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Emotional adjustment to sport injury

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Title:
Emotional adjustment to sport injury effects of injury severity, social support and athletic identity
Alternate title:
Effects of injury severity, social support and athletic identity
Creator:
Murray, John Francis, 1961-
Publication Date:
Language:
English
Physical Description:
v, 82 leaves : ill. ; 29 cm.

Subjects

Subjects / Keywords:
Athletic injuries ( jstor )
Cognitive psychology ( jstor )
Emotional adjustment ( jstor )
Emotional states ( jstor )
Pain ( jstor )
Physical trauma ( jstor )
Psychological stress ( jstor )
Psychology ( jstor )
Social psychology ( jstor )
Sports psychology ( jstor )
Adaptation, Psychological ( mesh )
Athletic Injuries -- psychology ( mesh )
Department of Clinical and Health Psychology thesis Ph.D ( mesh )
Dissertations, Academic -- College of Public Health and Health Professions -- Department of Clinical and Health Psychology -- UF ( mesh )
Models, Psychological ( mesh )
Self Concept ( mesh )
Social Support ( mesh )
Genre:
bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

Notes

Thesis:
Thesis (Ph. D.)--University of Florida, 1998.
Bibliography:
Includes bibliographical references (leaves 74-81).
Additional Physical Form:
Also available online.
General Note:
Typescript.
General Note:
Vita.
Statement of Responsibility:
by John Francis Murray.

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Source Institution:
University of Florida
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All applicable rights reserved by the source institution and holding location.
Resource Identifier:
0029138393 ( ALEPH )
49847293 ( OCLC )

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EMOTIONAL ADJUSTMENT TO SPORT INJURY: EFFECTS OF INJURY
SEVERITY, SOCIAL SUPPORT AND ATHLETIC IDENTITY







By

JOHN F. MURRAY


















A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 1998














ACKNOWLEDGEMENTS

I am very grateful for the support and guidance of my committee members, Drs. Eileen Fennell, James Rodrigue, Duane Dede, Sam Sears, and Milledge Murphey. I owe a special debt

of gratitude to Dr. Fennell whose wisdom, enthusiasm, and patience as my committee chair and mentor were invaluable. Credit is also due Chris Patrick and Drs. Peter Indelicato,

Robert Frank, and Richard Gutekunst f or supporting my research and helping secure my access to the football team. I also

wish to thank Mike Wasik and Tony Mennella for assisting with

data collection, and all athletes who agreed to participate in this study including members of the football team that won the national championship. Finally, I express my deepest

appreciation to my wife, Charlotte, and to all other members of my family.















TABLE OF CONTENTS

page

ACKNOWLEDGEMENTS ................................... ii

ABSTRACT ........................................... iv

INTRODUCTION ........................................ 1

Literature Review .............................. 3
Hypotheses .................................... 20

METHOD ............................................. 21

Subjects ...................................... 21
Measures ....................................... 21
Procedures .................................... 28

RESULTS ............................................ 30

Design and Analysis ........................... 30
Demographic/Subject Data ...................... 31
Dependent and Independent Variables ........... 38
Prediction of Mood Disturbance ................ 44
Secondary Analyses ............................ 53
Post-hoc Exploratory Analyses ................. 54

DISCUSSION ......................................... 58

Explanation of Findings ....................... 58
Overview and Future Research .................. 70
Clinical Implications ......................... 72

REFERENCES ......................................... 74

BIOGRAPHICAL SKETCH ................................ 82











iii














Abstract of Dissertation Presented to the Graduate
School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy EMOTIONAL ADJUSTMENT TO SPORT INJURY: EFFECTS OF INJURY
SEVERITY, SOCIAL SUPPORT AND ATHLETIC IDENTITY By

John F. Murray

August 1998

Chairperson: Eileen B. Fennell, Ph.D., ABPP Major Department: Clinical and Health Psychology

This study examined a stress and coping model suggesting

that emotional adjustment to sport injury is determined by appraisal of personal and situational factors. The contributions of injury severity, social support, and athletic identity to mood disturbance in elite and recreational athletes were examined. Another purpose was to identify differences between elite and recreational athletes including their emotional responses to sport injury in the acute phase. Thirty-one male collegiate football players on a division IA championship team and forty male recreational athletes

completed the Profile of Mood States (POMS) -Short Form within 72 hours after sustaining a sport injury. It was hypothesized that injury severity would best predict total mood disturbance in each group, with second best predictors being athletic



iv








identity among elite athletes and social support among recreational athletes.

Independent t tests showed that elite athletes reported

significantly higher athletic identity, social support, and physical conditioning than recreational athletes at the .05 level. Elite athletes also reported significantly reduced vigor, higher pain, and higher anger (approaching

significance) despite equal physician-rated and lower self reported injury severity.

Multiple regression analyses confirmed that injury

severity was the best predictor of mood disturbance in the recreational group, however, the remaining variables were in

reverse order as hypothesized (athletic identity second, social support third). 'Unexpectedly, social support best predicted total mood disturbance in the elite group with all

three variables in the model, followed by injury severity secondn) and athletic identity (third). Further

investigation should identify other factors correlated with post-injury maladjustment and longitudinally examine whether manipulating these variables (e.g., social support

enhancement) will improve emotional adjustment and hasten recovery.









V














INTRODUCTION

Sport injuries occur with tremendous frequency, often leading to severe mood disturbances and delayed recovery, although some athletes appear to adjust quite well. The

impact of this problem was recognized by the National

Institute of Arthritis, Musculoskeletal, and Skin Diseases which designated sports injuries as a "major health issue" (Booth, 1987). Unfortunately, few empirical data are available to clarify the influence of psychosocial factors in athletic injury because many of the reports are unsystematic,

anecdotal, or theoretical' in nature (Rose & Jevne, 1993). The emotional responses of athletes to injury, and factors associated with post-injury psychological adjustment, need to

be better understood for the psychology of sport injury to progress. Although more is known about psychological factors

leading to injury, recent models (Wiese-Bjornstal & Smith, 1993; Grove, 1993) have spurred investigation into the

emotional consequences of athletic injury and the role that psychological factors play in injury recovery (Gould, Udry, Bridges, & Beck, 1997).

Borrowing from Lazarus and Folkman's (1984) transactional theory of stress and coping, these models are based on the assumption that cognitive appraisal plays a central role in


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determining whether an injured athlete experiences stress leading to emotional disturbance. Factors hypothesized to influence cognitive appraisal (and emotional response)

following injury include athletic identity, social support, and injury severity.

Postinjury emotional disturbance has been associated with poor adherence to sport injury rehabilitation regimens (Daly,

Brewer, Van Raalte, Petipas, & Sklar, 1995). Understanding the emotional effects of sport injury, and factors related to these responses, is essential to reduce costly distress and rehabilitation setbacks. This knowledge would assist health care providers in identifying athletes at risk for emotional maladjustment and associated behavioral problems. Preventive

measures could then be enhanced before injury and improved treatment provided after injury. Personnel selection would likely improve by knowing which psychological qualities best predict post-injury emotional adjustment and recovery. This

line of research could also be extended into other stress reduction and personnel selection endeavors (e.g., business, performing arts).

The present study examined the emotional responses of elite and recreational athletes to sport injury using the Profile of Mood States (POMS) Short Form total score as the main outcome measure. The ability of three independent

variables (injury severity, social support and athletic

identity) to predict total mood disturbance was also examined.








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Knowing how elite and recreational athletes differentially respond to injury helps clinicians adjust their interventions accordingly..

The overall aim of this study was to gain better insight into the emotional impact of sport injury and contribute to those disciplines invested in reducing the stress of athletic injury.

Literature Review

Overview of Stress

Approaching the twenty-first century, stress is often described as a common fact of life. Factors contributing to stress include physical injury and illness, menta 1 deprivation, or any variety of factors that an individual may

perceive as "taxing. 11 Historically, stress has been viewed as both a stimulus and a response. Walter Cannon's (1932)

stimulus definition emphasized factors external to the organism (e.g., cold, lack of oxygen) which threaten

homeostasis and elicit a "fight or flight" response. However, the term "stress" was first adopted by Hans Selye (1956) whose response definition included an internal state within the organism subject to a perceived threat stressorr). Selye

(1956) viewed stress as a common denominator underlying all adaptive responses within the body, and a bodily defense to a variety of physiological and psychological insults. Both

Cannon (1932) and Selye (1956) viewed stress mainly from a physiological perspective.








4

Perhaps the most influential psychological model of

stress gained prominence in Lazarus and Folkman's "Stress, Appraisal, & Coping" (1984). This transactional theory

(highlighting the bi-directional relationship, or transaction, between person and environment) of stress and coping identifies cognitive appraisal as the key factor in

determining whether environmental stimuli are "stressful.,' From this perspective, the individual evaluates the nature of

the stressor (primary appraisal), as well as his or her personal resources (secondary appraisal), and determines

whether there are sufficient resources to meet the challenge. If resources are adequate, or the stimuli are not perceived as threatening, then the situation is not stress inducing.

Conversely, threat perceived as unmanageable leads to stress.

Although recent transactional theories like those of Lazarus and Folkman (1984) require further empirical

validation, support for the importance of cognitive appraisal has already accrued. For example, pain research indicates that one's interpretation of stimuli is critical and that belief in control over pain is correlated with reduced pain sensations (Jensen, Turner, Romano, & Karoly, 1991). This research also suggests that catastrophizers fare consistently worse than minimizers in terms of overall functioning. Whereas catastrophizers tend to engage in negativistic

thinking and worry in response to pain, minimizers are capable of sustaining morale by engaging in more constructive positive








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thinking. Other examples include findings from the field of psychoneuroimmunology which suggest that cognitive appraisal may influence immune system functioning (Ader & Cohen, 1993).

It should be mentioned that contrary positive views of stress also exist. For example, Dienstbier (1989) proclaimed that some level of intermittent stress is actually health promoting in producing increased physiological toughness. Toughness was operationalized as lower base arousal rates, greater sympathetic nervous system sensitivity, reduced depletion of catecholamine reserves (needed for high level energy to meet the demands of potentially stressful encounters), and lower cortisol secretions. Similarly, Aldwin (1995) asserted that learning to deal with adversity fosters human development. Nevertheless, prolonged or extreme stress is viewed as harmful even by Dienstbier (1989).

Stress has been linked to health problems and adverse health behaviors including increased cardiovascular response, vasoconstriction, platelet aggregation and plaque rupture (Kamark & Jennings 1991), infectious diseases (Kiecolt-Glaser, Garner, Speicher, Holliday & Glaser, 1984), autoimmune disease (Cohen & Herbert, 1996), smoking and alcohol abuse (Ader & Matthews, 1994), and a wide range of other physical and psychological problems (Matarazzo, 1983; Cooper, 1996). As an identified major health risk factor, it seems worthwhile to examine for factors which protect against stress or enhance the adequacy of coping.








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The stress of athletic injury

Sports injuries are a tremendous source of stress and serious health risk. Heil (1993) portrayed injuries as the

ultimate stressor in the life of the athlete and Burwitz, Moore, & Wilkinson (1994) described them as the most important issue in sports. Alarming data come from Kraus and Conroy

(1984) who reported 6,045 athletically/recreationally related deaths in 1978. In addition, there are an estimated seventeen million sport injuries per year in the United States alone,

including one million in high school football involving ten fatalities (Mueller & Blythe, 1987). In another report, Smith & Milliner (1991) stated that athletic injury combined with serious pre-injury stress prompted at least five suicide attempts at one university.

The psychology of sport injury rehabilitation is an

emerging area of inquiry in both sports medicine and sport psychology (Laubach, Brewer, Van Raalte, & Petipas, 1996). As in many areas of health care, the most complete picture of sport injuries will emerge when researchers adopt a

multidisciplinary approach that incorporates physiological, environmental and psychological factors (Udry, 1996). Larson, Starkey, & zaichkowsky (1996) claimed that stress, anger and

anxiety are the most frequently encountered psychological conditions associated with injury, but techniques to reduce these conditions are not frequently used.








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According to Heil (1993), "the groundwork for a

psychology of sport injury has been laid by a series of developments-in sport including a growing appreciation of the

scope, severity and underlying causes of injury." (p.1). Negative emotional reactions to athletic injury cited in the

literature include anger, depression, tension, frustration, reduced vigor, and emotional distress severe enough to warrant clinical intervention (Smith, Scott, O'Fallon & Young, 1990; Smith, Scott & Wiese, 1990; Leddy, Lambert, & Ogles, 1994).

There is a major gap, however, between the perceived need for psychological services and the actual care offered. For

example, forty-seven percent of a large sample of certified athletic trainers recently reported that every injured athlete suffers psychological trauma, but only twenty-five percent have a sport psychologist as a member of the sports medicine team (Larsen, Starkey, & Zaichkowsky, 1996). The athletic trainer for the Miami Dolphins recently stated that great individual variability exists in emotional responses to injury (Vermillion, personal communication, September 27, 1995).

Empirical research is needed to explain this variability. Healthcare professionals are also concerned about the

psychosocial impact of athletic injuries because the postinjury emotional response may influence medical decisions such as the optimal timing for surgical intervention and the athlete's post-injury rehabilitation (Smith, 1996).








8
It is also important to recognize that many injured athletes appear to adjust quite well (Brewer, Linder, &

Phelps, 1995), Although emotional response to athletic injury was traditionally conceptualized in terms of stage models based on models of grief and loss (Rotella & Heyman, 1986), there has been little empirical support for these models in sports (Evans & Hardy, 1995).

Factors deemed important in the cognitive, emotional and behavioral responses of athletes to injury were recently elucidated in models by Wiese-Bjornstal and Smith (1993) and Grove (1993) (See Figures 1 & 2). Influenced by Lazarus and

Folkman's (1984) transactional theory of stress and coping, and the injury prediction model of Andersen and Williams (1988), these models are based on the assumption that postinjury adjustment is determined by the athlete's cognitive appraisal of the injury and its effects.

Personal and situational factors are believed to

influence cognitive appraisal and moderate the relationship between injury and post-injury adjustment (Brewer, Linder, &

Phelps, 1995). Personal factors are stable dispositional characteristics of an individual (e.g., locus of control, self-concept), whereas situational factors are unstable,

variable aspects of the social and physical environment (e.g., injury severity, social support) (Brewer, Linder, & Phelps, 1995). It seems worthwhile to conduct research examining which factors most influence the degree to which athletic












9



injuries are appraised as stressful (leading to poorer


emotional adjustment).


Personal factors and athletic injury


Knowledge of an athlete's personal characteristics should


help sports medicine practitioners to anticipate, understand,


and deal with undesirable rehabilitation responses (Grove,


1993). Brewer (1994) reviewed several personal factors


hypothesized to affect cognitive appraisals of (and presumably


emotional responses to) athletic injury including trait


anxiety, self-esteem, self-motivation, coping skills,


extraversion, neuroticism, psychological investment in sport,


and injury history. Other potentially important personal





Response to injury Physical and Moderators Mediators and rehabilitaion psychological
process recovery process


Personal Cognitive ao~praisal
Injury characteristics and response everty* Goal adjustment Personality History Rate of perceived
Type reovey
*Self-effcacy
Belief and atrtnutions
S Individual differences Recognition of inlury Coping s Self-esteerrVidentity L
resources Sel-motivation
S Motivational orientation
Stre Pain tolerance Emotional response
response Fear of unknown
(assessment A eic Situatonal Tension. anger. Recovery
of demands depression Rcvr
o emand Sport-specific aeustration oreco, outcomes
resources situational factors Postive antuoe
r Level ano intensity and Outlook
Role on team
rime in season
History o Inury context
stressors Team relationships Benav,oural response

Interactions with Adherence to
sports medicine team rehab station Intervention -*eved suppon Use of PST strategies
Inor mat1o guerh Use of Sociat SuppOrt S Feedback on oals Risk-takinc Dehavours Feedback on goals Effort and intensity





Figure 1. Predictors of cognitive, emotional
and behavioral responses of athletes to injury

and rehabilitation (Wiese-Bjornstal & Smith,

1995).









10
















History of Personality Coping
Stressors [oses
~~REHABILITATION Sport Recovery &
Injury Cognitions Behaviors Return to
t t $ Competition
Physiological Reactions

Injury Treatment
Related Psychological Related
Factors Interventions Factors








Figure 2. Model of rehabilitation from sport
injury (Grove, 1993).











factors include athletic identity (Brewer, 1993), locus of control (Rotter, Chance, & Phares, 1972), commitment (Kobasa,










Maddi & Kahn, 1982), explanatory style, dispositional optimism, and hardiness (Grove, 1993), trait anxiety

(Spielberger,. 1972), sense of coherence (Antonovsky, 1985), and self-esteem (Rosenberg, 1979).

Empirical evidence for the role of personal factors in

emotional adjustment to athletic injury is sparse, as few studies have directly addressed this relationship. However,

Brewer (1994) described five personal variables: psychological investment in sport, physical self-esteem, age, pessimistic explanatory style, and hardiness-which have been correlated with emotional reactions to injury. Brewer (1993) also

presented a series of studies in which a strong and exclusive

identification with the'athlete role was associated with depressed mood following sport injuries. Thus, individuals

whose self-worth was derived exclusively or predominantly through athletic performance were more likely to appraise their injury in terms of threat or loss (Brewer, Van Raalte, & Linder, 1993).

Situational factors and athletic injury

A number of situational factors have also been found to

be correlated with post-injury emotional adjustment. Positive correlations include: medical prognosis (Albert, 1988),

recovery progress (McDonald & Hardy, 1990), social support for rehabilitation and impairment of sports performance (Brewer,

Van Raalte, & Linder (1991), and physician rated current injury status. Situational factors that have been found to be








12

inversely related to post-injury emotional adjustment are injury severity (Smith, Scott, O'Fallon, & Young, 1990),

duration of injury (McDonald & Hardy, 1990), impairment of daily activities (Crossman & Jamieson, 1985), and life stress (Brewer, 1993).

Several other situational variables hypothesized to influence post-injury emotional adjustment, but currently without empirical support, include personal control over recovery, time of athletic season, point in athletic career,

type of sport, social pressures, injury onset, injury course, rehabilitation self-efficacy, and pain (Brewer, 1994). overview of Copina

Current views of coping were influenced by animal

experimentation models which emphasized avoidance behavior to control aversive conditions and lower arousal (Miller, 1980), and psychoanalytic models which depicted coping as realistic

and flexible thoughts and acts that solve problems (Menninger, 1963). Both of these approaches have been criticized as overly simplistic for understanding the complexity of human coping (Lazarus & Folkman, 1984, p.139).

