Measuring Exercise Induced Affect in Adults With Brain Injuries

Transcription

1 RESEARCH ADAPTED PHYSICAL ACTIVITY QUARTERLY, 2006, 23, Human Kinetics, Inc. Measuring Exercise Induced Affect in Adults With Brain Injuries Simon Driver University of North Texas The aim of the study was to provide evidence for the validity and reliability of the Physical Activity Affect Scale (PAAS; Lox, Jackson, Tuholski, Wasley, & Treasure, 2000) as a measure of exercise induced affect for adults with brain injuries. The PAAS is a 12-item measure of feeling states based on Russellʼs (1980) conceptualization of affect. A confirmatory factor analysis was conducted on data from 193 participants with brain injuries who completed the PAAS following a single bout of exercise. Results identified four dimensions of affect (positive affect, negative affect, tranquility, and fatigue). Findings provide evidence for the validity and reliability of the PAAS as a measure of exercise induced affect for adults with brain injuries. The importance of measuring physical activity induced affect has increased in recent years (Lox, Jackson, Tuholski, Wasley, & Treasure, 2000), due to the potential impact of affect on adherence and psychosocial well-being (Biddle & Mutrie, 2001; Gauvin & Spence, 1998; Lox, Martin, & Petruzzello, 2003). For example, research has consistently shown affect to be a central determinant and consequence of physical activity behavior across a variety of populations (e.g., clinical, with and without disabilities; McAuley & Blissimer, 2000). Affect can be described as the positive or negative feelings created by a specific stimulus, in this case physical activity (Russell, 1980). Consequently, affect is regarded as an important dependent variable in predicting future physical activity behavior and psychosocial well-being (Lox et al., 2000). Thus, as a greater number of researchers complete studies examining the phenomenon of physical activity induced affect, the need for theoretically driven, valid, and reliable measures has increased. Research into the multidimensional nature of affect was stimulated by Russell (1980) who introduced a circumplex model of affect, which consisted of two bipolar axes reflecting affective valence (pleasure-displeasure) and activation (arousalsleepiness; see Figure 1). These two dimensions of affect formed quadrants based on the different combinations of valence and activation. Consequently, these axes provided two important pieces of information regarding future theory and research related to affect (Lox et al., 2000). First, affective reactions range on a continuum from positive to negative hedonic tone (valence). Second, affective reactions lie on a continuum from low to high activation. Subsequent empirical research supported the bipolar axes of affect (Ekkekakis & Petruzzello, 2002; Russell, 1980), Simon Driver is now with the Dept. of Kinesiology, Health Promotion, and Recreation at the University of North Texas, Denton. sdriver@coe.unt.edu. 1 01Driver(1) 1 12/11/05, 9:13:08 AM

2 2 Driver Figure 1 Russellʼs (1980) Conceptualization of Affect distinguishing between the four primary quadrants of affect. Enjoyment as an affective reaction falls within the high activation and positive hedonic tone quadrant whereas anger is in the high activation and negative hedonic tone quadrant. In contrast, tired falls within the low activation and negative hedonic tone quadrant whereas calm is within the low activation and positive hedonic tone quadrant. Since Russellʼs (1980) conceptualization of affect, several exercise specific measures of affect have been developed including the Exercise-Induced Feeling Inventory (EFI; Gauvin & Rejeski, 1993) and the Subjective Exercise Experience Scale (SEES; McAuley & Courneya, 1994). These measures have been used to assess an individualʼs level of affect in response to exercise participation. However, based on Russellʼs conceptualization of affect, both questionnaires fail to include questions that represent the four quadrants of affect. For example, the EFI does not contain a subscale that measures high activation and negative hedonic tone and the SEES fails to measure low activation and positive hedonic tone. Consequently, researchers were forced to utilize both measures to accurately assess each quadrant of Russellʼs (1980) multidimensional model. Due to the restrictions imposed on researchers by using both questionnaires (e.g., time, resources), Lox et al. (2000) created the Physical Activity Affect Scale (PAAS). The PAAS is an encompassing measurement tool, based on Russellʼs circumplex model, which merged the EFI and SEES to assess each of the affective quadrants. Lox et al. labeled the quadrants positive affect (high activation, positive tone), negative affect (high activation, negative tone), tranquility (low activation, positive tone), and fatigue (low activation, negative tone). Subscales were then created to represent the quadrants, each consisting of questions that captured the four different affective states. For example, positive affect was captured with the descriptors enthusiastic, energetic, and upbeat; negative affect (crummy, awful, discouraged); tranquility (calm, peaceful, relaxed); and fatigue (fatigued, tired, worn-out). The final instrument consisted of four subscales, each with three items, for a total of 12 items. Responses to each item are recorded on a Likert type scale ranging from 0 (do not feel) to 4 (feel very strongly). Scores for each item within a subscale are then added together resulting in a total score. The total score is then divided by three (the number of items 01Driver(1) 2 12/11/05, 9:13:11 AM

