology
of Sport and Physical Activity, 3
NWFA, see National Women’s Football
Association (NWFA)
O
Obsessive compulsive disorder, 119
One-day reversal effect, 67
One-on-one interaction, 186
Operant conditioning, 3, 5–6, 11, 13, 17, 135,
249
Optimal play calling, 153
Outcome expectancies
physical areas, 130
self-evaluative areas, 130
social areas, 130
Outcome goal, 121
Overmatching, 48–49
Overt behaviors, 4, 7, 200, 250
P
Parents, good behavior contracts, 206–207
Pavlovian conditioning, 5–6
Pavlovian extinction process, 5
Peak performance, 14–15
Pedometers, 26–28
general fitness using, 34–37
Percentage Baseball, 43, 57
Performance-enhancing effect, 212
Performance Failure Appraisal Inventory, 87
Performance feedback
behavioral coaching, 105–106
instructions, 106
verbal feedback, 106
performance, public posting, 106–107
components, 106
self-monitoring, 107–108
effect on participant’s behavior, 107
technology, 108–109
Performance goal, 74, 102, 104, 121–122
Performance measures, 62, 65, 67–69, 71–74,
251, 255
Personal consultant model, 115, 117
PET, see Positron emission tomography (PET)
Physical exercise, establishment and
maintenance
baseline levels and recommendations, 129
benefits, 127–128
central nervous system functioning, 128
impact, 127
–128
270 Index
Physical exercise (cont.)
interventions, 128
psychological ability, 128
subjective psychological, 128
health promotion, theoretical models,
129–134
Michie and colleagues’ taxonomy of
intervention components, 133t
proxy indicator, 127
typical intervention delivery mechanisms,
134–136
additive effect, 134
baseline patient characteristics, 134
Piezoceramic accelerometer, 30
Poisson distribution, 31
Polysomnography (PSG), 37–38
Positron emission tomography (PET), 180
Post-concussion effect, 183
Post-concussion testing, 187
Post-head injury performance, 185
Post hoc determination, 189
Post-routine time phase, 256
Posttraumatic amnesia (PTA), 179
Potential cognitive domains, 84
Potential long-term morbidity, 184
Pre-routine time phase, 256
Preseason baseline testing, 192
Prevention-based intervention
athletes, 208
by coaches, 206–207
by game officials, 207–208
by parents, 206–207
Problems in Sport Competition Scale (PSCS),
84
Problems in Sport Training Scale (PSTS), 84
Proficiency techniques
attentional focus, 166–167
component behavior as, 161–162, 166–167
deliberate practice, reinforcement of,
167–168
elite status, 165, 168
expert models, 165
instructional hierarchy as, 161
metophors usage, 165–166
rhythmic priming, 167
stimulus control as, 161–162, 164, 173
See also Muscular adaptations
Progesterone, 183
Prominent characteristics, 4–8
behavior, common synonyms, 4
external stimuli vs. internal stimuli, 4
Pavlovian conditioning of fear, 6f
stimulus discrimination training, 7f
Proprietary blend, 211–212
PSCS, see Problems in Sport Competition
Scale (PSCS)
PSG, see Polysomnography (PSG)
PSTS, see Problems in Sport Training Scale
(PSTS)
PsycINFO, 36
PTA, see Posttraumatic amnesia (PTA)
PubMed, 37, 182
Q
QOL, see Quality of life (QOL)
Quality of life (QOL), 128
Quantitative analyses
of behavior, 46–47
data, analysis, 56–57
data, sources, 55–56
data considerations, 54–55
matching law, 47–53
data on generalized equation, bar graph,
52f
plotted on coordinate plane, 48f
Reed et al.’s analysis of teams’ bias, 51f
three analyses using generalized, 48f
two- and three-point shots, 49
two generalized equation analyses,
50f–51f
Vollmer and Bourret’s concatenated
analyses, 50f
other models, 53
situational data, 55
translating to sports applications, 53–54
R
Race-car simulation, 257–258
Randomized controlled trial (RCT), 36, 128,
134
Rational emotive behavior therapy, 117
RCT, see Randomized controlled trial (RCT)
Reactive behaviors, 230
RE-AIM dimension, 138
Research-based behavioral intervention, 115
Research Quarterly for Exercise and Sport, 36
Respondent conditioning, see Conditioned
seeing
Response-acquisition program, 147
Return-to-play protocol, 189–190
Reversal design, see A-B-A-B design
Reversal-to-baseline phase, 67–68, 73, 75
Robust efficacy, 123
Rotational acceleration, 180, 185
RT3, 39
Index 271
S
The Sabermetric Manifesto, 43, 46
SARI, see Student–Athlete Relationship
Instrument (SARI)
School-based intervention, 135–136
physical activity promotion, 135
SCT and CT, principles, 135
self-management strategies
goal setting, 135
self-monitoring, 135
self-reinforcement, 135
stimulus control, 135
SPARK program, 135
SCN, see Suprachiasmatic nucleus (SCN)
ScoreKeeper
©
, 56
SCT, see Social Cognitive Theory (SCT)
SD, see Standard deviation (SD)
Second impact syndrome, 184
Self-defeating thought, 90
Self-efficacy, 80, 87, 90, 130–132, 134–135,
137, 145, 252