Lazarus and Folkman (1984) define coping as "the

constantly changing cognitive and behavioral efforts to manage external and/or internal demands appraised as taxing or exceeding the resources of the person (p.141)." They assert

that differences in cognitive appraisal explain much of the variability in how individuals respond to potentially








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stressful situations. Lazarus and Foikman have also

contributed to the notion that dispositional measures of

coping do not adequately characterize the range of coping strategies used in dealing with complex situations (Coyne & Downey, 1991). Recently, a consensus has developed on the

basic dimensions of coping and how to assess them. Coping has been conceptualized in terms of approach vs avoidance (Suls & Fletcher, 1985) and in terms of appraisal, problem focus, and emotion focus (Billings & Moos, 1984). Lazarus and Folkman's

distinction between problem-f ocused and emotion-f ocused coping has been by far the most influential conceptual ization, and various versions of their ways of Coping Checklist (WOCC) have been utilized in literally hundreds of studies (Coyne & Downey, 1991).

coping with general medical problems

Effective coping may play an important role in health promotion and disease prevention (Rodin & Salovey, 1989). Coping can influence hormone levels, cause direct tissue changes, or affect the immune system (Jacobs, Mason, Kosten, Kasl, & Ostfeld, 1985). Interpersonal coping styles may also

influence the type of care received (e.g., demanding, taskoriented patients may have their complaints acted upon more quickly). Effective coping has been linked to quicker recovery from illness, and active participant coping strategies may be especially effective in this regard (Cohen & Lazarus, 1979).








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Most studies focus on coping with severe medical

conditions. For example, Thompson, Gil, Abrams, and Phillips (1992) showed that good psychological adjustment was

associated with less use of palliative coping methods among adults with sickle cell disease. Palliative coping is a

defensive form of coping in which the individual attempts to regulate negative emotional states. This is contrasted with

instrumental coping, in which the individual actively attempts to alter the situation through efforts directed at the environment or self. Folkman, Chesney, Pollack, and Coates (1993) found that stress appraised as controllable was associated with involvement coping, which in turn was associated with diminished depressive mood. Involvement

coping was characterized by planful problem solving, social support seeking, and positive reappraisal. Finally,

Somerfield and Curbow (1992) demonstrated that coping is important in maintaining psychological well-being among individuals with cancer.

Bernard and Krupat (1994) outline two major approaches to help patients with a variety of medical difficulties to cope more effectively. Arousal management strategies attempt to alter physiological response through use of medications,

relaxation exercises and exercise. Transaction management attempts to affect the cognitive appraisal of stress, through problem focused or avoidant coping techniques.








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Coping with sport injuries

Research is only beginning to examine how athletes cope with injury, whereas studies examining coping resources as a predictor of injury are far more common. For example,

Williams, Tonymon and Wadsworth (1986) showed that a low level of coping resources was the strongest predictor of injury among volleyball players, and Smith, Smoll, & Ptacek (1990) found that athletes low in both social support and coping skills exhibited the greatest injury risk.

Some of the challenges facing athletes in coping with injury include dealing with emotions, reorganizing their lifestyles, maintaining hope, coping with self-concept

changes, and coping with changes in relationships with friends and family (Rose & Jevne, 1993). Crossman (1997) pointed out that mood disturbance for an athlete is usually a departure from the norm since most athletes are mentally healthy individuals. Morgan's "iceberg profile" (Morgan, Costill, Flynn, Raglin, & O'Connor, 1988) represents a popular graphic

depicting the mental health of elite athletes, whose scores on the Prof ile of Mood States (McNair, Lorr & Dropplemann, 1992) often produce a distinguished iceberg shape formed by a

singular elevation on the vigor scale, and low scores on negative scales including anxiety and depression. Crossman (1997) pointed out that since athletes are usually healthy,

they may be less prepared psychologically for the stress associated with an unforseen or unexpected injury.








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Many anecdotal reports exist to identify strategies athletes use to best cope with injury. Larson, Starkey, and Zaichkowsky (1996) noted that athletic trainers ranked a

series of behaviors, characteristics and attitudes of athletes who cope successfully with injury. The three most successful

were rehabilitation compliance, positive attitude about injury and life, and motivation to work hard during rehabilitation.

Williams and Roepke (1993) outlined several coping factors including trying to find the meaning of the injury,

facilitating grief, forming goals, eliminating self-defeating thoughts, managing pain, managing stress, and continuing to improve performance skill. Finally, Crossman (1997)

identified goal setting' and visualization as potentially valuable coping tools.

Recently, it was found that Stress Inoculation Training

(SIT) (Meichenbaum, 1985) reduced pain and anxiety, and led to more rapid return to physical functioning among knee injured athletes (Ross & Berger, 1996). The Stress Inoculation

Training program involves three components: conceptual i nation, skills acquisition, and application. Participants are

educated on the theories of emotion and pain, trained in selfmonitoring and coping methods (e.g., relaxation strategies, self -reinforcement), and instructed to engage in these coping strategies in response to discomfort or pain cues. This type of study is important in demonstrating treatment efficacy and establishing clinical sport psychology as a valuable segment








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of the sports medicine team. However, more fundamental

research is also needed to identify those factors most

important in adjustment to athletic injury. As these elements are identified, the quality of psychological services should improve accordingly.

How Sport Injuries are Unique

In studying athletic behavior, sport psychologists have

long borrowed from principles in psychology, but the sport psychological literature has proceeded at a slow pace and it has been difficult to find consistent results (Ogilvie, 1987; Nideffer, 1989). This may be in part due to inherent differences between athletes and the more traditional populations examined in psychological research. Heil (1993)

maintains that injured athletes are different from general medical patients in a number of ways. He states that athletes are more goal oriented, have a greater proclivity for physical training, better pain tolerance, more experience, and a

greater motivation to recover. Similarly, Pargman (1993) noted that "the problem of malingering is probably not overwhelming in sport compared with the more general medical population (p.69).11

In sum, athletic injuries represent a unique challenge, best resolved through empirical research conducted with appropriate sport populations. Studies examining the

influence of personal and situational factors on post-injury








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emotional adjustment should contribute significantly to the psychology of sport injury.

Summary Statement of Problem

-The present study was designed to examine the

relationship between three factors previously identified in the literature as associated with adjustment to sport injury, and total mood disturbance following sport injury. These

factors were injury severity, athletic identity and social support. A simple model, extended from Brewer (1994),

depicting these factors and total mood disturbance was examined (see Figure 3).

For purposes of this study, injury severity was

operationalized as estimatedd number of days until expected return to athletic participation for the injury sustained,", as rated by the athletic trainer (elite group) or physician (recreational group). Athletic identity was operationalized as the score received on the the Athletic Identity

Measurement Scale (AIMS) (Brewer, Van Raalte, & Linder, 1993). Social support was operationalized as the combined scale scores of the measures used by Smith, Sinoll, & Ptacek (1990) to assess amount and quality of social support. Finally,

total mood disturbance was operationalized as the total score from the Profile of Mood States-Short Form (McNair, Lorr, & Droppleman, 1992).










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ADJUSTMENT TO ATHLETIC INJURY



PERSONAL FACTORS SITUATIONAL FACTORS

ATHLETIC IDENTITY INJURY SEVERITY
SOCIAL SUPPORT




COGNITIVE APPRAISAL







EMOTIONAL RESPONSE

PROFILE OF MOOD STATES





BEHAVIORAL RESPONSE






Figure 3. cognitive appraisal model of psychological adjustment to athletic injury including dependent variable (Profile of Mood States) and independent variables (Athletic Identity, Injury Severity and Social support) (extended from Brewer, 1994).








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Hypotheses

The following hypotheses were developed to empirically test for differences between injured elite and recreational athletes, and their emotional response to athletic injury. Hypothesis 1

It was hypothesized that recreational and elite athletes would not differ significantly on any of the demographic/subject variables represented on a continuous scale including age, year in college, self-ratings of prior

physical conditioning, dispositional optimism, current pain level, injury severity, physical well being (vitality), and emotional well being (mental health). Hypothesis 2

It was hypothesized that athletic identity and social support would be significantly higher among elite athletes than recreational athletes.

Hypothesis 3

Among elite athletes, it was hypothesized that injury severity would best predict total mood disturbance followed by athletic identity (second) and social support (third). Hypothesis 4

Among recreational athletes, it was hypothesized that injury severity would best predict total mood disturbance

followed by social support (second) and athletic identity (third).














METHOD

Subjects

Seventy-one male college students (31 male varsity football players from an NCAA division IA championship football program and 40 male recreational athletes) were recruited. Inclusion criteria were: (1) between 18 and 28 years of age; (2) physical injury from sport participation within the previous 72 hours; (3) at least one day of missed sport participation due to injury; (4) no history of playing

a sport professionally; and (5) not under the influence of any substance which might impair cognitive functioning at the time of testing. Near unanimous participation was obtained. one recreational athlete asked to be excluded from the study for

undisclosed reasons after completing measures, and one varsity football player refused entry over concern that his injury status might be discovered by NFL personnel prior to the draft.

Measures

Demographic Questionnaire

All subjects completed a demographic/subject

questionnaire reporting their age, ethnic identity, year in college, major area of study, classification as either varsity or recreational athlete and sport in which they were injured.


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Varsity participants were further classified with regard to current position on the team (e.g., quarterback, linebacker) and level of participation (starter or nonstarter). Medical O-uestionnaire

A medical questionnaire was completed by each subject indicating current and past medical conditions, medications,

estimated physical condition immediately prior to injury, and current pain level. Other questions assessed past injuries, date and time of current injury, method of becoming injured,

and self -reported perception of injury severity (days expected until further athletic participation). Assigned medical

personnel provided injury data including diagnosis, severity

rating (mild, moderate, or severe determined by estimated days until further athletic participation), and whether the injury was considered season ending or career ending. In jury

severity ratings were based on time loss and followed the National Athletic Injury/Illness Reporting System (NAIRS; Coddington & Troxell, 1980). Quantitative self-reports of pre-injury physical condition and current pain level were accomplished using visual analog scales (VAS; Keefe, Brown, Scott, & Ziesat, 1982). For pain assessment, this consisted of a 10-point line, with 0 indicating "no pain" and

10 indicating "pain as bad as it could be." Respondents were instructed to place a mark on the line indicating their

current level of pain intensity and scoring involved measuring the distance from the "no pain" (0) end to the respondent's








23
mark. Pre-in jury physical condition was measured in a similar manner, and scored in the reverse direction with 0 indicating "my worst physical condition ever" and 10 indicating "my best physical condition ever."

SF-.36 Health Survey (Physical/Emotional Well Being)

To obtain an index of physical and emotional well being

and add control to the design, two sections from the SF-36 Health Survey (Ware & Donald, 1992) assessing vitality

(physical well being) and mental health (emotional well being) were utilized. Both the vitality and mental health scales are bipolar in nature and tap well being, measuring a wide range of negative and positive health states.

The SF-36 satisfies~rigorous psychometric criteria for construct validity and internal consistency and most reliability estimates exceed the .80 level (Sullivan,

Karlsson, & Ware, 1995). The vitality scale and mental health scale display good psychometric qualities including itemdiscriminant validity and scale reliability. The vitality

scale is intended to capture differences in subjective physical well being (e.g., energy level and fatigue) whereas the mental health scale includes one or more items from each of four major mental health dimensions (anxiety, depression, loss of behavioral/emotional control, and psychological well

being). The vitality scale is derived from four items (raw score range of 4-24) and the mental health scale is derived from 5 items (raw score range of 5-30) from the SF-36. Raw








24

scores are then converted into as a standard score with a range of possible scores on each scale of 0-100. This

standard score was used in the analysis.

Subjects completed each section twice, once for current well being and again as a retrospective assessment of their physical and emotional states immediately following injury. Profile of Mood States-Short Form (POMS)

To measure emotional adjustment following injury, the total adjustment score from the Profile of Mood States-Short Form (McNair, Lorr, & Droppleman, 1992) was utilized. This measure assesses transient mood states across six dimensions: tension-anxiety, depression-dejection, anger-hostility, vigoractivity, fatigue-inertia, and confusion-bewilderment, and provides a global score of overall mood disturbance. The

advantages of the POMS include its speed and ease of administration (Eichman, 1978).

The POMS has been cited in at least 56 published papers since its widespread use in sports research began in 1975 (McNair, Lorr, & Dropplemann, 1992), and participants in 19 different sports have served as subjects in studies using the POMS (LeUnes, Hayward, & Daiss, 1988). The POMS has excellent psychometric properties (Pargman, 1993) and has been validated on populations across a variety of domains. Internal

consistency of the POMS factors is highly satisfactory, with reliability coefficients ranging from .87 to .95 (McNair, Lorr, & Dropplemann, 1992). Test-retest reliability estimates








25

ranged from .65 to .74 on a university medical center psychiatry clinic. The POMS has also shown good concurrent validity by its correlations with similar types of instruments.

The POMS short form (McNair, Lorr, & Dropplemann, 1992) was developed based on a need for a form that is even easier to complete and consists of 30 items and the same six scales as measured by the long form. The POMS-Short Form correlates very highly with POMS long version, with factor correlations among a college sample ranging from .67 to .88. The total

adjustment score used in the analysis was obtained by adding scores from each of the 6 subscales with the vigor dimension

weighted negatively. The'range of possible scores is thus -20 to 100.

Athletic Identity Measurement Scale (AIMal

The Athletic Identity Measurement Scale (AIMS) (Brewer, Van Raalte, & Linder, 1993) measures the degree to which an individual identifies with the athletic role and the exclusivity of that identification. The AIMS consists of 10 statements in which the athlete is asked to indicate his/her

agreement with the athletic role on a 7 point likert scale anchored by "strongly agree" and "strongly disagree." The

range of possible scores on the AIMS total scores used in the analysis ranged from 10-70.

Initial studies by the authors show the AIMS to possess excellent psychometric qualities (Brewer, Van Raalte, &








26

Linder, 1993). Test-retest reliability of the AIMS was .89, indicating that the AIMS is stable over a 14 day period. A

coefficient alpha of .93 provided support for the internal consistency of the AIMS. Construct validity was also

demonstrated as the AIMS correlated significantly (.83) with

a scale measuring perceived importance of sport competence, the PIP Importance of Sports Competence Scale. Finally, none

of the AIMS item scores were significantly correlated with scores on the short form of the Marlowe-Crowne Social Desirability Scale (Crowne & Marlowe, 1960), suggesting that scores do not reflect a social desirability response bias. Social Support

The measure of social support used in this project was

created by Smith, Smoll, and Ptacek (1990). This measures the amount and quality of social support available to subjects from 20 different individuals (e.g., mother, father, coach,

and best friend) and groups (e.g., teammates and clubs or religious groups to which they belong). This measure was derived from one used by Cauce, Felner, and Primavera (1982)

to assess adolescents' subjective appraisals of the individual and group components of their social support network. Since

athletes were assessed in this study, the additions to the scale by Smith, Smoll, and Ptacek (1990) were used. These included added items for assistant coach, trainer, team physician, and athletic religious organizations to assess the athletic environment in greater detail.








27

On separate scales, athletes were asked to indicate the extent to which each individual and group could be counted on

to provide them with (a) emotional support and caring, and (b) help and guidance, on a scale ranging from "not at all helpful" (1) to "very helpful" (5). Scores were summed to provide an overall measure of the two varieties of social support. The range of possible scores on the two scales is thus 20 to 100. For the present study, a grand total social

support index was obtained by summing the two varieties of social support.

On a separate sample of 94 athletes, the social support scale had a one-week test-retest reliability of .87 for emotional support and .88 for help and guidance, and the two social support measures correlated .88 with one another (Smith, Small, & Ptacek, 1990). Life Orientation Test-Revised (LOT-R'

The Life Orientation Test-Revised (LOT-R) (Schier & Carver, 1994) is a measure of dispositonal optimism that was used to add further control to the design while examining the relationship between outcome expectancies and emotional adjustment in the two groups. The LOT-R was derived from a theory that optimists (those with expectations of successful outcomes) are more persistent and more effective than pessimistic individuals in their goal directed behaviors in a

variety of domains (Schier & Carver, 1985). Dispositional optimism has been proposed to affect many areas of functioning








28

including health, performance, self-reported physical

symptoms, and ability to complete a rehabilitation program (Schier & carver, 1985).

The LOT-R has excellent psychometric properties. It was normed on a sample of 2,055 undergraduates and showed high internal consistency and test-retest reliability, and excellent convergent and discriminant validity. Cronbach's alpha for the entire scale was .76 and the test-retest correlation was .79 (Schier & Carver, 1985).

Subjects were instructed to indicate the extent of their agreement with each of ten items of the LOT-R anchored by 0=strongly disagree and 4=strongly agree. Six of the 10 items are used to derive the LOT-R total score used in the study while the remaining 4 items are distractors. The range of

possible-scores is thus 0-24.

Procedures

Testing was conducted in a consistent manner for all subjects to reduce potential sources of experimental bias. All data were handled as confidential medical information, and stored in a locked file cabinet in the Department of Clinical and Health Psychology at the University of Florida.

Injured football players reporting to the University Athletic Association (UAA) training room, and injured students reporting to the University Health Care Center, were approached by the principal investigator while awaiting

treatment and asked to provide an additional 20 minutes to








29
take part in the study. After informed consent was obtained subjects were asked to complete the measures. Subjects first

completed the, initial section of the demographic questionnaire to determine whether they met criteria for further inclusion into the study. Two recreational athletes were excused from

the study and thanked for their participation because they were older than 28 years of age. All other subjects met requirements for continued entry and completed the demographic instrument followed by the medical questionnaire, POMS, AIMS, and Social Support measure. All questions were written at a 6th grade reading level, but subjects were offered and provided verbal clarification on items as requested.

Following data collection, subjects were thanked for their participation. All additional data (e.g., diagnosis, severity classification) were collected from the health center

physician and certified athletic trainer assigned to football.














RESULTS

Design and Analysis

Group differences on demographic/subject measures represented on a continuous scale were assessed using independent t tests with alpha set at the .05 level.

Similarly, group differences on the main dependent measure (POMS total score) and two of the major independent variables

(athletic identity and medical rating of injury severity) were assessed using independent t tests with alpha set at the .05 level. Differences between groups on the social support measure were reported descriptively rather than by using a significance test. This was because the social support scale

included questions about individuals much less available to recreational athletes than elite athletes (e.g. team manager, team trainer, head coach, assistant coach), explaining large group differences on the measure. Means and standard

deviations were computed for all variables.