3 Affect and Brain Injuries 3 within the subscale), and a mean score for each subscale is obtained. Higher scores represent stronger affective states. Exploratory and confirmatory factor analyses were then completed with a sample of 489 college students (M age = 22.86, SD = 5.24). Participants completed the PAAS immediately following a single bout of physical activity (e.g., walking, jogging, aerobic dance, weight training). Results identified four latent variables that represented the subscales of the PAAS, including positive and negative affect, tranquility, and fatigue. Results supported the fit of the data to the proposed model with significant factor loadings for each item (ranging from.61.92) and acceptable fit indices (e.g., GFI =.90, CFI =.92; see Lox et al., 2000 for more details). These findings provided evidence of the validity of the PAAS among college aged individuals. Subsequently, several studies have utilized the PAAS to examine acute affective responses to physical activity participation (Lox, Tucker, & Jackson, 2000; Lox, Tuholski, Jackson, & Woodford, 2004). For example, Lox, Tucker, and Jackson (2000) found that Tai Chi had a positive impact on the acute affective states of adults with HIV-1. Results indicated significant decreases in negative affect and fatigue from pre to post acute exercise, while significant increases in positive affect and tranquility were also reported. The study highlights the important acute affective benefits that may result from participation in a physical activity program in a clinical population. When individuals are regularly exposed to a stimulus that has a positive influence on affect, it can lead to chronic changes in affect (Biddle & Mutrie, 2001; Lox et al., 2003). For example, if individuals are enrolled in an exercise program where they frequently experience acute affective benefits, then over a period of time, this will lead to changes in their chronic affective states. Changes can be explained by social-cognitive theory (Bandura, 1986), which predicts that enhanced self-efficacy (belief in ability) can potentially influence an individualʼs affective states and cognitions. If individuals successfully participate in an exercise program, then their self-efficacy may increase (e.g., ability to use treadmill, lift weights), resulting in greater feelings of enjoyment and decreased anxiety. Thus, increases in self-efficacy act as an antecedent to enhanced affective states (Driver, OʼConnor, Lox, & Rees, 2003). In light of previous research examining the relationship between exercise and affect, an enhanced understanding of the affective benefits that can result from physical activity participation is especially important to specialists working with people who have brain injuries. This is largely due to the severe negative psychosocial reaction experienced after the injury (Armstrong, 1991; Driver & OʼConnor, 2003). Research has shown that individuals with brain injuries experience decreases in self-esteem (Baker-Roth, McLaughlin, Weitzenkamp, & Womeldorff, 1995), social contacts (Potter, Smith, & Finegan, 1994), as well as an increased disturbance of affective states (e.g., depression, anxiety, fear, stress, aggression; Armstrong, 1991). Considering the myriad of negative psychosocial outcomes that can result from brain injuries, an improved understanding of the potential positive affective changes that can accompany physical activity participation would be highly beneficial to rehabilitation specialists. For example, if research consistently shows that physical activity participation has a positive influence on the affect of people post injury, then specialists may begin prescribing exercise as a mode of rehabilitation. Activities that facilitate positive affective 01Driver(1) 3 12/11/05, 9:13:12 AM