Self-Efficacy in Sport Scale, 87
Self-esteem in children, 240
Self-monitoring
individual program components,
importance, 133
pedometer, 134
Self-regulation, 131, 137, 253, 257
Self-report methodology, 200–201
instruments used in, 200
validity and reliability of, 200–201
Self-talk, 79–81
attributional statements, 80
comparisons with others, 80
nonverbal speech, assessment, 80
self-statements, timing, 80–81
specific content, assessment, 80
SEM, see Structural equation modeling (SEM)
Semi-structured behavioral interview format,
84
Shaping procedure, 108
SIC, see Sport Interference Checklist (SIC)
SigmaPlot
R
, 57
Single-case evaluation designs
behavior trends during baseline
measurement, 64f
decreasing trend vs. increasing trend, 63
description, 65–77
See also individual entries
principles and operations, 62–64
baseline evaluation, 63
internal and external validity, 63–64
intervention, 63
measurement, 62–63
visual inspection, 62
Single-case research designs, see Single-case
evaluation designs
Single-subject design, 3–4, 7–9, 100, 102, 213
Single Subject Research (SSR), 7, 17, 213,
249
SIQ, see Sport Imagery Questionnaire (SIQ)
SIQ-C, see Sport Imagery Questionnaire for
Children (SIQ-C)
Site of attachment, 30–31
Skepticism, 83
Skinnerian analysis, 4
SMTQ, see Sports Mental Toughness
Questionnaire (SMTQ)
Soccer-specific concussion, 182
Social Cognitive Theory (SCT), 129–130, 228,
230
core factors, 130–131
Social learning theory, 132
Social validation, 8
SPARK program, see Sports, Play, and Active
Recreation for Kids (SPARK)
program
Spearman–Brown prophecy formula, 34
Spontaneous (emitted) behaviors, 230
Sport Anxiety Scale-2 (SAS-2), 242
Sport Behavior Inventory (SBI), 200
SportDiscus, 36
Sport Imagery Questionnaire for Children
(SIQ-C), 124
Sport Imagery Questionnaire (SIQ), 124
Sport Interference Checklist (SIC), 84–85
Sport neuropsychology
areas of research and practice, 178
common neuropsychological tests, 187t
concussion pathophysiology, 178–181
educational approaches, 192–193
public policy changes, 192
self-evident problems, 193
management programs, 186–188
baseline testing, 186
concussion management, computerized
instruments, 188
trauma testing, 186–188
related concussion, 178–180
definition, 178
severity grading guidelines, 179t
resolution of symptoms: normal recovery
curve and complications, 188–191
length of recovery, 188–189
repetitive head trauma, effects, 190–191
return to play protocol, 189–190
272 Index
Sport neuropsychology (cont.)
Zurich conference consensus
recommendations, 190t
SLAM: gold standard for studying sport
concussion, 185–186
sport psychology vs. sport science
disciplines, comparison, 178t
Sport nutritional supplements, behavioral
effects of, 220–222
general considerations, 213–214
recovery drinks, 217–218
research support, 214–223
caffeine, 220–222
carbohydrate-protein drink, 217
creatine, 219–222
nonsupported supplements, 222
sports drinks, 214
testosterone levels raised, 223
testosterone prohormone supplements,
222–223
whey protein, 218–219
Sport psyching, 16
Sport-related aggression., see Aggressive
behavior
Sports, Play, and Active Recreation for Kids
(SPARK) program, 135
Sports drinks
carbohydrate–sodium drink i ngestion, 215
dehydration, 214
increased blood flow to skin, 214
Sports Medicine, 36
Sports Mental Toughness Questionnaire
(SMTQ), 87
SSR, see Single Subject Research (SSR)
Standard deviation (SD), 31–33, 36
Structural equation modeling (SEM), 131
Student–Athlete Relationship Instrument
(SARI), 16, 84, 86
t
Suprachiasmatic nucleus (SCN), 39
Symptoms Check-List-90-Revised, 87
T
TAG, see Teaching with acoustical guidance
(TAG)
Task-oriented coping, 87
Teaching with acoustical guidance (TAG), 72
Test–retest temporal stability, 32
Theory of Planned Behavior, 129
Theory of Reasoned Action, 129
Third-variable confounds, 55
Thompson Scientific, 36
Topographical-based behavior, 101
Transient neurocognitive impact, 185
Traumatic biomechanical force, 178
True variance, 251
t-tests, 31, 62
TurboStats
©
, 56
TurboStats Software Company
©
, 56
Turnover rate index, 51
Tutorial on IAPZ, 259
U
Unique discriminative stimulus, 146
The United States Consumer Product Safety
Commission (CPSC), 181
United States Olympic Committee (USOC),
117
USOC, see United States Olympic Committee
(USOC)
V
Video
feedback effect, 73
modeling effect, 73
Visualization, see Imagery
W
Web of Science, 36
9-Week program, 136
Weight-related criticism, 130
Win-at-all-cost culture, 119
Winning Profile Athlete Inventory, 87
Within-game performance, 153
Within-sport behavioral checklist, 15
Worn-out cognitive distortion, 116
Y
24 Yamax MLS-2000 digital pedometer, 33
Yard-stick, 250
Z
Zeo (sleep-monitoring system), 39