Prediction of mood disturbance within each group by the

three main independent variables was accomplished using two separate stepwise linear regression analyses. An exploratory analysis was conducted to examine the relationship between measures first used in this study and total mood disturbance. Correlation coefficients were calculated between total mood


30








31

disturbance and self-ratings of prior physical conditioning,

current pain, injury severity, vitality, mental health and optimism.

A post-hoc exploratory analysis was conducted to examine for differences between athletes at different levels of mood. All subjects were combined to ensure a sufficient sample size (n=71) and then grouped into low, medium and high categories on the dependent measure (POMS total score) and independent measures (athletic identity, injury severity, and social support). Three separate chi square tests of independence were used to examine whether classification on mood level was

independent of classification on the independent measures.

Finally, a second 'post-hoc exploratory analysis was conducted to determine the order and magnitude of prediction

of the main independent variables on total mood disturbance among subjects classified only low or high on total mood disturbance (n=47). A stepwise linear regression analysis was used for this purpose.

Demographic/Subiect Data

All subjects reported their age, year in college, ethnic identity, major area of study, sport, position on team, date, time, and method of injury, past injuries, current

medications, days expected to miss participation, current pain level, estimated physical condition immediately before injury, optimism from the LOT-R, and vitality and mental health from the SF-36. Elite subjects further reported their starter vs.









32
nonstarter and redshirt vs. non-redshirt status. Independent

t tests on demographic/subject variables represented on a continuous scale were conducted with alpha set at .05.





Table 1

Demographic/Subject Variables Showing Nonsignificant Differences by Group Classification



Elite Recreational



Variable 1 (SD) M (SD) t(1,69)



Age 20.71 (1.19) 21.15 (2.57) -.88

Year 3.10 (.94) 2.98 (1.33) .43

LOT-R 17.26 (3.88) 16.20 (3.64) 1.18

V-1 47.74 (16.92) 50.00 (20.10) -.50

V-2 53.87 (18.96) 52.05 (19.22) .40

141--1 67.03 (17.12) 62.30 (21.26) 1.01

MH-2 72.52 (16.99) 71.69 (16.98) .20



Note. Year depicts year in college, LOT-R depicts the Life orientation Test (Revised), and V and NH depict the Vitality and Mental Health scales from the SF-36 Health Survey (1 =at time of injury, 2= currently). For all t
values, p ns.








33

Table 2

Demographic/sub-iect Variables Showing significant Dif ferences by Group Classification



Elite Recreational



Variable M (SD) M (ED) t(1,69)



Pain 5.71 (2.44) 3.48 (2.42) 3.84 p<.000

PhyCon 8.54 (1.41) 7.30 (2.39) 2.56 P<.001 InjSev 9.40 (10.40) 17.25 (18.24) -2.14 p<.036



Note. Pain and PhyCon (Physical Conditioning prior to injury) are based on a 1-10 scale with higher numbers
indicating greater amounts. InjSev (self-reported injury
severity) is based on estimated days until resumed activity.





Elite and recreational athletes reported similar age, year in college, dispositional optimism and well being ratings both immediately following injury and at time of testing. Between-group differences on pain, physical conditioning and

self-reported injury severity were significant with elite athletes reporting higher pain and physical conditioning, but lower injury severity. Recreational athletes had more variable physical conditioning and pain ratings than elites.

Table 1 reports the means, standard deviations, and associated t statistics for demographic/subject variables in which no








34
significant differences were found. Table 2 reports the

means, standard deviations, and associated t statistics for demographic/subject variables in which significant differences were found. No other significant differences were noted on continuous demographic/subject measures.





Table 3

Ethnic Identity: Number and Percent of Group



Elite Recreational Total Ethnic ID n % n % n %



Caucasian 12 (39%) 33 (83%) 45 (63%) Afr. Amer. 19 (61%) 3 (7.5%) 22 (31%)

Hispanic 0 (0%) 3 (7.5%) 3 (4%)

Asian 0 (0%) 1 (2%) 1 (1%)







Frequency distributions are reported for noncontinuous

demographic/subject variables including ethnic identity in Table 3, injury type in Table 4, team position for elite football players in Table 5, injury types and location in Table 6, and sport for recreational athletes in Table 7.









35
Table 4

Injury Type: Number and Percent of Group



Elite Recreational Total Injury Type Pa % n % n %



Sprains 20 (65%) 24 (60%) 44 (62%)

Strains 6 (19%) 3 (8%) 9 (13%)

Contusions 3 (10%) 6 (15%) 9 (13%)

Fractures 1 (3%) 7 (17%) 8 (11%)

Other 1 (3%) 0 (0%) 1 (1%)







Table 5

Football Positions: Number and Percent-of Group of Elite Athletes



Position on Team n



Offense 14 (45%)

Linemen 7 (23%)

Running Backs 4 (13%)

Receivers 2 (6%)

Kickers 1 (3%)








36
Table 5--continued



Position on Tea m nf%



Defense 17 (55%)

Linemen 10 (32%)

Secondary 6 (20%)

Linebackers 1 (3%)







Table 6

Injury Types & Location: Number and Percent of Group



Elite Recreational Total

Type and Location n % n~ % n %



sprains 20 (65%) 24 (60%) 42 (62%)

Ankle 8 (26%) 14 (35%) 20 (28%)

Knee 7 (23%) 5 (13%) 12 (17%)

Finger 0 (0%) 4 (10%) 4 (6%)

Shoulder 2 (7%) 0 (0%) 2 (3%)

Back 2 (7%) 0 (0%) 2 (3%)

Leg 1 (3%) 0 (0%) 1 (3%)

Wrist 0 (0%) 1 (3%) 1 (3%)








37

Table 6--continued



Elite Recreational Total Type and Location n % n % n %



Strains 6 (19%) 3 (8%) 9 (13%)

Knee 1 (3%) 2 (5%) 3 (4%)

Back 1 (3%) 1 (2.5%) 2 (3%)

Hamstring 2 (7%) 0 (0%) 2 (3%)

Abdomen 1 (3%) 0 (0%) 1 (1%)

Shoulder 1 (3%) 0 (0%) 1 (1%)

Contusions 3 (10%) 6 (15%) 9 (13%)

Knee 1 (3%) 1 (2.5%) 2 (3%)

Shoulder 2 (7%) 0 (0%) 2 (3%)

Thigh 0 (0%) 2 (5%) 2 (3%)

Finger 0 (0%) 1 (2.5%) 1 (1%)

Hand 0 (0%) 1 (2.5%) 1 (1%)

Arm 0 (0%) 1 (2.5%) 1 (1%)

Fractures 1 (3%) 7 (17%) 8 (11%)

Finger 0 (0%) 4 (10%) 4 (6%)

Leg 1 (3%) 1 (2.5%) 2 (3%)

Toe 0 (0%) 1 (2.5%) 1 (2.5%)

Hand 0 (0%) 1 (2.5%) 1 (2.5%)

Other 1 (3%) 0 (0%) 1 (1%)

Plexitis 1 (3%) 0 (0%) 1 (1%)








38
Table 7

Recreational Siports: Number and Perc-ent of Group



Sports



Basketball 16 (40%)

Flag Football 9 (23%)

Roller Hockey 3 (7.5%)

Soccer 3 (7.5%)

Racquetball 2 (5%)

Running 2 (5%)

Softball 2 (5%)

Wrestling 1 (2.5%)

Volleyball 1 (2.5%)

Ultimate Frisbee 1 (2.5%)







Dependent and Independent Variables

Means, standard deviations, and associated t statistics

for the dependent and independent variables appear in Table 8. Total Mood Disturbance

Between-group differences on the main dependent measure (total mood disturbance from the POMS Short Form) were not significant [t(1,69)=1.20, p=.24]. On the whole, elite and

recreational athletes appear to have experienced similar total








39

levels of mood disturbance following sport injury. In addition, these scores were not in the clinically interpretable range. Compared to a noninjured male college





Table 8

Dependent (DV) and Major Independent Variables (IVs)



Elite Recreational



Variable M (SD) M MQ) 1 (1,69) 1p



POMS 21.16 (19.01) 16.15 (16.22) 1.20 .24

Injury 1.84 (.86) 2.18 (.68) -1.85 .07

AIMS 44.19 (7.44) 38.76 (8.67) 2.78 .007

Social 127.87 (31.50) 66.83 (17.92) Note. POMS depicts total mood disturbance from the Profile of Mood States Short Form, Injury depicts medical rating of injury severity from 1 (mild) to
3 (severe), AIMS depicts the Athletic Identity Measurement Scale, and Social depicts social support.
*p<.01




sample (McNair, Lorr & Droppleman, 1992, p.23), injured elite athletes scored .33 standard deviations above the mean while injured recreational athletes scored .03 standard deviations below the mean. Furthermore, only 4% of all subjects met clinical criteria for total mood disturbance (2 SD above the








40

mean). Thus, global mood disturbance does not appear to have resulted in this study, at least in comparison to the moods of noninjured college students.

For illustrative purposes, a comparison was made between elite and recreational athletes' POMS subscale scores and the previously mentioned noninjured college sample provided by McNair, Lorr & Dropplemann (1992). Subscale scores are





Table 9

Profile of Mood States Factor Scores of Injured Athletes Compared to a Non-injured College Sample



Elite Recreational

POMS Factor SD from Norm SD from Norm



Tension-Anxiety -.46 -.44

Depression-Dejection .18 .01

Anger-Hostility .50 .08

Vigor-Activity -1.30 -.65

Fatigue-Inertia -.38 -.23

Confusion-Bewilderment .15 -.02










41

provided in standard deviation units from the college mean in Table 9, and represented in Figure 4 with Morgan's iceberg profile" (noninjured elite athletes) superimposed (Morgan et al., 1988). Most notable in this depiction is the reduction







80 -- Morgan's Iceberg Profile
(1988)
------ Injured Elite Athletes 70
Injured Recreational Athletes


60








40



30



20 I I I
Tension Depression Anger Vigor Fatigue Confusion Profile of Mood States Subscales






Figure 4. Injured elite and recreational
athletes' POMS subscale scores with Morgan's (1988) "iceberg profile" of noninjured elite
athletes.








42

on the vigor-activity scale experienced by elite athletes following injury (1.30 SD below the college mean and 3.30 SD below the mean represented by noninjured elite athletes). Table 10 depicts the percentage of subjects meeting clinical criteria for mood disturbance on individual subscales of the POMS (at least 2 SD above the noninjured mean reported by McNair, Lorr & Droppleman, 1992).




Table 10

Profile of Mood States Factor Scores of Injured Athletes: Percent of Subjects with Clinical Mood Disturbance in Each Sample



POMS Factor Elite Recreational



Tension-Anxiety 3% 0%

Depression-Dejection 13% 10%

Anger-Hostility 13% 0%

Vigor-Activity 35% 15%

Fatigue-Inertia 3% 5%

Confusion-Bewilderment 0% 5%








43
To examine whether injured athlete's POMS subscale scores differed significantly from the means reported by McNair, Lorr & Dropplemann (1992), Welch'*s (1947) statistical test was utilized. Results indicated that none of the subscale scores in either group differed significantly from the mean at the .05 alpha level.

Injury Severity

No significant difference in medical rating of injury severity between recreational and elite athletes was found. However, mean injury severity was higher for recreational athletes and approached significance [t(l,69)=-1.85, p=.069]. In addition, the elite group was significantly more variable than the recreational group on injury severity. Athletic Identity

As hypothesized, elite athletes reported significantly higher athletic identity on the Athletic Identity Measurement Scale than recreational athletes [t(1,69)=2.78, p<.0l]. This finding contributes internal validation to this study since a

natural factor distinguishing elite athletes from recreational athletes is selection to a division IA college football program.

Social Support

As hypothesized, elite athletes reported higher overall

levels of social support than recreational athletes. No

significance test was used for these differences since results appears largely due to the fact that there were more social








44
support providers available for elite athletes than recreational athletes.

Prediction of Mood Disturbance

Data were initially examined to assess for violations of the statistical assumptions of linearity, normality, and

homogeneity of variance. Scatterplots on the correlations between the POMS total score and each of the major independent variables are presented in Figures 5-7 (elite group) and Figures 8-10 (recreational group). Since no assumptions were

violated, multiple linear regression analyses were deemed appropriate to examine hypotheses 3 and 4. Elite Group

An initial stepwiselinear multiple regression analysis determined the order and magnitude of prediction of the

independent variables (injury severity, athletic identity, and social support) on total mood disturbance in the elite group. Table 11 displays these results. Total explained variance in mood disturbance was 27 percent with all three variables in

the model (Multiple R = .52, p = .04). In rejection of

hypothesis 3, the order of these variables in predicting total mood disturbance was social support (first) followed by injury severity (second) and athletic identity (third). Thus, social support contributed the most to prediction of total mood disturbance in the presence of all three variables.









45








60,




1 0
401 :






0 1 1
0
z=






0


0
0

0



1.0 2.0 3.0

INJURY SEVERITY




Figure 5. Scatterplot of correlations
between total mood disturbance (Profile
of Mood States total adjustment score) and trainer rating of injury severity
among elite athletes.









46





60





40






201 %
M




0
0
O




F- -20
60 80 160 120 140 160 180 200

TOTAL SOCIAL SUPPORT







Figure 6. Scatterplot of correlations
between total mood disturbance (Profile
of Mood States total adjustment score) and self-reported social support among
elite athletes.









47






60





401





20"
z

C O


0
o


0
-20
20 30 40 50 6 0 70


ATHLETIC IDENTITY






Figure 7. Scatterplot of correlations
between total mood disturbance (Profile
of Mood States total adjustment score)
and athletic identity (Athletic Identity Measurement Scale) among elite athletes.









48





60


50o


4030


LU 201 10J 10



0
0 S-10 -,

0
-20

1.0 2.0 3.0

INJURY SEVERITY







Figure 8. Scatterplot of correlations between total mood disturbance (Profile of Mood States total adjustment score)
and physician rating of injury severity
among recreational athletes.










49






60


50- o



40


30 0 o


W 20
U

0
z 000 o



0
-10
0



-20

20 40 60 80 100 120

TOTAL SOCIAL SUPPORT







Figure 9. Scatterplot of correlations between total mood disturbance (Profile
of Mood States total adjustment score)
and self-reported social support
among recreational athletes.









50





60 50


4030


w 20o
0-0

I

0

-10

I- -20 ,
20 3o 4o 50 60

ATHLETIC IDENTITY







Figure 10. Scatterplot of correlations between total mood disturbance (Profile
of Mood States total adjustment score)
and athletic identity (Athletic Identity
Measurement Scale) among recreational
athletes.








51

Table 11

Summary of Multiole Linear Regression Analysis for Variables Predicting Total Mood Distur~bance in the Elite Group


Standardized

Variable Beta Coefficient T P Level



Step 1

Injury Severity .45 2.67 .01

Step 2

Injury Severity .29 1.44 .16

Social Support -.28 -1.37 .18

Step 3

Social Support -.33 -1.53 .14

Injury Severity .25 1.18 .25

Athletic Identity .14 .78 .44







Recreational Group

A second stepwise linear multiple regression analysis was conducted to determine the order and magnitude of prediction

of the independent variables in the recreational group. Table 12 displays these results. Total explained variance in mood disturbance was only 14 percent with all three variables in

the model (Multiple R = .37, p = .14). Contrary to hypothesis 4, the order of prediction of these variables in predicting








52

total mood disturbance was injury severity (first) followed by athletic identity (second) and social support (third).





Table 12

Summary of Multiple Linear Regression Analysis for Variables Predicting Total Mood Disturbance in the Recreational Group



Standardized

Variable Beta Coefficient T P Level



Step 1

Injury Severity .30 1.92 .06

Step 2

Injury Severity .26 1.73 .09

Athletic Identity .22 1.45 .16

Step 3

Injury Severity .26 1.68 .10

Athletic Identity .22 1.40 .17

Social Support .04 .26 .80








53

Secondary Analyses

Exploratory analyses were conducted to determine the relationship between measures first used in the current study on total mood disturbance in each group. Pearson Product

Moment correlation coefficients were calculated between total mood disturbance and self ratings of prior physical

conditioning, pain, injury severity, vitality (at times 1 and 2) and mental health (at times 1 and 2). Results are reported in Table 13 (elite group) and Table 14 (recreational group).




Table 13

Pearson Product Moment Correlation Coefficients Between Total Mood Disturbance and--Measures First Used: Elite Group



Measure Correlated Coefficient P Level



Physical Conditioning -.15 .42

Pain Level -.16 .38

Injury Severity (Medical) .47 .01

Injury Severity (self) .32 .08

Vitality Time 1 -.29 .12

Vitality Time 2 -.52 .00

Mental Health Time 1 -.55 .00

Mental Health Time 2 -.56 .00








54
Table 14

Pearson Product Moment Correlation coefficients Between Total Mood Disturbgnce and Measures First Used: Recr@ational Group



Measure Correlated Coefficient P Level



Physical Conditioning -.01 .95

Pain Level .14 .41

Injury Severity (Medical) .30 .06

Injury Severity (Self) .59 .00

Vitality Time 1 -.49 .00

Vitality Time 2 -.52 .00

Mental Health Time 1 -.40 .01

Mental Health Time 2 -.43





Post-hoc Exploratory Analyses

Chi square analyses were conducted after classifying all subjects as low, medium, and high on the dependent variable

(POMS total score) and three independent variables (injury severity, social support and athletic identity). Classifications were made to secure a similar number in each

cell while also taking advantage of natural breaks in the data.









55

Table 15

Crosstabulation of Total Mood Level and Injury Severity



Total Mood Level

Low Medium High Total Injury Severity



Low 11 5 4 20

Medium 9 12 8 29

High 3 7 12 22

Total 23 24 24 71







Table 16

Crosstabulation of Total Mood Level and Athletic Identity



Total Mood Level

Low Medium High Total Athletic Identity



Low 10 7 5 22

Medium 9 9 8 26

High 4 8 11 23

Total 23 24 24 71








56

Table 17

Crosstabulation of Total Mood Level and Social Surngort



Total Mood Level

Low medium High Total Social Support



Low 8 10 5 23

Medium 6 8 10 24

High 9 6 8 23

Total 23 24 23 70







only the association between total mood level and injury severity was significant at the .05 level: (4, Ni = 71) = 10.80, p =.029). The associations between total mood level and athletic identity: (4, X = 71) = 5.04, p =.283), and total mood level and social support: (4, N = 70) = 3.24, p

= .519), failed to reach statistical significance. Crosstabulations of each independent variable by the dependent measure appear in tables 15, 16, and 17.