4 4 Driver states are especially beneficial due to the recognition that psychosocial impairments are the most challenging for specialists to positively influence after brain injuries (Kwan & Sulzberger, 1995). The challenge provided to rehabilitation specialists is exacerbated by the high incidence of traumatic brain injuries. Brain injury is the leading cause of death and disability in industrialized countries (Ylvisaker & Feeney, 1999), with an estimated 80, ,000 people experiencing permanent disability every year within the United States (National Institute of Health, 1998). Consequently, the high incidence ensures that this acquired disability is becoming a serious public health issue (National Institute of Health, 1998). The size of the population also highlights the importance of conducting research into successful modes of rehabilitation (Driver et al., 2003). Several studies have been completed examining the influence of exercise as an intervention on the affective experiences of people who have brain injuries (Driver et al., 2003; Driver & OʼConnor, 2003). For example, Driver and OʼConnor (2003) utilized the PAAS to examine changes in affect among 16 individuals with brain injuries in response to an eight-week aquatic exercise and resistance training program. Results indicated significant increases in acute and chronic positive affect and tranquility after completion of the program and significant decreases in both acute and chronic negative affect and fatigue. These results have implications for specialists involved in the rehabilitation of people with brain injuries, as physical activity may positively influence the acute and chronic negative affective reaction experienced post injury. Consequently, physical activity may play an important role in the rehabilitation of people with brain injuries. However, the Driver et al. study did not provide evidence of the validity and reliability of the PAAS for the sample of people with brain injuries. When using a questionnaire that was originally designed and validated with a particular population (e.g., college age students, older adults), it is important to recognize that the questionnaire may no longer be a valid measure when used with another population (e.g., people with brain injuries; Yun & Ulrich, 2002). Thus, researchers who use a measure with a population that is different from the original sample, without understanding the possible impact on the validity of results, may misinterpret scores (Yun & Ulrich, 2002). This is a concern as specialists sometimes administer instruments (e.g., questionnaires, surveys, standardized assessment tools) to individuals who are different to the population that the instrument was originally created (Yun & Ulrich, 2002). Therefore, when using measures that were created with a population (e.g., college students) that is different from the current sample (e.g., people with brain injuries), it is important to provide sound evidence of validity and reliability. This is especially applicable to people with brain injuries who may experience cognitive impairments post injury that could impact their ability to successfully read, interpret, and answer items of the PAAS. Also, due to the changes in affect experienced post injury, individuals may have a different view about affective states such as enjoyment or tranquility. Thus, it is important to determine whether the PAAS is an appropriate measure of affect for people with brain injuries, as valid and reliable measures of affect are essential if quality research is to be completed examining the impact of exercise on affect. Consequently, the aim of this current study was to provide further evidence of validity (e.g., face, factorial) and reliability (e.g., test-retest, internal consistency) of the PAAS among people with brain injuries. 01Driver(1) 4 12/11/05, 9:13:14 AM

5 Participants Method Affect and Brain Injuries 5 Participants were selected from one rehabilitation center using purposive sampling and specific criteria. First, participants had to be adults with traumatic brain injuries (e.g., aged > 18 years old). Second, individuals had to be outpatients at a rehabilitation center. This was to ensure that the sample was representative of people living in the community, as opposed to living in acute care where physical activity programs may be mandatory as part of their multidisciplinary rehabilitation program. Third, participants all had to function at Level VII (automatic - appropriate) or VIII (purposeful - appropriate) on the Ranchos Los Amigos Scale of Cognitive Functioning (Hagan, Malkmus, & Durham, 1979). Individuals functioning at these levels are able to recall activities independently, can problem solve, and do not rely on cueing for direction (Hagan et al., 1979). This criterion ensured that individuals could understand the instructions and questions for the instruments, as well as be able to recall relevant information. Fourth, individuals must have experienced the brain injury at least one year prior to the meeting. This criterion was included due to psychosocial disturbances that individuals typically experience immediately post injury. During this period individuals experience increased disturbances in affective states such as depression (Levin, Goldstein, & MacKenzie, 1997; Oddy, Humphrey, & Uttley, 1978), fear, anger, and despair (Armstrong, 1991; Brown & Vandergoot, 1998). This disruption in affect stabilizes within one year of injury as individuals adjust to the injury and their new abilities (Armstrong, 1991). These criteria were selected in an attempt to make the sample as homogenous as possible. Participants were informed about the selection criteria by the researcher at the physical activity sessions before enrollment in the study began. The final sample consisted of 193 adults with brain injuries, and included 105 male and 88 female participants (M age = years, SD = 4.71). Time since injury ranged from 13 to 61 months (M = 38.69, SD = 15.28). Participants were all enrolled in a physical activity program offered at the rehabilitation center. Sessions were provided by the center on multiple occasions throughout the week and individuals attended an activity class of their choice at their convenience. Classes each lasted 45 minutes and a variety of different programs were available including aquatics, resistance training, climbing, and walking. All activities were completed in a group environment and were instructed by rehabilitation center recreation, physical, or aquatic therapists. The primary aim of these sessions was to provide individuals with the opportunity to be physically active and learn lifetime fitness skills. Procedure Approval from the Institutional Review Board was received to complete the research study. The researcher then contacted the recreational therapy personnel at a local rehabilitation center and informed personnel about the protocol required for the study. The therapy personnel were also informed that the testing was noninvasive and would not disrupt the normal functioning of the center or the physical activity programs. After the center had granted permission, the researcher scheduled a time to attend physical activity sessions. At each session, individuals were informed about the nature of the study, what would be required of them (e.g., completing 01Driver(1) 5 12/11/05, 9:13:16 AM