The final post-hoc exploratory analysis was conducted to determine whether it was possible to significantly predict to mood disturbance among subjects classified as either high or

low on total mood disturbance (n=47). A stepwise linear








57

regression analysis was conducted and results indicated that

the best overall predictor of mood disturbance was injury severity followed by athletic identity (second) and social support (third). injury severity by itself explained 17 percent of the variance in mood disturbance (Multiple R = .41, p = .005). Adding athletic identity to the equation increased the explained variance in mood disturbance to 24 percent (Multiple R = .49, p = .003). Adding social support did not significantly increase the explained variance in mood disturbance. These results appear in Table 18.





Table 18

Multiple Linear Regression Analysis for Variables Predicting High and LoW Total Mood Disturbance in Combined Group (N=47)


Standardized

Variable Beta Coefficient T P Level



Step 1

Injury Severity .41 2.99 .005

Step 2

Injury Severity .42 3.12 .003

Athletic Identity .27 2.00 .05














DISCUSSION

Explanation of Findings

This study examined an extension of a model of adjustment to athletic injury proposed by Brewer (1994). Emotional

response to athletic injury among elite and recreational

athletes was measured as total mood disturbance on the Profile of Mood States-Short Form (McNair, Lorr & Droppleman, 1992).

The contribution of one personal factor (athletic identity) and two situational factors (injury severity and social support) in predicting total mood disturbance was examined. Subjects were also compared across a variety of demographic

and self-report variables to gain knowledge of the differences between elite college student-athletes and college students who participate in athletics recreationally.

As hypothesized, independent t tests demonstrated that elite and recreational athletes reported nonsignificant differences on age, year in college, optimism, vitality, and

mental health at the .05 alpha level. However, elite athletes reported significantly higher levels of pain and physical conditioning, and lower injury severity at the .05 alpha level. As hypothesized, athletic identity and social support

were significantly higher among elite athletes at the .05 alpha level. Contrary to the hypothesis, a stepwise multiple


58








59

linear regression analysis revealed social support as the best overall predictor of mood disturbance in the elite group followed by injury severity (second) and athletic identity (third) In contrast, injury severity best predicted total mood disturbance among recreational athletes (as

hypothesized), with remaining variables in reverse order as hypothesized (athletic identity second, social support third).

since neither group scored in the clinically

interpretable range on total mood disturbance, two post-hoc exploratory analyses were conducted to estimate clinical mood disturbance. Groups were combined to add sufficient power to the analysis and variables were examined at three levels of total mood disturbance (low, medium and high). A chi square test of independence revealed that only injury severity was

significantly associated with total mood level at the .05 alpha level. A multiple linear regression analysis, with groups divided into high and low mood disturbance, revealed that injury severity and athletic identity together explained 24 percent of the variance in total mood disturbance

(significant at the .005 alpha level). Social support did not add significantly to the prediction of total mood disturbance in combined athletes in either analysis. Hypothesis 1

The initial hypothesis was to examine for differences between elite and recreational athletes on the demographic/subject variables represented on a continuous








60

scale. The groups were similar on measures of age, year in

college, optimism, vitality and mental health. Dispositional optimism (LOT-R) (Scheier & Carver, 1994), and well-being measures of vitality and mental health (SF-36) (Ware & Donald, 1992) were added as control features to the design. Knowing

that the groups did not differ on these anchor measures helped eliminate extraneous sources of variance in explaining findings.

Compared to a normative sample of college males reported

by Ware and Donald (1992), elite athletes scored approximately .8 standard deviations above the mean and recreational athletes approximately .6 standard deviations above the mean on the measure of dispositional optimism. This interesting

finding suggests that being an athlete is correlated with greater optimism. It is encouraging that the pearson product

moment correlation between total mood disturbance and optimism in this study was low and negative (-.12 with athletes combined), supporting the distinction between injury-induced,

transitory mood states measured by the POMS, and general attitudes reflected on the LOT-R.

Contrary to the first hypothesis, elite athletes reported significantly higher levels of pain and physical conditioning, and lower self-reported injury severity compared with recreational athletes. The lower variability of scores

reported by elite athletes on physical conditioning and injury severity measures (see standard deviations reported in Table








61

2) is not surprising since elite football players represent a more homogenous/intact group with more similar backgrounds in

football, regimented daily training routines, and exacting fitness standards.

Elite athletes' more positive estimation of their

previous physical conditioning and quicker estimations to recovery (player rated injury severity) may be due to a college football culture where physical hardiness and contribution to team goals are most highly valued. Although

elites predicted a more rapid self- recovery from injury, they did not deny their experience of pain. It is widely assumed

that being an elite football player involves playing with pain. Higher ratings of pain by elites may represent greater somatic awareness among individuals closely attuned to

disruptions in their physical functioning, more extensive tissue involvement, or an acceptable means of expressing lost athleticism due to injury (a .27 pearson product moment

correlation between pain and athletic identity for elites supports this latter possibility). Table 7 reveals that

elites experienced a greater proportion of larger surface injuries involving the knee.. shoulder, back, and leg, often from violent contact, whereas recreational athletes experienced more circumscribed ankle, finger, wrist, hand, arm and toe injuries. on the other hand, seven of the eight more severe fractures were experienced by the recreational group. Whether physical injury characteristics produced higher








62
sensations of pain is difficult to answer since only one pain rating was obtained and injury types varied widely.

It is plausible that the meaning of injury during a championship football season (e.g., reduced playing time, loss of status) was more devastating (and painful) for elite athletes than recreational athletes who identified less exclusively with the athlete role and had much less to lose. A trend toward higher total mood disturbance for elites also

supports greater injury-related distress. Table 13, however, demonstrates that higher pain ratings for elites were not positively correlated with total mood disturbance (-.16). Taken together, it appears that pain was more salient for injured elite football 'players, but that pain did not contribute significantly to mood disturbance. The greater pain reported by elite athletes is consistent with research

showing that negative interpretation of painful stimuli (e.g., equating pain with lost opportunity) might have increased pain and elevated emotional disturbance too (Jensen et al., 1991).

Caution should be exercised in interpreting differences in pain intensity between the groups si nce ethnic differences

in pain intensity have been reported (Thomas & Rose, 1991; Faucett, Gordon, & Levine, 1994). In the current study, African Americans averaged 1.4 points higher on self-reported

pain intensity. Although this trend is consistent with the literature showing lower ratings of pain among Caucasian

groups, differences in this study did not reach statistical








63
significance. Another limitation involves use of self-report measures and retrospective analyses. Future studies could supplement self-report ratings with physiological or

behavioral indices, external ratings, or multiple stages of data collection.

Hypothes s 2

The second hypothesis was to examine whether elite

athletes reported greater athletic identity and social support compared with recreational athletes. Table 8 displays these results and confirms these hypotheses.

That elite athletes reported significantly higher athletic identity compared with recreational athletes (approximately one standard deviation difference) is not

surprising, and this finding adds internal validity to the design. These groups indeed represent very different levels

of athleticism. Scores on the AIMS for the recreational group were consistent with those reported for "recreational /fitness" athletes by Brewer (1993). AIMS scores by the elite group were approximately one standard deviation below the mean

reported for "intercollegiate/national" athletes by Brewer (1993). Thus, there is an even more elite level of professional athletes than the current sample of college football players that would have scored even higher on athletic identity.

Finding higher social support among elite athletes was not surprising given the measure used in this study. College








64

football players will score higher than recreational athletes as a result of more social support available in the football program (e.g.., coaches, trainers, physicians). This measure was selected based on work by Smith, Smoll, and Ptacek (199o) because it represented the range of potential social support

providers for the elite sample and had good psychometric properties. A post-hoc examination of social support data,

however, revealed that even when all seven athletically loaded items were eliminated removed from this measure (e.g., team trainer/physician, head coach, closest teammate), elite athletes still scored 24 points higher than recreational athletes ([t(1,68)=-6.03, p7-.000]. While social support scores reported by recreational athletes were approximately one standard deviation higher than the high school varsity athlete norms provided by Smith, Smoll, and Ptacek (1990), elite athletes scored approximately 3 standard deviations above this norm. Although both elite and recreational

athletes reported higher than average social support, the extremely high reports of social support by elite athletes highlights the very social nature of football. Social support appears to represent a major benefit associated with participating in college football. Future studies might

incorporate even more elite samples of injured professional athletes and assess whether athletic identity and social support continue to rise with level of participation. The same degree of perceived social support might not exist at an








65
even more competitive professional level, or in an individual sport like tennis, but this awaits empirical verification. Hypothesis 3.

The third hypothesis was to evaluate the relative

contributions of the three independent measures in predicting total mood disturbance among elite athletes. It was

hypothesized that injury severity would best predict total mood disturbance followed by athletic identity (second) and social support (third). This hypothesis was rejected, as social support best predicted total mood disturbance with all

three variables in the model (Table 11). This surprising

finding lends support to the value of perceived social support involving perceptions of both help and guidance and emotional

support and caring (Smith, Smoll & Ptacek, 1990). Football is a team sport in which reliance upon others is critical, and current results underscore this point. Finding that social

support explained greater variance in mood disturbance than either injury severity or athletic identity might also reflect the vulnerability of elite football players when social support is low or absent. Sport psychological interventions

aimed at enhancing social support, especially for targeted athletes, would seem to be an appropriate means of alleviating psychological distress and hastening recovery following injury.

It does not appear that perceived social support is a correlate of mood disturbance among athletes in general, as








66

the pearson product moment correlation between social support and total mood disturbance was -.44 for elite athletes, but only .09 for, recreational athletes. Rather, injured elite football players with low social support might have been much more susceptible to emotional disturbances following injury,

or injury occurrence during a championship football season might have led to perceptions of reduced social support.

Assessing social support, challenging negative selfstatements and mobilizing support resources may be highly beneficial for injured elite athletes. However, this study

did not examine whether total mood disturbance correlated with success in physical rehabilitation. This should be addressed

in future studies utilizing a longitudinal design and also assessing the potential effects of these treatments in different sports.

Hypothesis 4

The fourth hypothesis was to evaluate the relative

contributions of the three independent measures in predicting total mood disturbance among recreational athletes. It was

hypothesized that injury severity would best predict total mood disturbance followed by social support (second) and athletic identity (third). As hypothesized, injury severity

best predicted total mood disturbance. The order of remaining variables was reversed with athletic identity (second) and social support (third), and total explained variance in mood

disturbance was very low (.14). This suggests that predicting








67

to mood disturbance for recreational athletes in various sports is more difficult than for elite athletes in football.

Injury severity was the best overall predictor of total

mood disturbance for recreational athletes and social support added almost nothing to the prediction equation. it is

plausible that because recreational athletes are less

experienced social support consumers, unusual attention drawn to an injury might equate with greater severity for

recreational athletes. This possibility is supported by a .28 pearson product moment correlation between social support and player rating of injury severity (for elites this same correlation was -.18).

However, it is questionable whether prediction to mood disturbance was even addressed in the current analysis, since total mood disturbance was .03 standard deviations below the

male college student mean. Although 35 of the 40 injured recreational athletes were participants in team sports, the importance of team and social influences for these part-time

participants cannot be equated with the level of group and social commitment on a national championship football team. Finally, explained variance in total mood disturbance was only 14 percent for recreational athletes (compared to 27 percent

for elite athletes) with all three independent variables in the model. This low explained variance indicates that future research is needed to identify other correlates of

maladjustment following 'injury. Elite athletes appear to








68

share a major advantage in social support compared to recreational athletes, but the effects of social support for injured recreational athletes awaits further empirical investigation. Future studies should utilize a larger sample size involving more athletes with actual mood disturbance. Secondary Analyses

Secondary correlational analyses were conducted to determine the relationship between measures first used in the current study and total mood disturbance. These appear in Tables 13 and 14. Mental well being showed the highest correlation with total mood disturbance for elite athletes (-.56). This may reflect a greater psychological investment

in remaining free of irfjury for elites. Self -ratings of

injury severity showed highest correlations with total mood disturbance (. 59) in the recreational sample. Thus, the

degree of recreational athletes' emotional distress increased

with perceptions that their injury was more serious (for elites, this correlation was only .32). Finally, a

dissociation between elite and recreational athletes occurred on correlations between mood disturbance and pain (-.16 for elite athletes, .14 for recreational athletes). This appears to indicate that self-reported pain is less disturbing

emotionally for elite athletes than for recreational athletes. Post-hoc Exploratory Analyses

One question of the current study is whether it is even possible to test a model of emotional disturbance when scores








69
on this measure were not in the clinically interpretable range. Although they may not have been clinically significant compared to a noninjured population, it should be remembered

that elite athletes are a unique population as demonstrated on the "iceberg profile" (Figure 4). Thus, mood disturbance for elite athletes might manifest as normal scores compared with the super-profiles usually obtained. Although being an elite athlete appears to carry some emotional advantages as reflected on the "iceberg profile," negative responses following injury may suggest that insulation from mood disturbance is often shortlived. Nevertheless, the question of subclinical mood disturbance still remains, and certainly for the even less mood disturbed recreational sample.

To address this issue, two post-hoc exploratory analyses

were conducted to estimate a clinical question of greater mood disturbance. All subjects were combined to obtain sufficient power in the analysis. This represents a limitation in the analysis, as mixed recreational and elite athletes are quite different from either population alone. Chi square analyses indicated that only the association between total mood level and injury severity was significant (Table 15). However,

there is a visible trend in which athletes with greater athletic identity appeared to be classified into greater mood disturbance categories (Table 16).

To add further power to the analysis, the second post-hoc exploratory analysis divided athletes into only the top third








70
and bottom third on mood disturbance. A multiple linear

regression analysis further confirmed the value of knowing both injury severity and athletic identity in predicting mood disturbance. These results appear in Table 18. Although these findings should not be generalized to a true clinical population, they offer guidance for future research.

Overview and Future Research

The present study can be viewed as a preliminary

investigation into the correlates of postinjury emotional disturbance following athletic injury. However, the

generalizability of this study is necessarily limited to the

acute phase following injury. Still little is known about how these responses correlate with actual injury recovery. it should also be noted that three very different analyses were conducted with different groups and different findings. One examined elite football players, a second examined a diverse

recreational group, and a third examined these groups combined (post-hoc exploratory analyses). Further, most measures in this study were self-report instruments which may have been

influenced by response biases, especially given the sensitive nature of asking national champion football players to report their emotional weaknesses in the training room. Although medical experts provided injury severity ratings based on known injury type and expected time to recovery, the

pragmatics of involving two separate data collection sites made it impossible to use the same rater for each athlete








71
group. Nevertheless, this preliminary study provides a basis

for further investigation and informs clinical practice in sport injury.rehabilitation. Future studies might employ a

longitudinal design to better assess outcome, capture injured athletes with greater emotional disturbance, and examine the responses of injured athletes in different sports.

In sum, the present study provides empirical evidence of

differences between elite and recreational athletes, their mood responses following injury, and factors associated with these responses. Mood disturbance did not attain clinical significance compared with a college student norm. For elite athletes, however, this may underestimate the actual level of

distress they experienced, compared with the usual mood profile of elite athletes. Compared with recreational athletes, elite athletes had higher self-reports of athletic identity, social support, pain, and previous physical conditioning, and lower perceptions of injury severity. For elite football players, social support was the greatest predictor of emotional disturbance (with athletic identity and injury severity

known). For recreational and mixed athletes, injury severity, followed by athletic identity, were the best predictors.

Thus, an impetus is provided for future research to more closely examine these and other factors important in emotional and physical recovery from sport injury.








72
Clinical Implications

More research is needed before widespread conclusions about the best psychological treatments for sport injury can

be made. Given the paucity of literature in this area and potentially devastating effects of injury, however, there is

every reason to develop aggressive treatment approaches to this problem faced by so many individuals. Greater assessment and treatment protocols should be developed by sports medicine teams to anticipate and treat the psychological effects of injury. Heil's (1993) Sports Medicine Injury Checklist is an example of an excellent tool to identify problems in adjustment to injuryFindings from the current study suggest indicate that for elite athletes, social support should be a major focus. It is recommended that elite teams evaluate social support long before injury occurrence and provide special interventions to

those low on this factor. Psychoeducational outreach to

explain the importanceof social support in the team setting is also encouraged. Athlete and coach awareness of the powerful influence of social support needs to be reinforced.

Following injury, a variety of psychological treatments should be readily available ranging from imagery and relaxation to counseling and psychotherapy. For recreational athletes, the clinical implications from this study are less clear. The emotional impact of sport injury may be less for these individuals since they may undergo less identity foreclosure








73

with the athlete role, but this does not eliminate the need for prompt psychological care in situations that warrant it.

since emotional disturbance is possible following any injury, these athletes should be carefully provided with high quality

professional attention to enhance emotional adjustment and recovery. Some recreational athletes may be more susceptible to adjustment difficulties than elite athletes due to many fewer support resources available.














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BIOGRAPHICAL SKETCH

John Francis Murray was born in Fort Lauderdale, Florida, on November 30, 1961. He is the second of four children of J. Richard and Joan P. Murray. Raised in south Florida, he attended Loyola University New Orleans where he majored in psychology. After graduation in May 1983 with a Bachelor of

Arts degree, John travelled extensively as a professional tennis coach. He directed, managed and taught tennis at clubs, hotels and resorts in North America, Hawaii, Europe and the Middle East. John returned to the academic world in 1991

and obtained a Master of' Exercise and Sport Sciences degree from the University of Florida (with an emphasis in sport psychology) in 1992. John entered the doctoral studies

graduate program in the Department of Clinical and Health Psychology at the University of Florida in 1992. He obtained

a Master of Science degree in clinical psychology in 1995 with a specialization in medical psychology.













82














I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy.


Eileen Fennell, Chair
Professor of Clinical and
Health Psychology

I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, iry-cope and quality, as a dissertation for the degree PDhiosophy.

es Rodri4E
ociate Professor of
Clinical and Health Psychology

I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, iniscope and quality, as a dissertation for the degree'hf Docto of Philosophy.


Duane Dede
Assistant Professor of
Clinical and Health Psychology

I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as
dissertation for the degree of Doctor of Philosophy


aimuel Sears '
Assistant Professor of
Clinical and Health Psychology









I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree ,f Doctor of PDilosophy.


M011 illedge Murphey
Associate Professor of
Exercise and Sport Sciences

This dissertation was submitted to the Graduate Faculty of the College of Health Professions and to the Graduate School and was accepted as partial fulfillment of the requirements for the degree of Doctor of Philosophy.

August, 1998

Dean, College of Health
Professions

Dean, Graduate School




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EMOTIONAL ADJUSTMENT TO SPORT INJURY: EFFECTS OF INJURY
SEVERITY, SOCIAL SUPPORT AND ATHLETIC IDENTITY
By
JOHN F. MURRAY
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
1998

ACKNOWLEDGEMENTS
I am very grateful for the support and guidance of my
committee members, Drs. Eileen Fennell, James Rodrigue, Duane
Dede, Sam Sears, and Milledge Murphey. I owe a special debt
of gratitude to Dr. Fennell whose wisdom, enthusiasm, and
patience as my committee chair and mentor were invaluable.
Credit is also due Chris Patrick and Drs. Peter Indelicato,
Robert Frank, and Richard Gutekunst for supporting my research
and helping secure my access to the football team. I also
wish to thank Mike Wasik and Tony Mennella for assisting with
data collection, and all athletes who agreed to participate in
this study including members of the football team that won the
national championship. Finally, I express my deepest
appreciation to my wife, Charlotte, and to all other members
of my family.
11

TABLE OF CONTENTS
page
ACKNOWLEDGEMENTS ii
ABSTRACT iv
INTRODUCTION 1
Literature Review 3
Hypotheses 20
METHOD 21
Subjects 21
Measures 21
Procedures 2 8
RESULTS 30
Design and Analysis 30
Demographic/Subject Data 31
Dependent and Independent Variables 38
Prediction of Mood Disturbance 44
Secondary Analyses 53
Post-hoc Exploratory Analyses 54
DISCUSSION 58
Explanation of Findings 58
Overview and Future Research 70
Clinical Implications 72
REFERENCES 74
BIOGRAPHICAL SKETCH 8 2
iii

Abstract of Dissertation Presented to the Graduate
School of the University of Florida in Partial Fulfillment
of the Requirements for the Degree of Doctor of Philosophy
EMOTIONAL ADJUSTMENT TO SPORT INJURY: EFFECTS OF INJURY
SEVERITY, SOCIAL SUPPORT AND ATHLETIC IDENTITY
By
John F. Murray
August 1998
Chairperson: Eileen B. Fennell, Ph.D., ABPP
Major Department: Clinical and Health Psychology
This study examined a stress and coping model suggesting
that emotional adjustment to sport injury is determined by
appraisal of personal and situational factors. The
contributions of injury severity, social support, and athletic
identity to mood disturbance in elite and recreational
athletes were examined. Another purpose was to identify
differences between elite and recreational athletes including
their emotional responses to sport injury in the acute phase.
Thirty-one male collegiate football players on a division IA
championship team and forty male recreational athletes
completed the Profile of Mood States (POMS) -Short Form within
72 hours after sustaining a sport injury. It was hypothesized
that injury severity would best predict total mood disturbance
in each group, with second best predictors being athletic
IV

identity among elite athletes and social support among
recreational athletes.
Independent t tests showed that elite athletes reported
significantly higher athletic identity, social support, and
physical conditioning than recreational athletes at the .05
level. Elite athletes also reported significantly reduced
vigor, higher pain, and higher anger (approaching
significance) despite equal physician-rated and lower self
reported injury severity.
Multiple regression analyses confirmed that injury
severity was the best predictor of mood disturbance in the
recreational group, however, the remaining variables were in
reverse order as hypothesized (athletic identity second,
social support third). Unexpectedly, social support best
predicted total mood disturbance in the elite group with all
three variables in the model, followed by injury severity
(secoond) and athletic identity (third). Further
investigation should identify other factors correlated with
post-injury maladjustment and longitudinally examine whether
manipulating these variables (e.g., social support
enhancement) will improve emotional adjustment and hasten
recovery.
v

INTRODUCTION
Sport injuries occur with tremendous frequency, often
leading to severe mood disturbances and delayed recovery,
although some athletes appear to adjust quite well. The
impact of this problem was recognized by the National
Institute of Arthritis, Musculoskeletal, and Skin Diseases
which designated sports injuries as a "major health issue"
(Booth, 1987). Unfortunately, few empirical data are
available to clarify the influence of psychosocial factors in
athletic injury because many of the reports are unsystematic,
anecdotal, or theoretical in nature (Rose & Jevne, 1993). The
emotional responses of athletes to injury, and factors
associated with post-injury psychological adjustment, need to
be better understood for the psychology of sport injury to
progress. Although more is known about psychological factors
leading to injury, recent models (Wiese-Bjornstal & Smith,
1993; Grove, 1993) have spurred investigation into the
emotional consequences of athletic injury and the role that
psychological factors play in injury recovery (Gould, Udry,
Bridges, & Beck, 1997).
Borrowing from Lazarus and Folkman's (1984) transactional
theory of stress and coping, these models are based on the
assumption that cognitive appraisal plays a central role in
1

2
determining whether an injured athlete experiences stress
leading to emotional disturbance. Factors hypothesized to
influence cognitive appraisal (and emotional response)
following injury include athletic identity, social support,
and injury severity.
Postinjury emotional disturbance has been associated with
poor adherence to sport injury rehabilitation regimens (Daly,
Brewer, Van Raalte, Petipas, & Sklar, 1995). Understanding
the emotional effects of sport injury, and factors related to
these responses, is essential to reduce costly distress and
rehabilitation setbacks. This knowledge would assist health
care providers in identifying athletes at risk for emotional
maladjustment and associated behavioral problems. Preventive
measures could then be enhanced before injury and improved
treatment provided after injury. Personnel selection would
likely improve by knowing which psychological qualities best
predict post-injury emotional adjustment and recovery. This
line of research could also be extended into other stress
reduction and personnel selection endeavors (e.g., business,
performing arts).
The present study examined the emotional responses of
elite and recreational athletes to sport injury using the
Profile of Mood States (POMS) Short Form total score as the
main outcome measure. The ability of three independent
variables (injury severity, social support and athletic
identity) to predict total mood disturbance was also examined.

3
Knowing how elite and recreational athletes differentially
respond to injury helps clinicians adjust their interventions
accordingly.
The overall aim of this study was to gain better insight
into the emotional impact of sport injury and contribute to
those disciplines invested in reducing the stress of athletic
injury.
Literature Review
Overview of Stress
Approaching the twenty-first century, stress is often
described as a common fact of life. Factors contributing to
stress include physical injury and illness, mental
deprivation, or any variety of factors that an individual may
perceive as "taxing." Historically, stress has been viewed as
both a stimulus and a response. Walter Cannon's (1932)
stimulus definition emphasized factors external to the
organism (e.g., cold, lack of oxygen) which threaten
homeostasis and elicit a "fight or flight" response. However,
the term "stress" was first adopted by Hans Selye (1956) whose
response definition included an internal state within the
organism subject to a perceived threat (stressor). Selye
(1956) viewed stress as a common denominator underlying all
adaptive responses within the body, and a bodily defense to a
variety of physiological and psychological insults. Both
Cannon (1932) and Selye (1956) viewed stress mainly from a
physiological perspective.

4
Perhaps the most influential psychological model of
stress gained prominence in Lazarus and Folkman's "Stress,
Appraisal, & Coping" (1984). This transactional theory
(highlighting the bi-directional relationship, or transaction,
between person and environment) of stress and coping
identifies cognitive appraisal as the key factor in
determining whether environmental stimuli are "stressful."
From this perspective, the individual evaluates the nature of
the stressor (primary appraisal), as well as his or her
personal resources (secondary appraisal), and determines
whether there are sufficient resources to meet the challenge.
If resources are adequate, or the stimuli are not perceived as
threatening, then the situation is not stress inducing.
Conversely, threat perceived as unmanageable leads to stress.
Although recent transactional theories like those of
Lazarus and Folkman (1984) require further empirical
validation, support for the importance of cognitive appraisal
has already accrued. For example, pain research indicates
that one's interpretation of stimuli is critical and that
belief in control over pain is correlated with reduced pain
sensations (Jensen, Turner, Romano, & Karoly, 1991). This
research also suggests that catastrophizers fare consistently
worse than minimizers in terms of overall functioning.
Whereas catastrophizers tend to engage in negativistic
thinking and worry in response to pain, minimizers are capable
of sustaining morale by engaging in more constructive positive

5
thinking. Other examples include findings from the field of
psychoneuroimmunology which suggest that cognitive appraisal
may influence immune system functioning (Ader & Cohen, 1993).
It should be mentioned that contrary positive views of
stress also exist. For example, Dienstbier (1989) proclaimed
that some level of intermittent stress is actually health
promoting in producing increased physiological toughness.
Toughness was operationalized as lower base arousal rates,
greater sympathetic nervous system sensitivity, reduced
depletion of catecholamine reserves (needed for high level
energy to meet the demands of potentially stressful
encounters), and lower cortisol secretions. Similarly, Aldwin
(1995) asserted that learning to deal with adversity fosters
human development. Nevertheless, prolonged or extreme stress
is viewed as harmful even by Dienstbier (1989).
Stress has been linked to health problems and adverse
health behaviors including increased cardiovascular response,
vasoconstriction, platelet aggregation and plaque rupture
(Kamark & Jennings 1991), infectious diseases (Kiecolt-Glaser,
Garner, Speicher, Holliday & Glaser, 1984), autoimmune disease
(Cohen & Herbert, 1996), smoking and alcohol abuse (Ader &
Matthews, 1994), and a wide range of other physical and
psychological problems (Matarazzo, 1983; Cooper, 1996). As an
identified major health risk factor, it seems worthwhile to
examine for factors which protect against stress or enhance
the adequacy of coping.

6
The stress of athletic injury
Sports injuries are a tremendous source of stress and
serious health risk. Heil (1993) portrayed injuries as the
ultimate stressor in the life of the athlete and Burwitz,
Moore, & Wilkinson (1994) described them as the most important
issue in sports. Alarming data come from Kraus and Conroy
(1984) who reported 6,045 athletically/recreationally related
deaths in 1978. In addition, there are an estimated seventeen
million sport injuries per year in the United States alone,
including one million in high school football involving ten
fatalities (Mueller & Blythe, 1987). In another report, Smith
& Milliner (1991) stated that athletic injury combined with
serious pre-injury stress prompted at least five suicide
attempts at one university.
The psychology of sport injury rehabilitation is an
emerging area of inquiry in both sports medicine and sport
psychology (Laubach, Brewer, Van Raalte, & Petipas, 1996). As
in many areas of health care, the most complete picture of
sport injuries will emerge when researchers adopt a
multidisciplinary approach that incorporates physiological,
environmental and psychological factors (Udry, 1996). Larson,
Starkey, & Zaichkowsky (1996) claimed that stress, anger and
anxiety are the most frequently encountered psychological
conditions associated with injury, but techniques to reduce
these conditions are not frequently used.

7
According to Heil (1993), "the groundwork for a
psychology of sport injury has been laid by a series of
developments in sport including a growing appreciation of the
scope, severity and underlying causes of injury." (p.l).
Negative emotional reactions to athletic injury cited in the
literature include anger, depression, tension, frustration,
reduced vigor, and emotional distress severe enough to warrant
clinical intervention (Smith, Scott, O'Fallon & Young, 1990;
Smith, Scott & Wiese, 1990; Leddy, Lambert, & Ogles, 1994).
There is a major gap, however, between the perceived need
for psychological services and the actual care offered. For
example, forty-seven percent of a large sample of certified
athletic trainers recently reported that every injured athlete
suffers psychological trauma, but only twenty-five percent
have a sport psychologist as a member of the sports medicine
team (Larsen, Starkey, & Zaichkowsky, 1996). The athletic
trainer for the Miami Dolphins recently stated that great
individual variability exists in emotional responses to injury
(Vermillion, personal communication, September 27, 1995).
Empirical research is needed to explain this variability.
Healthcare professionals are also concerned about the
psychosocial impact of athletic injuries because the post¬
injury emotional response may influence medical decisions such
as the optimal timing for surgical intervention and the
athlete's post-injury rehabilitation (Smith, 1996).

8
It is also important to recognize that many injured
athletes appear to adjust quite well (Brewer, Linder, &
Phelps, 1995). Although emotional response to athletic injury
was traditionally conceptualized in terms of stage models
based on models of grief and loss (Rotella & Heyman, 1986),
there has been little empirical support for these models in
sports (Evans & Hardy, 1995).
Factors deemed important in the cognitive, emotional and
behavioral responses of athletes to injury were recently
elucidated in models by Wiese-Bjornstal and Smith (1993) and
Grove (1993) (See Figures 1 & 2). Influenced by Lazarus and
Folkman's (1984) transactional theory of stress and coping,
and the injury prediction model of Andersen and Williams
(1988), these models are based on the assumption that post¬
injury adjustment is determined by the athlete's cognitive
appraisal of the injury and its effects.
Personal and situational factors are believed to
influence cognitive appraisal and moderate the relationship
between injury and post-injury adjustment (Brewer, Linder, &
Phelps, 1995). Personal factors are stable dispositional
characteristics of an individual (e.g., locus of control,
self-concept), whereas situational factors are unstable,
variable aspects of the social and physical environment (e.g.,
injury severity, social support) (Brewer, Linder, & Phelps,
1995). It seems worthwhile to conduct research examining
which factors most influence the degree to which athletic

9
injuries are appraised as stressful (leading to poorer
emotional adjustment).
Personal factors and athletic injury
Knowledge of an athlete's personal characteristics should
help sports medicine practitioners to anticipate, understand,
and deal with undesirable rehabilitation responses (Grove,
1993). Brewer (1994) reviewed several personal factors
hypothesized to affect cognitive appraisals of (and presumably
emotional responses to) athletic injury including trait
anxiety, self-esteem, self-motivation, coping skills,
extraversión, neuroticism, psychological investment in sport,
and injury history. Other potentially important personal
Moderators
Mediators
Response to injury Physical and
and rehabilitaion psychological
process recovery process
Figure 1. Predictors of cognitive, emotional
and behavioral responses of athletes to injury
and rehabilitation (Wiese-Bjornstal & Smith,
1995) .

10
History of
Stressors
Sport
Injury
Injury
Related
Factors
Personality
>
REHABILITATION
Cognitions <-> Behaviors
t 4 t 4
Physiological Reactions
Psychological
Interventions
Coping
Resourses
Recovery &
Return to
Competition
Treatment
Related
Factors
Figure 2. Model of rehabilitation from sport
injury (Grove, 1993).
factors include athletic identity (Brewer, 1993), locus of
control (Rotter, Chance, & Phares, 1972), commitment (Kobasa,

11
Maddi & Kahn, 1982), explanatory style, dispositional
optimism, and hardiness (Grove, 1993), trait anxiety
(Spielberger,. 1972), sense of coherence (Antonovsky, 1985),
and self-esteem (Rosenberg, 1979).
Empirical evidence for the role of personal factors in
emotional adjustment to athletic injury is sparse, as few
studies have directly addressed this relationship. However,
Brewer (1994) described five personal variables: psychological
investment in sport, physical self-esteem, age, pessimistic
explanatory style, and hardiness-which have been correlated
with emotional reactions to injury. Brewer (1993) also
presented a series of studies in which a strong and exclusive
identification with the ' athlete role was associated with
depressed mood following sport injuries. Thus, individuals
whose self-worth was derived exclusively or predominantly
through athletic performance were more likely to appraise
their injury in terms of threat or loss (Brewer, Van Raalte,
& Linder, 1993).
Situational factors and athletic injury
A number of situational factors have also been found to
be correlated with post-injury emotional adjustment. Positive
correlations include: medical prognosis (Albert, 1988),
recovery progress (McDonald & Hardy, 1990), social support for
rehabilitation and impairment of sports performance (Brewer,
Van Raalte, & Linder (1991), and physician rated current
injury status. Situational factors that have been found to be

12
inversely related to post-injury emotional adjustment are
injury severity (Smith, Scott, O'Fallon, & Young, 1990),
duration of injury (McDonald & Hardy, 1990), impairment of
daily activities (Crossman & Jamieson, 1985), and life stress
(Brewer, 1993).
Several other situational variables hypothesized to
influence post-injury emotional adjustment, but currently
without empirical support, include personal control over
recovery, time of athletic season, point in athletic career,
type of sport, social pressures, injury onset, injury course,
rehabilitation self-efficacy, and pain (Brewer, 1994).
Overview of Copina
Current views of coping were influenced by animal
experimentation models which emphasized avoidance behavior to
control aversive conditions and lower arousal (Miller, 1980),
and psychoanalytic models which depicted coping as realistic
and flexible thoughts and acts that solve problems (Menninger,
1963). Both of these approaches have been criticized as
overly simplistic for understanding the complexity of human
coping (Lazarus & Folkman, 1984, p.139).
Lazarus and Folkman (1984) define coping as "the
constantly changing cognitive and behavioral efforts to manage
external and/or internal demands appraised as taxing or
exceeding the resources of the person (p.141)." They assert
that differences in cognitive appraisal explain much of the
variability in how individuals respond to potentially

13
stressful situations. Lazarus and Folkman have also
contributed to the notion that dispositional measures of
coping do not adequately characterize the range of coping
strategies used in dealing with complex situations (Coyne &
Downey, 1991). Recently, a consensus has developed on the
basic dimensions of coping and how to assess them. Coping has
been conceptualized in terms of approach vs avoidance (Suls &
Fletcher, 1985) and in terms of appraisal, problem focus, and
emotion focus (Billings & Moos, 1984). Lazarus and Folkman's
distinction between problem-focused and emotion-focused coping
has been by far the most influential conceptualization, and
various versions of their Ways of Coping Checklist (WOCC) have
been utilized in literally hundreds of studies (Coyne &
Downey, 1991).
Copina with general medical problems
Effective coping may play an important role in health
promotion and disease prevention (Rodin & Salovey, 1989).
Coping can influence hormone levels, cause direct tissue
changes, or affect the immune system (Jacobs, Mason, Kosten,
Kasl, & Ostfeld, 1985). Interpersonal coping styles may also
influence the type of care received (e.g., demanding, task-
oriented patients may have their complaints acted upon more
quickly). Effective coping has been linked to quicker
recovery from illness, and active participant coping
strategies may be especially effective in this regard (Cohen
& Lazarus, 1979).