6 6 Driver questionnaires), and the selection criteria (see Participants section above). People were then invited to participate. If individuals met the selection criteria and were willing to participate, they were included in the study and completed an informed consent form. These participants then engaged in their workout as usual and met with the researcher immediately following completion of the session at the side of the pool or in the gymnasium. The researcher was not involved in the instruction/ supervision of the physical activity sessions but was present at the end of sessions to administer the questionnaire. During this time, the instructions were read to the participants as a group. Then at that exact moment in time, respondents were asked to indicate the extent to which the questions described how they felt. Individuals then completed the questionnaire, which took less than one minute to complete. The researcher was available to all participants while they were completing the questionnaires. If at any time participants wanted to discontinue participation they were free to leave. The researcher then collected completed questionnaires and participants went to the changing rooms or left the facility. Measures The PAAS (Lox et al., 2000) was utilized to measure the participantsʼ exercise induced affect. Lox et al. tested the psychometric properties of the PAAS and provided evidence of factorial and criterion related validity of the PAAS for college aged students. The original questionnaire includes four subscales with three questions per subscale (total of 12 questions). The questions assess each quadrant of Russellʼs (1980) multidimensional model of affect including positive and negative affect, tranquility, and fatigue. Data Analysis Two different steps were taken to provide evidence for the validity of the PAAS. First, to increase the face validity of the questionnaire, the measure was administered to 10 experts from different areas of specialization including adapted physical education, exercise psychology, and rehabilitation (e.g., recreation therapists). Experts critiqued the measure based on wording, relevance, and accuracy. Changes were then made to the original instrument based on several criteria. For example, alterations were made if more than 50% of the experts indicated the same change (a majority). However, if one expert provided a strong rationale for a specific change (e.g., font size, wording based on level of cognitive functioning) that the other experts did not suggest, then the measure was also altered and resubmitted to the other experts for approval. Once all the changes had been made, the revised measure was again returned to the 10 experts for reevaluation. These steps were completed in an attempt to increase the specificity of the measure for people with brain injuries. Secondly, the instruments were given to five people with brain injuries. Once again, individuals assessed the instructions and wording to ensure clarity and readability. Measures were again modified if 50% of participants identified the same change. The final measure deviated from the Lox et al. (2000) original measure as the font size was increased from 10 to 12 and the instructions were changed from describe how you feel at this moment in time to describe how you feel right now (immediately). 01Driver(1) 6 12/11/05, 9:13:17 AM

7 Affect and Brain Injuries 7 A confirmatory factor analysis was also computed to provide evidence for the factorial validity of the PAAS. The statistical program LISREL 8.54 (Joreskog & Sorbom, 2002) was used to test whether the data for adults with brain injuries sufficiently fit the covariance structure generated by Lox et al. (2000). A maximum likelihood confirmatory factor analysis was performed on the covariance matrix to estimate the fit of the data to the PAAS model. Two different procedures were employed to provide evidence for the reliability of the PAAS. First, the internal consistency of the PAAS subscales were calculated using cronbach alpha (Cronbach, 1951), which determines the extent to which a construct (e.g., positive affect) accounts for the covariance among all items (e.g., energetic, upbeat, enthusiastic; Cronbach, 1951). When items are a function of the same construct, then the measure can be considered internally consistent (Zimmerman, Zumbo, & Lalonde, 1993). Alpha coefficients greater than.70 are considered acceptable (Cronbach, 1951). Second, the test-retest reliability of the PAAS was assessed to provide evidence of the stability of the measure over time. This involved administering the PAAS to a sample of 50 participants, who were included within the original sample of 193, on a separate occasion after a single bout of exercise. The same protocol was utilized when administering the questionnaires as outlined within the Procedure section. Pearson product moment correlations were computed to determine the stability of the measure. The Pearson r equation focuses on the within subject variance (Howell, 2001; Tabachnick & Fidell, 2001) and is of primary interest when examining scores on the same measure on two different occasions. The significance of the correlation was also calculated to establish whether or not scores at time one and time two were significantly correlated. Test-retest correlations greater than.50 in the social sciences are often considered acceptable due to the situational and environmental changes associated with perceptions, judgments, or affective feelings over time (Neuman, 1997). Results The data were initially screened for outliers or missing values. No data were removed or missing from the sample. Subsequent analyses revealed that the data met the assumption of normality as the values for skewness and kurtosis were not significantly different from zero (p <.01). Values of skewness ranged from.34 to.71 (M =.17), and kurtosis from.64 to.37 (M =.21). The correlation matrix, means, and standard deviations for the sample are included in Table 1. The fit of the data to the conceptual model was assessed using a chi-squared test, which involved comparing the underlying covariance structure generated by LISREL estimates with the covariance structure of the empirical data. The chisquare value reported from the analysis χ 2 (48) = 62.3, p =.08 indicates that there is not a statistical difference between the two covariance matrices. Therefore, the null hypothesis can be accepted because there is no difference between the conceptual model for the PAAS and the data collected for people with brain injuries. As the chi square value can be influenced by sample size (Bollen & Long, 1994), however, several other fit indices were also utilized to assess the fit of the model (Joreskog & Sorbom, 2002). The Comparative Fit Index (CFI) is recognized as one of the most appropriate measures of fit because it is unaffected by sample size and estimates the relative difference in noncentrality of interest (Loehlin, 1998). 01Driver(1) 7 12/11/05, 9:13:19 AM