14
Most studies focus on coping with severe medical
conditions. For example, Thompson, Gil, Abrams, and Phillips
(1992) showed that good psychological adjustment was
associated with less use of palliative coping methods among
adults with sickle cell disease. Palliative coping is a
defensive form of coping in which the individual attempts to
regulate negative emotional states. This is contrasted with
instrumental coping, in which the individual actively attempts
to alter the situation through efforts directed at the
environment or self. Folkman, Chesney, Pollack, and Coates
(1993) found that stress appraised as controllable was
associated with involvement coping, which in turn was
associated with diminished depressive mood. Involvement
coping was characterized by planful problem solving, social
support seeking, and positive reappraisal. Finally,
Somerfield and Curbow (1992) demonstrated that coping is
important in maintaining psychological well-being among
individuals with cancer.
Bernard and Krupat (1994) outline two major approaches to
help patients with a variety of medical difficulties to cope
more effectively. Arousal management strategies attempt to
alter physiological response through use of medications,
relaxation exercises and exercise. Transaction management
attempts to affect the cognitive appraisal of stress, through
problem focused or avoidant coping techniques.

15
Coping with sport injuries
Research is only beginning to examine how athletes cope
with injury, whereas studies examining coping resources as a
predictor of injury are far more common. For example,
Williams, Tonymon and Wadsworth (1986) showed that a low level
of coping resources was the strongest predictor of injury
among volleyball players, and Smith, Smoll, & Ptacek (1990)
found that athletes low in both social support and coping
skills exhibited the greatest injury risk.
Some of the challenges facing athletes in coping with
injury include dealing with emotions, reorganizing their
lifestyles, maintaining hope, coping with self-concept
changes, and coping with changes in relationships with friends
and family (Rose & Jevne, 1993). Crossman (1997) pointed out
that mood disturbance for an athlete is usually a departure
from the norm since most athletes are mentally healthy
individuals. Morgan's "iceberg profile" (Morgan, Costill,
Flynn, Raglin, & O'Connor, 1988) represents a popular graphic
depicting the mental health of elite athletes, whose scores on
the Profile of Mood States (McNair, Lorr & Dropplemann, 1992)
often produce a distinguished iceberg shape formed by a
singular elevation on the vigor scale, and low scores on
negative scales including anxiety and depression. Crossman
(1997) pointed out that since athletes are usually healthy,
they may be less prepared psychologically for the stress
associated with an unforseen or unexpected injury.

16
Many anecdotal reports exist to identify strategies
athletes use to best cope with injury. Larson, Starkey, and
Zaichkowsky (1996) noted that athletic trainers ranked a
series of behaviors, characteristics and attitudes of athletes
who cope successfully with injury. The three most successful
were rehabilitation compliance, positive attitude about injury
and life, and motivation to work hard during rehabilitation.
Williams and Roepke (1993) outlined several coping factors
including trying to find the meaning of the injury,
facilitating grief, forming goals, eliminating self-defeating
thoughts, managing pain, managing stress, and continuing to
improve performance skill. Finally, Crossman (1997)
identified goal setting' and visualization as potentially
valuable coping tools.
Recently, it was found that Stress Inoculation Training
(SIT) (Meichenbaum, 1985) reduced pain and anxiety, and led to
more rapid return to physical functioning among knee injured
athletes (Ross & Berger, 1996). The Stress Inoculation
Training program involves three components: conceptualization,
skills acquisition, and application. Participants are
educated on the theories of emotion and pain, trained in self¬
monitoring and coping methods (e.g., relaxation strategies,
self-reinforcement), and instructed to engage in these coping
strategies in response to discomfort or pain cues. This type
of study is important in demonstrating treatment efficacy and
establishing clinical sport psychology as a valuable segment

17
of the sports medicine team. However, more fundamental
research is also needed to identify those factors most
important in adjustment to athletic injury. As these elements
are identified, the quality of psychological services should
improve accordingly.
How Sport Injuries are Unique
In studying athletic behavior, sport psychologists have
long borrowed from principles in psychology, but the sport
psychological literature has proceeded at a slow pace and it
has been difficult to find consistent results (Ogilvie, 1987;
Nideffer, 1989). This may be in part due to inherent
differences between athletes and the more traditional
populations examined in psychological research. Heil (1993)
maintains that injured athletes are different from general
medical patients in a number of ways. He states that athletes
are more goal oriented, have a greater proclivity for physical
training, better pain tolerance, more experience, and a
greater motivation to recover. Similarly, Pargman (1993)
noted that "the problem of malingering is probably not
overwhelming in sport compared with the more general medical
population (p.69)."
In sum, athletic injuries represent a unique challenge,
best resolved through empirical research conducted with
appropriate sport populations. Studies examining the
influence of personal and situational factors on post-injury

18
emotional adjustment should contribute significantly to the
psychology of sport injury.
Summary Statement of Problem
The present study was designed to examine the
relationship between three factors previously identified in
the literature as associated with adjustment to sport injury,
and total mood disturbance following sport injury. These
factors were injury severity, athletic identity and social
support. A simple model, extended from Brewer (1994),
depicting these factors and total mood disturbance was
examined (see Figure 3).
For purposes of this study, injury severity was
operationalized as "estimated number of days until expected
return to athletic participation for the injury sustained," as
rated by the athletic trainer (elite group) or physician
(recreational group). Athletic identity was operationalized
as the score received on the the Athletic Identity
Measurement Scale (AIMS) (Brewer, Van Raalte, & Linder, 1993).
Social support was operationalized as the combined scale
scores of the measures used by Smith, Smoll, & Ptacek (1990)
to assess amount and quality of social support. Finally,
total mood disturbance was operationalized as the total score
from the Profile of Mood States-Short Form (McNair, Lorr, &
Droppleman, 1992).

19
ADJUSTMENT TO ATHLETIC INJURY
Figure 3. Cognitive appraisal model of psychological
adjustment to athletic injury including dependent
variable (Profile of Mood States) and independent
variables (Athletic Identity, Injury Severity and
Social support) (extended from Brewer, 1994).

20
Hypotheses
The following hypotheses were developed to empirically
test for differences between injured elite and recreational
athletes, and their emotional response to athletic injury.
Hypothesis 1
It was hypothesized that recreational and elite athletes
would not differ significantly on any of the
demographic/subject variables represented on a continuous
scale including age, year in college, self-ratings of prior
physical conditioning, dispositional optimism, current pain
level, injury severity, physical well being (vitality), and
emotional well being (mental health).
Hypothesis 2
It was hypothesized that athletic identity and social
support would be significantly higher among elite athletes
than recreational athletes.
Hypothesis 3
Among elite athletes, it was hypothesized that injury
severity would best predict total mood disturbance followed by
athletic identity (second) and social support (third).
Hypothesis 4
Among recreational athletes, it was hypothesized that
injury severity would best predict total mood disturbance
followed by social support (second) and athletic identity
(third).

METHOD
Subjects
Seventy-one male college students (31 male varsity
football players from an NCAA division IA championship
football program and 40 male recreational athletes) were
recruited. Inclusion criteria were: (1) between 18 and 28
years of age; (2) physical injury from sport participation
within the previous 72 hours; (3) at least one day of missed
sport participation due to injury; (4) no history of playing
a sport professionally; and (5) not under the influence of any
substance which might impair cognitive functioning at the time
of testing. Near unanimous participation was obtained. One
recreational athlete asked to be excluded from the study for
undisclosed reasons after completing measures, and one varsity
football player refused entry over concern that his injury
status might be discovered by NFL personnel prior to the
draft.
Measures
Demographic Questionnaire
All subjects completed a demographic/subject
questionnaire reporting their age, ethnic identity, year in
college, major area of study, classification as either varsity
or recreational athlete and sport in which they were injured.
21

22
Varsity participants were further classified with regard to
current position on the team (e.g., quarterback, linebacker)
and level of participation (starter or nonstarter).
Medical Questionnaire
A medical questionnaire was completed by each subject
indicating current and past medical conditions, medications,
estimated physical condition immediately prior to injury, and
current pain level. Other questions assessed past injuries,
date and time of current injury, method of becoming injured,
and self-reported perception of injury severity (days expected
until further athletic participation). Assigned medical
personnel provided injury data including diagnosis, severity
rating (mild, moderate, or severe determined by estimated days
until further athletic participation), and whether the injury
was considered season ending or career ending. Injury
severity ratings were based on time loss and followed the
National Athletic Injury/Illness Reporting System (NAIRS;
Coddington & Troxell, 1980). Quantitative self-reports of
pre-injury physical condition and current pain level were
accomplished using visual analog scales (VAS; Keefe, Brown,
Scott, & Ziesat, 1982). For pain assessment, this consisted
of a 10-point line, with 0 indicating "no pain" and
10 indicating "pain as bad as it could be." Respondents were
instructed to place a mark on the line indicating their
current level of pain intensity and scoring involved measuring
the distance from the "no pain" (0) end to the respondent's

23
mark. Pre-injury physical condition was measured in a similar
manner, and scored in the reverse direction with 0 indicating
"my worst physical condition ever" and 10 indicating "my best
physical condition ever."
SF-36 Health Survey (Physical/Emotional Well Being1
To obtain an index of physical and emotional well being
and add control to the design, two sections from the SF-36
Health Survey (Ware & Donald, 1992) assessing vitality
(physical well being) and mental health (emotional well being)
were utilized. Both the vitality and mental health scales are
bipolar in nature and tap well being, measuring a wide range
of negative and positive health states.
The SF-36 satisfies rigorous psychometric criteria for
construct validity and internal consistency and most
reliability estimates exceed the .80 level (Sullivan,
Karlsson, & Ware, 1995). The vitality scale and mental health
scale display good psychometric qualities including item-
discriminant validity and scale reliability. The vitality
scale is intended to capture differences in subjective
physical well being (e.g., energy level and fatigue) whereas
the mental health scale includes one or more items from each
of four major mental health dimensions (anxiety, depression,
loss of behavioral/emotional control, and psychological well
being). The vitality scale is derived from four items (raw
score range of 4-24) and the mental health scale is derived
from 5 items (raw score range of 5-30) from the SF-36. Raw

24
scores are then converted into as a standard score with a
range of possible scores on each scale of 0-100. This
standard score was used in the analysis.
Subjects completed each section twice, once for current
well being and again as a retrospective assessment of their
physical and emotional states immediately following injury.
Profile of Mood States-Short Form (POMS'!
To measure emotional adjustment following injury, the
total adjustment score from the Profile of Mood States-Short
Form (McNair, Lorr, & Droppleman, 1992) was utilized. This
measure assesses transient mood states across six dimensions:
tension-anxiety, depression-dejection, anger-hostility, vigor-
activity, fatigue-inertia, and confusion-bewilderment, and
provides a global score of overall mood disturbance. The
advantages of the POMS include its speed and ease of
administration (Eichman, 1978).
The POMS has been cited in at least 56 published papers
since its widespread use in sports research began in 1975
(McNair, Lorr, & Dropplemann, 1992), and participants in 19
different sports have served as subjects in studies using the
POMS (LeUnes, Hayward, & Daiss, 1988). The POMS has excellent
psychometric properties (Pargman, 1993) and has been validated
on populations across a variety of domains. Internal
consistency of the POMS factors is highly satisfactory, with
reliability coefficients ranging from .87 to .95 (McNair,
Lorr, & Dropplemann, 1992). Test-retest reliability estimates

25
ranged from .65 to .74 on a university medical center
psychiatry clinic. The POMS has also shown good concurrent
validity by its correlations with similar types of
instruments.
The POMS short form (McNair, Lorr, & Dropplemann, 1992)
was developed based on a need for a form that is even easier
to complete and consists of 30 items and the same six scales
as measured by the long form. The POMS-Short Form correlates
very highly with POMS long version, with factor correlations
among a college sample ranging from .67 to .88. The total
adjustment score used in the analysis was obtained by adding
scores from each of the 6 subscales with the vigor dimension
weighted negatively. The range of possible scores is thus -20
to 100.
Athletic Identity Measurement Scale (AIMS'!
The Athletic Identity Measurement Scale (AIMS) (Brewer,
Van Raalte, & Linder, 1993) measures the degree to which an
individual identifies with the athletic role and the
exclusivity of that identification. The AIMS consists of 10
statements in which the athlete is asked to indicate his/her
agreement with the athletic role on a 7 point likert scale
anchored by "strongly agree" and "strongly disagree." The
range of possible scores on the AIMS total scores used in the
analysis ranged from 10-70.
Initial studies by the authors show the AIMS to possess
excellent psychometric qualities (Brewer, Van Raalte, &

26
Linder, 1993). Test-retest reliability of the AIMS was .89,
indicating that the AIMS is stable over a 14 day period. A
coefficient alpha of .93 provided support for the internal
consistency of the AIMS. Construct validity was also
demonstrated as the AIMS correlated significantly (.83) with
a scale measuring perceived importance of sport competence,
the PIP Importance of Sports Competence Scale. Finally, none
of the AIMS item scores were significantly correlated with
scores on the short form of the Marlowe-Crowne Social
Desirability Scale (Crowne & Marlowe, 1960), suggesting that
scores do not reflect a social desirability response bias.
Social Support
The measure of social support used in this project was
created by Smith, Smoll, and Ptacek (1990). This measures the
amount and quality of social support available to subjects
from 20 different individuals (e.g., mother, father, coach,
and best friend) and groups (e.g., teammates and clubs or
religious groups to which they belong). This measure was
derived from one used by Cauce, Felner, and Primavera (1982)
to assess adolescents' subjective appraisals of the individual
and group components of their social support network. Since
athletes were assessed in this study, the additions to the
scale by Smith, Smoll, and Ptacek (1990) were used. These
included added items for assistant coach, trainer, team
physician, and athletic religious organizations to assess the
athletic environment in greater detail.

27
On separate scales, athletes were asked to indicate the
extent to which each individual and group could be counted on
to provide them with (a) emotional support and caring, and (b)
help and guidance, on a scale ranging from "not at all
helpful" (1) to "very helpful" (5). Scores were summed to
provide an overall measure of the two varieties of social
support. The range of possible scores on the two scales is
thus 20 to 100. For the present study, a grand total social
support index was obtained by summing the two varieties of
social support.
On a separate sample of 94 athletes, the social support
scale had a one-week test-retest reliability of .87 for
emotional support and .88 for help and guidance, and the two
social support measures correlated .88 with one another
(Smith, Smoll, & Ptacek, 1990).
Life Orientation Test-Revised (LOT-R'l
The Life Orientation Test-Revised (LOT-R) (Schier &
Carver, 1994) is a measure of dispositonal optimism that was
used to add further control to the design while examining the
relationship between outcome expectancies and emotional
adjustment in the two groups. The LOT-R was derived from a
theory that optimists (those with expectations of successful
outcomes) are more persistent and more effective than
pessimistic individuals in their goal directed behaviors in a
variety of domains (Schier & Carver, 1985). Dispositional
optimism has been proposed to affect many areas of functioning

28
including health, performance, self-reported physical
symptoms, and ability to complete a rehabilitation program
(Schier & Carver, 1985).
The LOT-R has excellent psychometric properties. It was
normed on a sample of 2,055 undergraduates and showed high
internal consistency and test-retest reliability, and
excellent convergent and discriminant validity. Cronbach's
alpha for the entire scale was .76 and the test-retest
correlation was .79 (Schier & Carver, 1985).
Subjects were instructed to indicate the extent of their
agreement with each of ten items of the LOT-R anchored by
0=strongly disagree and 4=strongly agree. Six of the 10 items
are used to derive the LOT-R total score used in the study
while the remaining 4 items are distractors. The range of
possible scores is thus 0-24.
Procedures
Testing was conducted in a consistent manner for all
subjects to reduce potential sources of experimental bias.
All data were handled as confidential medical information, and
stored in a locked file cabinet in the Department of Clinical
and Health Psychology at the University of Florida.
Injured football players reporting to the University
Athletic Association (UAA) training room, and injured students
reporting to the University Health Care Center, were
approached by the principal investigator while awaiting
treatment and asked to provide an additional 20 minutes to

29
take part in the study. After informed consent was obtained
subjects were asked to complete the measures. Subjects first
completed the initial section of the demographic questionnaire
to determine whether they met criteria for further inclusion
into the study. Two recreational athletes were excused from
the study and thanked for their participation because they
were older than 28 years of age. All other subjects met
requirements for continued entry and completed the demographic
instrument followed by the medical questionnaire, POMS, AIMS,
and Social Support measure. All questions were written at a
6th grade reading level, but subjects were offered and
provided verbal clarification on items as requested.
Following data collection, subjects were thanked for their
participation. All additional data (e.g., diagnosis, severity
classification) were collected from the health center
physician and certified athletic trainer assigned to football.

RESULTS
Design and Analysis
Group differences on demographic/subject measures
represented on a continuous scale were assessed using
independent t tests with alpha set at the .05 level.
Similarly, group differences on the main dependent measure
(POMS total score) and two of the major independent variables
(athletic identity and medical rating of injury severity) were
assessed using independent t tests with alpha set at the .05
level. Differences between groups on the social support
measure were reported descriptively rather than by using a
significance test. This was because the social support scale
included questions about individuals much less available to
recreational athletes than elite athletes (e.g., team manager,
team trainer, head coach, assistant coach), explaining large
group differences on the measure. Means and standard
deviations were computed for all variables.
Prediction of mood disturbance within each group by the
three main independent variables was accomplished using two
separate stepwise linear regression analyses. An exploratory
analysis was conducted to examine the relationship between
measures first used in this study and total mood disturbance.
Correlation coefficients were calculated between total mood
30

31
disturbance and self-ratings of prior physical conditioning,
current pain, injury severity, vitality, mental health and
optimism.
A post-hoc exploratory analysis was conducted to examine
for differences between athletes at different levels of mood.
All subjects were combined to ensure a sufficient sample size
(n=71) and then grouped into low, medium and high categories
on the dependent measure (POMS total score) and independent
measures (athletic identity, injury severity, and social
support). Three separate chi square tests of independence
were used to examine whether classification on mood level was
independent of classification on the independent measures.
Finally, a second post-hoc exploratory analysis was
conducted to determine the order and magnitude of prediction
of the main independent variables on total mood disturbance
among subjects classified only low or high on total mood
disturbance (n=47). A stepwise linear regression analysis was
used for this purpose.
Demographic/Subject Data
All subjects reported their age, year in college, ethnic
identity, major area of study, sport, position on team, date,
time, and method of injury, past injuries, current
medications, days expected to miss participation, current pain
level, estimated physical condition immediately before injury,
optimism from the LOT-R, and vitality and mental health from
the SF-36. Elite subjects further reported their starter vs.

32
nonstarter and redshirt vs. non-redshirt status. Independent
t tests on demographic/subject variables represented on a
continuous scale were conducted with alpha set at .05.
Table 1
Demographic/Subiect Variables Showing Nonsignificant
Differences by Group Classification
Elite Recreational
Variable
M
(SD)
M
(SD)
t
Age
20.71
(1.19)
21.15
(2.57)
-.88
Year
3.10
( .94)
2.98
(1.33)
.43
LOT-R
17.26
(3.88)
16.20
(3.64)
1.18
V-l
47.74
(16.92)
50.00
(20.10)
-.50
V-2
53.87
(18.96)
52.05
(19.22)
.40
MH-1
67.03
(17.12)
62.30
(21.26)
1.01
MH-2
72.52
(16.99)
71.69
(16.98)
. 20
Note. Year depicts year in college, LOT-R depicts the
Life Orientation Test (Revised), and V and MH depict the
Vitality and Mental Health scales from the SF-36 Health
Survey (1 = at time of injury, 2= currently). For all t
values, p = ns.

33
Table 2
Demographic/Subject Variables Showing Significant Differences
by Group Classification
Variable
Elite
Recreational
t (1
,69)
M
(SB)
M
(SB)
Pain
5.71
(2.44)
3.48
(2.42)
3.84 *
p<.000
PhyCon
8.54
(1.41)
7.30
(2.39)
2.56 *
p<.001
InjSev
9.40
(10.40)
17.25
(18.24)
-2.14 *
p<.036
Note. Pain and PhyCon (Physical Conditioning prior to
injury) are based on a 1-10 scale with higher numbers
indicating greater amounts. InjSev (self-reported injury
severity) is based on estimated days until resumed activity.
Elite and recreational athletes reported similar age,
year in college, dispositional optimism and well being ratings
both immediately following injury and at time of testing.
Between-group differences on pain, physical conditioning and
self-reported injury severity were significant with elite
athletes reporting higher pain and physical conditioning, but
lower injury severity. Recreational athletes had more
variable physical conditioning and pain ratings than elites.
Table 1 reports the means, standard deviations, and associated
t statistics for demographic/subject variables in which no

34
significant differences were found. Table 2 reports the
means, standard deviations, and associated t statistics for
demographic/subject variables in which significant differences
were found. No other significant differences were noted on
continuous demographic/subject measures.
Table 3
Ethnic Identity: Number and Percent of Group
Elite Recreational Total
Ethnic ID
n
%
n
%
n
%
Caucasian
12
(39%)
33
(83%)
45
(63%)
Afr. Amer.
19
(61%)
3
(7.5%)
22
(31%)
Hispanic
0
(0%)
3
(7.5%)
3
(4%)
Asian
0
(0%)
1
(2%)
1
(1%)
Frequency distributions are reported for noncontinuous
demographic/subject variables including ethnic identity in
Table 3, injury type in Table 4, team position for elite
football players in Table 5, injury types and location in
Table 6, and sport for recreational athletes in Table 7.

Table 4
Injury Type; Number and Percent of Group
Elite Recreational Total
Injury Type
n
%
n %
n %
Sprains
20
(65%)
24
(60%)
44
(62%)
Strains
6
(19%)
3
(8%)
9
(13%)
Contusions
3
(10%)
6
(15%)
9
(13%)
Fractures
1
(3%)
7
(17%)
8
(11%)
Other
1
(3%)
0
(0%)
1
(1%)
Table 5
Football Positions: Number and Percent of Group
of Elite Athletes
Position on Team n %
Offense 14 (45%)
Linemen 7 (23%)
Running Backs 4 (13%)
Receivers 2 (6%)
(3%)
Kickers
1

Table 5—continued
Position on Team
n
%
Defense
17
(55%)
Linemen
10
(32%)
Secondary
6
(20%)
Linebackers
1
(3%)
Table 6
Injury Types & Location: Number and Percent of Group
Elite Recreational Total
Type and Location
n
%
n
%
n
%
Sprains
20
(65%)
24
(60%)
42
(62%)
Ankle
8
(26%)
14
(35%)
20
(28%)
Knee
7
(23%)
5
(13%)
12
(17%)
Finger
0
(0%)
4
(10%)
4
(6%)
Shoulder
2
(7%)
0
(0%)
2
(3%)
Back
2
(7%)
0
(0%)
2
(3%)
Leg
1
(3%)
0
(0%)
1
(3%)
Wrist
0
(0%)
1
(3%)
1
(3%)

Table 6—continued
Elite Recreational Total
Type and Location n % n % n %
Strains
Knee
Back
Hamstring
Abdomen
Shoulder
Contusions
Knee
Shoulder
Thigh
Finger
Hand
Arm
Fractures
Finger
Leg
Toe
Hand
Plexitis
6 (19%)
1 (3%)
1 (3%)
2 (7%)
1 (3%)
1 (3%)
3 (10%)
1 (3%)
2 (7%)
0 (0%)
0 (0%)
0 (0%)
0 (0%)
1 (3%)
0 (0%)
1 (3%)
0 (0%)
0 (0%)
1 (3%)
1 (3%)
3
(8%)
9
(13%)
2
(5%)
3
(4%)
1
(2.5%)
2
(3%)
0
(0%)
2
(3%)
0
(0%)
1
(1%)
0
(0%)
1
(1%)
6
(15%)
9
(13%)
1
(2.5%)
2
(3%)
0
(0%)
2
(3%)
2
(5%)
2
(3%)
1
(2.5%)
1
(1%)
1
(2.5%)
1
(1%)
1
(2.5%)
1
(1%)
7
(17%)
8
(11%)
4
(10%)
4
(6%)
1
(2.5%)
2
(3%)
1
(2.5%)
1
(2.5%)
1
(2.5%)
1
(2.5%)
0
(0%)
1
(1%)
0
(0%)
1
(1%)
Other

38
Table 7
Recreational Sports: Number and Percent of Group
Sport
n
%
Basketball
16
(40%)
Flag Football
9
(23%)
Roller Hockey
3
(7.5%)
Soccer
3
(7.5%)
Racquetball
2
(5%)
Running
2
(5%)
Softball
2
(5%)
Wrestling
1
(2.5%)
Volleyball
1
(2.5%)
Ultimate Frisbee
1
(2.5%)
Dependent and Independent Variables
Means, standard deviations, and associated t statistics
for the dependent and independent variables appear in Table 8.
Total Mood Disturbance
Between-group differences on the main dependent measure
(total mood disturbance from the POMS Short Form) were not
significant [t(1,69)=1.20, p=.24]. On the whole, elite and
recreational athletes appear to have experienced similar total

39
levels of mood disturbance following sport injury.
In addition, these scores were not in the clinically
interpretable range. Compared to a noninjured male college
Table 8
Dependent (DV) and Major Independent Variables dVsl
Elite Recreational
Variable
M
(SD)
M
(SD)
t (1,69)
E
POMS
21.16
(19.01)
' 16.15
(16.22)
1.20
.24
Injury
1.84
( .86)
2.18
(.68)
-1.85
.07
AIMS
44.19
(7.44)
38.76
(8.67)
2.78 *
.007
Social
127.87
(31.50)
66.83
(17.92)
Note. POMS depicts total mood disturbance from the
Profile of Mood States Short Form, Injury depicts
medical rating of injury severity from 1 (mild) to
3 (severe), AIMS depicts the Athletic Identity
Measurement Scale, and Social depicts social support.
*p<.01
sample (McNair, Lorr & Droppleman, 1992, p.23), injured elite
athletes scored .33 standard deviations above the mean while
injured recreational athletes scored .03 standard deviations
below the mean. Furthermore, only 4% of all subjects met
clinical criteria for total mood disturbance (2 SD above the

40
mean). Thus, global mood disturbance does not appear to have
resulted in this study, at least in comparison to the moods of
noninjured college students.
For illustrative purposes, a comparison was made between
elite and recreational athletes' POMS subscale scores and the
previously mentioned noninjured college sample provided by
McNair, Lorr & Dropplemann (1992). Subscale scores are
Table 9
Profile of Mood States Factor Scores of Injured
Athletes Compared to a Non-iniured College Sample
POMS Factor
Elite
SD from Norm
Recreational
SD from Norm
Tension-Anxiety
-.46
-.44
Depression-Dejection
.18
.01
Anger-Hostility
.50
.08
Vigor-Activity
-1.30
-.65
Fatigue-Inertia
-.38
-.23
Confusion-Bewilderment
.15
-.02

41
provided in standard deviation units from the college mean in
Table 9, and represented in Figure 4 with Morgan's "iceberg
profile" (noninjured elite athletes) superimposed (Morgan et
al., 1988). Most notable in this depiction is the reduction
Profile of Mood States - Subscales
Figure 4. Injured elite and recreational
athletes' POMS subscale scores with Morgan's
(1988) "iceberg profile" of noninjured elite
athletes.

42
on the vigor-activity scale experienced by elite athletes
following injury (1.30 SD below the college mean and 3.30 SD
below the mean represented by noninjured elite athletes).
Table 10 depicts the percentage of subjects meeting clinical
criteria for mood disturbance on individual subscales of the
POMS (at least 2 SD above the nonin jured mean reported by
McNair, Lorr & Droppleman, 1992).
Table 10
Profile of Mood States Factor Scores of Injured
Athletes; Percent of Subjects with Clinical Mood
Disturbance in Each Sample
POMS Factor
Elite
Recreational
Tension-Anxiety
3%
0%
Depression-Dejection
13%
10%
Anger-Hostility
13%
0%
Vigor-Activity
35%
15%
Fatigue-Inertia
3%
5%
Confusion-Bewilderment
0%
5%

43
To examine whether injured athlete's POMS subscale scores
differed significantly from the means reported by McNair, Lorr
& Dropplemann (1992), Welch's (1947) statistical test was
utilized. Results indicated that none of the subscale scores
in either group differed significantly from the mean at the
.05 alpha level.
Injury Severity
No significant difference in medical rating of injury
severity between recreational and elite athletes was found.
However, mean injury severity was higher for recreational
athletes and approached significance [t(1,69)=-l.85, p=.069].
In addition, the elite group was significantly more variable
than the recreational group on injury severity.
Athletic Identity
As hypothesized, elite athletes reported significantly
higher athletic identity on the Athletic Identity Measurement
Scale than recreational athletes [t(l,69)=2.78, p<.01]. This
finding contributes internal validation to this study since a
natural factor distinguishing elite athletes from recreational
athletes is selection to a division IA college football
program.
Social Support
As hypothesized, elite athletes reported higher overall
levels of social support than recreational athletes. No
significance test was used for these differences since results
appears largely due to the fact that there were more social

44
support providers available for elite athletes than
recreational athletes.
Prediction of Mood Disturbance
Data were initially examined to assess for violations of
the statistical assumptions of linearity, normality, and
homogeneity of variance. Scatterplots on the correlations
between the POMS total score and each of the major independent
variables are presented in Figures 5-7 (elite group) and
Figures 8-10 (recreational group). Since no assumptions were
violated, multiple linear regression analyses were deemed
appropriate to examine hypotheses 3 and 4.
Elite Group
An initial stepwise linear multiple regression analysis
determined the order and magnitude of prediction of the
independent variables (injury severity, athletic identity, and
social support) on total mood disturbance in the elite group.
Table 11 displays these results. Total explained variance in
mood disturbance was 27 percent with all three variables in
the model (Multiple R = .52, p = .04). In rejection of
hypothesis 3, the order of these variables in predicting total
mood disturbance was social support (first) followed by injury
severity (second) and athletic identity (third). Thus, social
support contributed the most to prediction of total mood
disturbance in the presence of all three variables.

TOTAL MOOD DISTURBANCE
45
60-
401
20-
0-
-20.
1.0
2.0
INJURY SEVERITY
3.0
Figure 5. Scatterplot of correlations
between total mood disturbance (Profile
of Mood States total adjustment score)
and trainer rating of injury severity
among elite athletes.

TOTAL MOOD DISTURBANCE
46
60
I
I
i
¡
I
201 =>
â–¡
i
I
I
|
¡
¡
I
CM
'20 T T r ,
60 80 100 120 140 160 180 200
TOTAL SOCIAL SUPPORT
Figure 6. Scatterplot of correlations
between total mood disturbance (Profile
of Mood States total adjustment score)
and self-reported social support among
elite athletes.

TOTAL MOOD DISTURBANCE
47
60
40 -j
20 H
a
20
30
40
50
60
70
ATHLETIC IDENTITY
Figure 7. Scatterplot of correlations
between total mood disturbance (Profile
of Mood States total adjustment score)
and athletic identity (Athletic Identity
Measurement Scale) among elite athletes.

TOTAL MOOD DISTURBANCE
48
60
50-
40-
301
í
|
20-j
I
i
I
io-!
INJURY SEVERITY
¡
i
I
i
3.0
Figure 8. Scatterplot of correlations
between total mood disturbance (Profile
of Mood States total adjustment score)
and physician rating of injury severity
among recreational athletes.

TOTAL MOOD DISTURBANCE
49
60-
50-
40-
30 J
20 -
10-
0-
-10-
-20 „
20
40
60
80
100
120
TOTAL SOCIAL SUPPORT
Figure 9. Scatterplot of correlations
between total mood disturbance (Profile
of Mood States total adjustment score)
and self-reported social support
among recreational athletes.

TOTAL MOOD DISTURBANCE
50
60 — —
a
50-
40-
30-
20-
I.
10-
0-
-10
3
-2on T t ;
20 30 40 50 60
ATHLETIC IDENTITY
Figure 10. Scatterplot of correlations
between total mood disturbance (Profile
of Mood States total adjustment score)
and athletic identity (Athletic Identity
Measurement Scale) among recreational
athletes.

51
Table 11
Summary of Multiple Linear Regression Analysis for Variables
Predicting Total Mood Disturbance in the Elite Group
Variable
Standardized
Beta Coefficient
T
P Level
Step 1
Injury Severity
.45
2.67
.01
Step 2
Injury Severity
.29
1.44
.16
Social Support
-.28
-1.37
.18
Step 3
Social Support
-.33
-1.53
.14
Injury Severity
.25
1.18
.25
Athletic Identity
.14
.78
.44
Recreational Group
A second stepwise linear multiple regression analysis was
conducted to determine the order and magnitude of prediction
of the independent variables in the recreational group. Table
12 displays these results. Total explained variance in mood
disturbance was only 14 percent with all three variables in
the model (Multiple R = .37, p = .14). Contrary to hypothesis
4, the order of prediction of these variables in predicting

52
total mood disturbance was injury severity (first) followed by
athletic identity (second) and social support (third).
Table 12
Summary of Multiple Linear Regression Analysis for Variables
Predicting Total Mood Disturbance in the Recreational Group
Variable
Standardized
Beta Coefficient
T
P Level
Step 1
Injury Severity
. 30
1.92
.06
Step 2
Injury Severity
. 26
1.73
.09
Athletic Identity
. 22
1.45
.16
Step 3
Injury Severity
.26
1.68
.10
Athletic Identity
. 22
1.40
.17
Social Support
.04
.26
.80

53
Secondary Analyses
Exploratory analyses were conducted to determine the
relationship between measures first used in the current study
on total mood disturbance in each group. Pearson Product
Moment correlation coefficients were calculated between total
mood disturbance and self ratings of prior physical
conditioning, pain, injury severity, vitality (at times 1 and
2) and mental health (at times 1 and 2). Results are reported
in Table 13 (elite group) and Table 14 (recreational group).
Table 13
Pearson Product Moment Correlation Coefficients Between Total
Mood Disturbance and Measures First Used:
Elite Grouo
Measure Correlated Coefficient
P Level
Physical Conditioning
-.15
.42
Pain Level
-.16
.38
Injury Severity (Medical)
.47
.01
Injury Severity (Self)
.32
.08
Vitality Time 1
-.29
. 12
Vitality Time 2
-.52
.00
Mental Health Time 1
-.55
. 00
Mental Health Time 2
-.56
. 00

54
Table 14
Pearson Product Moment Correlation Coefficients Between Total
Mood Disturbance and Measures First Used: Recreational Group
Measure Correlated
Coefficient
P Level
Physical Conditioning
-.01
.95
Pain Level
. 14
.41
Injury Severity (Medical)
. 30
.06
Injury Severity (Self)
.59
.00
Vitality Time 1
-.49
.00
Vitality Time 2
-.52
.00
Mental Health Time 1
-.40
.01
Mental Health Time 2
-.43
.01
Post-hoc Exploratory Analyses
Chi square analyses were conducted after classifying all
subjects as low, medium, and high on the dependent variable
(POMS total score) and three independent variables (injury
severity, social support and athletic identity).
Classifications were made to secure a similar number in each
cell while also taking advantage of natural breaks in the
data.

55
Table 15
Crosstabulation of Total Mood Level and Iniurv Severity
Total Mood Level
Injury Severity
Low
Medium
High
Total
Low
11
5
4
20
Medium
9
12
8
29
High
3
7
12
22
Total
23
24
24
71
Table 16
Crosstabulation of Total Mood Level and Athletic Identity
Total Mood Level
Low Medium High Total
Athletic Identity
Low 10
Medium 9
High 4
7 5 22
9 8 26
8 11 23
Total
23
24
24
71

56
Table 17
Crosstabulation of Total Mood Level and Social Support
Total
Mood Level
Low
Medium
High
Total
Social Support
Low
8
10
5
23
Medium
6
8
10
24
High
9
6
8
23
Total
23
24
23
70
Only the
association
between
total mood level
and injury
severity was
significant
at the
.05 level:
(4,
N = 71) =
10.80, p = .029). The associations between
total
mood level
and athletic
identity:
(4, N =
71) = 5.04, p =
.283) , and
total mood level and social support: (4, N = 70) = 3.24, p
= .519), failed to reach statistical significance.
Crosstabulations of each independent variable by the dependent
measure appear in tables 15, 16, and 17.
The final post-hoc exploratory analysis was conducted to
determine whether it was possible to significantly predict to
mood disturbance among subjects classified as either high or
low on total mood disturbance (n=47). A stepwise linear

57
regression analysis was conducted and results indicated that
the best overall predictor of mood disturbance was injury
severity followed by athletic identity (second) and social
support (third). Injury severity by itself explained 17
percent of the variance in mood disturbance (Multiple R = .41,
p = .005). Adding athletic identity to the equation increased
the explained variance in mood disturbance to 24 percent
(Multiple R = .49, p = .003). Adding social support did not
significantly increase the explained variance in mood
disturbance. These results appear in Table 18.
Table 18
Multiple Linear Regression Analysis for Variables Predicting
High and Low Total Mood Disturbance in Combined Group (N=47~)
Variable
Standardized
Beta Coefficient
T
P Level
Step
1
Injury Severity
.41
2.99
.005
Step
2
Injury Severity
. 42
3.12
.003
Athletic Identity
. 27
2.00
.05

DISCUSSION
Explanation of Findings
This study examined an extension of a model of adjustment
to athletic injury proposed by Brewer (1994). Emotional
response to athletic injury among elite and recreational
athletes was measured as total mood disturbance on the Profile
of Mood States-Short Form (McNair, Lorr & Droppleman, 1992).
The contribution of one personal factor (athletic identity)
and two situational factors (injury severity and social
support) in predicting total mood disturbance was examined.
Subjects were also compared across a variety of demographic
and self-report variables to gain knowledge of the differences
between elite college student-athletes and college students
who participate in athletics recreationally.
As hypothesized, independent t tests demonstrated that
elite and recreational athletes reported nonsignificant
differences on age, year in college, optimism, vitality, and
mental health at the .05 alpha level. However, elite athletes
reported significantly higher levels of pain and physical
conditioning, and lower injury severity at the .05 alpha
level. As hypothesized, athletic identity and social support
were significantly higher among elite athletes at the .05
alpha level. Contrary to the hypothesis, a stepwise multiple
58

59
linear regression analysis revealed social support as the best
overall predictor of mood disturbance in the elite group
followed by injury severity (second) and athletic identity
(third). In contrast, injury severity best predicted total
mood disturbance among recreational athletes (as
hypothesized), with remaining variables in reverse order as
hypothesized (athletic identity second, social support third).
Since neither group scored in the clinically
interpretable range on total mood disturbance, two post-hoc
exploratory analyses were conducted to estimate clinical mood
disturbance. Groups were combined to add sufficient power to
the analysis and variables were examined at three levels of
total mood disturbance (low, medium and high). A chi square
test of independence revealed that only injury severity was
significantly associated with total mood level at the .05
alpha level. A multiple linear regression analysis, with
groups divided into high and low mood disturbance, revealed
that injury severity and athletic identity together explained
24 percent of the variance in total mood disturbance
(significant at the .005 alpha level). Social support did not
add significantly to the prediction of total mood disturbance
in combined athletes in either analysis.
Hypothesis 1
The initial hypothesis was to examine for differences
between elite and recreational athletes on the
demographic/subject variables represented on a continuous

60
scale. The groups were similar on measures of age, year in
college, optimism, vitality and mental health. Dispositional
optimism (LOT-R) (Scheier & Carver, 1994), and well-being
measures of vitality and mental health (SF-36) (Ware & Donald,
1992) were added as control features to the design. Knowing
that the groups did not differ on these anchor measures helped
eliminate extraneous sources of variance in explaining
findings.
Compared to a normative sample of college males reported
by Ware and Donald (1992), elite athletes scored approximately
. 8 standard deviations above the mean and recreational
athletes approximately .6 standard deviations above the mean
on the measure of dispositional optimism. This interesting
finding suggests that being an athlete is correlated with
greater optimism. It is encouraging that the pearson product
moment correlation between total mood disturbance and optimism
in this study was low and negative (-.12 with athletes
combined), supporting the distinction between injury-induced
transitory mood states measured by the POMS, and general
attitudes reflected on the LOT-R.
Contrary to the first hypothesis, elite athletes reported
significantly higher levels of pain and physical conditioning,
and lower self-reported injury severity compared with
recreational athletes. The lower variability of scores
reported by elite athletes on physical conditioning and injury
severity measures (see standard deviations reported in Table

61
2) is not surprising since elite football players represent a
more homogenous/intact group with more similar backgrounds in
football, regimented daily training routines, and exacting
fitness standards.
Elite athletes' more positive estimation of their
previous physical conditioning and quicker estimations to
recovery (player rated injury severity) may be due to a
college football culture where physical hardiness and
contribution to team goals are most highly valued. Although
elites predicted a more rapid self- recovery from injury, they
did not deny their experience of pain. It is widely assumed
that being an elite football player involves playing with
pain. Higher ratings of pain by elites may represent greater
somatic awareness among individuals closely attuned to
disruptions in their physical functioning, more extensive
tissue involvement, or an acceptable means of expressing lost
athleticism due to injury (a .27 pearson product moment
correlation between pain and athletic identity for elites
supports this latter possibility). Table 7 reveals that
elites experienced a greater proportion of larger surface
injuries involving the knee, shoulder, back, and leg, often
from violent contact, whereas recreational athletes
experienced more circumscribed ankle, finger, wrist, hand, arm
and toe injuries. On the other hand, seven of the eight more
severe fractures were experienced by the recreational group.
Whether physical injury characteristics produced higher

62
sensations of pain is difficult to answer since only one pain
rating was obtained and injury types varied widely.
It is plausible that the meaning of injury during a
championship football season (e.g., reduced playing time, loss
of status) was more devastating (and painful) for elite
athletes than recreational athletes who identified less
exclusively with the athlete role and had much less to lose.
A trend toward higher total mood disturbance for elites also
supports greater injury-related distress. Table 13, however,
demonstrates that higher pain ratings for elites were not
positively correlated with total mood disturbance (-.16).
Taken together, it appears that pain was more salient for
injured elite football players, but that pain did not
contribute significantly to mood disturbance. The greater
pain reported by elite athletes is consistent with research
showing that negative interpretation of painful stimuli (e.g.,
equating pain with lost opportunity) might have increased pain
and elevated emotional disturbance too (Jensen et al., 1991).
Caution should be exercised in interpreting differences
in pain intensity between the groups since ethnic differences
in pain intensity have been reported (Thomas & Rose, 1991;
Faucett, Gordon, & Levine, 1994). In the current study,
African Americans averaged 1.4 points higher on self-reported
pain intensity. Although this trend is consistent with the
literature showing lower ratings of pain among Caucasian
groups, differences in this study did not reach statistical

63
significance. Another limitation involves use of self-report
measures and retrospective analyses. Future studies could
supplement self-report ratings with physiological or
behavioral indices, external ratings, or multiple stages of
data collection.
Hypothesis 2
The second hypothesis was to examine whether elite
athletes reported greater athletic identity and social support
compared with recreational athletes. Table 8 displays these
results and confirms these hypotheses.
That elite athletes reported significantly higher
athletic identity compared with recreational athletes
(approximately one standard deviation difference) is not
surprising, and this finding adds internal validity to the
design. These groups indeed represent very different levels
of athleticism. Scores on the AIMS for the recreational group
were consistent with those reported for "recreational/fitness"
athletes by Brewer (1993). AIMS scores by the elite group
were approximately one standard deviation below the mean
reported for "intercollegiate/national" athletes by Brewer
(1993). Thus, there is an even more elite level of
professional athletes than the current sample of college
football players that would have scored even higher on
athletic identity.
Finding higher social support among elite athletes was
not surprising given the measure used in this study. College

64
football players will score higher than recreational athletes
as a result of more social support available in the football
program (e.g.., coaches, trainers, physicians). This measure
was selected based on work by Smith, Smoll, and Ptacek (1990)
because it represented the range of potential social support
providers for the elite sample and had good psychometric
properties. A post-hoc examination of social support data,
however, revealed that even when all seven athletically loaded
items were eliminated removed from this measure (e.g., team
trainer/physician, head coach, closest teammate), elite
athletes still scored 24 points higher than recreational
athletes ([t(l,68)=-6.03, p=.000]. While social support
scores reported by recreational athletes were approximately
one standard deviation higher than the high school varsity
athlete norms provided by Smith, Smoll, and Ptacek (1990),
elite athletes scored approximately 3 standard deviations
above this norm. Although both elite and recreational
athletes reported higher than average social support, the
extremely high reports of social support by elite athletes
highlights the very social nature of football. Social support
appears to represent a major benefit associated with
participating in college football. Future studies might
incorporate even more elite samples of injured professional
athletes and assess whether athletic identity and social
support continue to rise with level of participation. The
same degree of perceived social support might not exist at an

65
even more competitive professional level, or in an individual
sport like tennis, but this awaits empirical verification.
Hypothesis 3
The third hypothesis was to evaluate the relative
contributions of the three independent measures in predicting
total mood disturbance among elite athletes. It was
hypothesized that injury severity would best predict total
mood disturbance followed by athletic identity (second) and
social support (third). This hypothesis was rejected, as
social support best predicted total mood disturbance with all
three variables in the model (Table 11). This surprising
finding lends support to the value of perceived social support
involving perceptions of both help and guidance and emotional
support and caring (Smith, Smoll & Ptacek, 1990). Football is
a team sport in which reliance upon others is critical, and
current results underscore this point. Finding that social
support explained greater variance in mood disturbance than
either injury severity or athletic identity might also reflect
the vulnerability of elite football players when social
support is low or absent. Sport psychological interventions
aimed at enhancing social support, especially for targeted
athletes, would seem to be an appropriate means of alleviating
psychological distress and hastening recovery following
injury.
It does not appear that perceived social support is a
correlate of mood disturbance among athletes in general, as

66
the pearson product moment correlation between social support
and total mood disturbance was -.44 for elite athletes, but
only .09 for recreational athletes. Rather, injured elite
football players with low social support might have been much
more susceptible to emotional disturbances following injury,
or injury occurrence during a championship football season
might have led to perceptions of reduced social support.
Assessing social support, challenging negative self¬
statements and mobilizing support resources may be highly
beneficial for injured elite athletes. However, this study
did not examine whether total mood disturbance correlated with
success in physical rehabilitation. This should be addressed
in future studies utilizing a longitudinal design and also
assessing the potential effects of these treatments in
different sports.
Hypothesis 4
The fourth hypothesis was to evaluate the relative
contributions of the three independent measures in predicting
total mood disturbance among recreational athletes. It was
hypothesized that injury severity would best predict total
mood disturbance followed by social support (second) and
athletic identity (third). As hypothesized, injury severity
best predicted total mood disturbance. The order of remaining
variables was reversed with athletic identity (second) and
social support (third), and total explained variance in mood
disturbance was very low (.14). This suggests that predicting

67
to mood disturbance for recreational athletes in various
sports is more difficult than for elite athletes in football.
Injury severity was the best overall predictor of total
mood disturbance for recreational athletes and social support
added almost nothing to the prediction equation. It is
plausible that because recreational athletes are less
experienced social support consumers, unusual attention drawn
to an injury might equate with greater severity for
recreational athletes. This possibility is supported by a .28
pearson product moment correlation between social support and
player rating of injury severity (for elites this same
correlation was -.18).
However, it is questionable whether prediction to mood
disturbance was even addressed in the current analysis, since
total mood disturbance was .03 standard deviations below the
male college student mean. Although 35 of the 40 injured
recreational athletes were participants in team sports, the
importance of team and social influences for these part-time
participants cannot be equated with the level of group and
social commitment on a national championship football team.
Finally, explained variance in total mood disturbance was only
14 percent for recreational athletes (compared to 27 percent
for elite athletes) with all three independent variables in
the model. This low explained variance indicates that future
research is needed to identify other correlates of
maladjustment following injury. Elite athletes appear to

68
share a major advantage in social support compared to
recreational athletes, but the effects of social support for
injured recreational athletes awaits further empirical
investigation. Future studies should utilize a larger sample
size involving more athletes with actual mood disturbance.
Secondary Analyses
Secondary correlational analyses were conducted to
determine the relationship between measures first used in the
current study and total mood disturbance. These appear in
Tables 13 and 14. Mental well being showed the highest
correlation with total mood disturbance for elite athletes
(-.56). This may reflect a greater psychological investment
in remaining free of injury for elites. Self-ratings of
injury severity showed highest correlations with total mood
disturbance (.59) in the recreational sample. Thus, the
degree of recreational athletes' emotional distress increased
with perceptions that their injury was more serious (for
elites, this correlation was only .32). Finally, a
dissociation between elite and recreational athletes occurred
on correlations between mood disturbance and pain (-.16 for
elite athletes, .14 for recreational athletes). This appears
to indicate that self-reported pain is less disturbing
emotionally for elite athletes than for recreational athletes.
Post-hoc Exploratory Analyses
One guestion of the current study is whether it is even
possible to test a model of emotional disturbance when scores

69
on this measure were not in the clinically interpretable
range. Although they may not have been clinically significant
compared to a noninjured population, it should be remembered
that elite athletes are a unique population as demonstrated on
the "iceberg profile" (Figure 4). Thus, mood disturbance for
elite athletes might manifest as normal scores compared with
the super-profiles usually obtained. Although being an elite
athlete appears to carry some emotional advantages as
reflected on the "iceberg profile," negative responses
following injury may suggest that insulation from mood
disturbance is often shortlived. Nevertheless, the question
of subclinical mood disturbance still remains, and certainly
for the even less mood disturbed recreational sample.
To address this issue, two post-hoc exploratory analyses
were conducted to estimate a clinical question of greater mood
disturbance. All subjects were combined to obtain sufficient
power in the analysis. This represents a limitation in the
analysis, as mixed recreational and elite athletes are quite
different from either population alone. Chi square analyses
indicated that only the association between total mood level
and injury severity was significant (Table 15). However,
there is a visible trend in which athletes with greater
athletic identity appeared to be classified into greater mood
disturbance categories (Table 16).
To add further power to the analysis, the second post-hoc
exploratory analysis divided athletes into only the top third

70
and bottom third on mood disturbance. A multiple linear
regression analysis further confirmed the value of knowing
both injury severity and athletic identity in predicting mood
disturbance. These results appear in Table 18. Although
these findings should not be generalized to a true clinical
population, they offer guidance for future research.
Overview and Future Research
The present study can be viewed as a preliminary
investigation into the correlates of postinjury emotional
disturbance following athletic injury. However, the
generalizability of this study is necessarily limited to the
acute phase following injury. Still little is known about how
these responses correlate with actual injury recovery. It
should also be noted that three very different analyses were
conducted with different groups and different findings. One
examined elite football players, a second examined a diverse
recreational group, and a third examined these groups combined
(post-hoc exploratory analyses). Further, most measures in
this study were self-report instruments which may have been
influenced by response biases, especially given the sensitive
nature of asking national champion football players to report
their emotional weaknesses in the training room. Although
medical experts provided injury severity ratings based on
known injury type and expected time to recovery, the
pragmatics of involving two separate data collection sites
made it impossible to use the same rater for each athlete

71
group. Nevertheless, this preliminary study provides a basis
for further investigation and informs clinical practice in
sport injury rehabilitation. Future studies might employ a
longitudinal design to better assess outcome, capture injured
athletes with greater emotional disturbance, and examine the
responses of injured athletes in different sports.
In sum, the present study provides empirical evidence of
differences between elite and recreational athletes, their
mood responses following injury, and factors associated with
these responses. Mood disturbance did not attain clinical
significance compared with a college student norm. For elite
athletes, however, this may underestimate the actual level of
distress they experienced'compared with the usual mood profile
of elite athletes. Compared with recreational athletes, elite
athletes had higher self-reports of athletic identity, social
support, pain, and previous physical conditioning, and lower
perceptions of injury severity. For elite football players,
social support was the greatest predictor of emotional
disturbance (with athletic identity and injury severity
known). For recreational and mixed athletes, injury severity,
followed by athletic identity, were the best predictors.
Thus, an impetus is provided for future research to more
closely examine these and other factors important in emotional
and physical recovery from sport injury.

72
Clinical Implications
More research is needed before widespread conclusions
about the best psychological treatments for sport injury can
be made. Given the paucity of literature in this area and
potentially devastating effects of injury, however, there is
every reason to develop aggressive treatment approaches to
this problem faced by so many individuals. Greater assessment
and treatment protocols should be developed by sports medicine
teams to anticipate and treat the psychological effects of
injury. Heil's (1993) Sports Medicine Injury Checklist is an
example of an excellent tool to identify problems in
adjustment to injury.
Findings from the current study suggest indicate that for
elite athletes, social support should be a major focus. It is
recommended that elite teams evaluate social support long
before injury occurrence and provide special interventions to
those low on this factor. Psychoeducational outreach to
explain the importance of social support in the team setting
is also encouraged. Athlete and coach awareness of the
powerful influence of social support needs to be reinforced.
Following injury, a variety of psychological treatments should
be readily available ranging from imagery and relaxation to
counseling and psychotherapy. For recreational athletes, the
clinical implications from this study are less clear. The
emotional impact of sport injury may be less for these
individuals since they may undergo less identity foreclosure

73
with the athlete role, but this does not eliminate the need
for prompt psychological care in situations that warrant it.
Since emotional disturbance is possible following any injury,
these athletes should be carefully provided with high quality
professional attention to enhance emotional adjustment and
recovery. Some recreational athletes may be more susceptible
to adjustment difficulties than elite athletes due to many
fewer support resources available.

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BIOGRAPHICAL SKETCH
John Francis Murray was born in Fort Lauderdale, Florida,
on November 30, 1961. He is the second of four children of J.
Richard and Joan P. Murray. Raised in south Florida, he
attended Loyola University New Orleans where he majored in
psychology. After graduation in May 1983 with a Bachelor of
Arts degree, John travelled extensively as a professional
tennis coach. He directed, managed and taught tennis at
clubs, hotels and resorts in North America, Hawaii, Europe and
the Middle East. John returned to the academic world in 1991
and obtained a Master of' Exercise and Sport Sciences degree
from the University of Florida (with an emphasis in sport
psychology) in 1992. John entered the doctoral studies
graduate program in the Department of Clinical and Health
Psychology at the University of Florida in 1992. He obtained
a Master of Science degree in clinical psychology in 1995 with
a specialization in medical psychology.
82

I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of Doctor of Philosophy.
¿aJUí-a- "'2) Li
Eileen Fennell, Chair
Professor of Clinical and
Health Psychology
I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, irv'fecope and quality, as
a dissertation for the degree qf Dbc^prJ<5f) Philosophy.
Jlaiies Rodn
;ociate Professor of
/Clinical and Health Psychology
I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of Doctor of Philosophy.
0 w-
Duane Dede
Assistant Professor of
Clinical and Health Psychology
I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of, Doctor of Philosophy.
Samuel Sears
Assistant Professor of
Clinical and Health Psychology

I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree jof Doctor of Philosophy.
Milledge Murphey
Associate Professor of
Exercise and Sport Sciences
This dissertation was submitted to the Graduate Faculty
of the College of Health Professions and to the Graduate
School and was accepted as partial fulfillment of the
requirements for the degree of Doctor of Philosophy.
August, 1998
Dean, College of Health
Professions
Dean, Graduate School