8 8 Driver Table 1 Correlation Matrix, Means, and Standard Deviations for Each PAAS Item Variable Correlation Matrix M (SD) Awful (0.68) Calm (0.96) Fatigued (1.13) Enthusiastic (1.08) Crummy (0.64) Relaxed (0.97) Tired (0.91) Energetic (1.06) Discouraged (0.51) Peaceful (1.09) Worn out (1.06) Upbeat (1.07) Fit Indices: GFI =.95, AGFI =.92, NFI =.96, NNFI =.99, CFI =.99, RMSEA = Driver(1) 8 12/11/05, 9:13:21 AM

9 Affect and Brain Injuries 9 Other descriptive fit indices calculated include the Goodness of Fit Index (GFI), which represents the amount of covariance accounted for by the model relative to the total covariance and the Normed Fit Index (NFI), which quantifies the improvement of the hypothesized model over the null baseline model. The Adjusted GFI and Non NFI were also calculated, which is recommended as both indices make an adjustment based on the ratio of degrees of freedom between the model being fitted and the baseline model (Loehlin, 1998). Values for the various fit indices range between 0-1 with values less than.90 being considered unacceptable (Loehlin, 1998) and suggesting a poor model fit. Steigerʼs (1990) Root Mean Square Error of Approximation (RMSEA) was also calculated to assess the badness of the model fit. The RMSEA is the square root of the ratio of the rescaled noncentrality index to the models degrees of freedom (Loehlin, 1998). Values of.05 or less indicate a close model fit,.08 a reasonable fit, and greater than.10 an unacceptable fit. Results from the different fit indices were all acceptable, reinforcing the fit of the data for people with brain injuries to the conceptual model (see Table 1). Other pertinent results include coefficients for the factor loadings, representing the relationship between each questionnaire item and the subscale it is intended to measure (see Figure 2 for the standardized values). The values for the factor loadings (.77 to.91, median =.83) were all significant (p <.001), indicating that the questionnaire items significantly relate to the appropriate latent variables. For example, the items enthusiastic (.91), upbeat (.78), and energetic (.80) are each significantly related to the positive affect latent variable. Further evidence of factorial validity is provided by values of the squared multiple correlation (SMC), representing the amount of variance explained by the model in each of the observed variables. The SMCs ranged between.59 and.83 (median =.71). As standardized estimates are reported, the variance for each of the questionnaire items will equal 1.0. Thus, by squaring the factor loading and adding this to the residual value, one will obtain the total variance, which will equal 1.0. For example, about 83% of the variance for the latent variable positive affect is accounted for by the questionnaire item enthusiastic (e.g., R 2 =.912 =.83 or 83%). However, it is important to note that some of the variance is a result of the fact that positive affect is correlated with the other subscales (see Figure 2). The remaining 17% of the variance for the enthusiastic item can be due to error (e.g., random errors and unique error to the indicator). As another example, consider the fatigue latent variable, which accounts for 59% of the variance in the item worn-out, with the remaining 41% resulting from error variance. The correlations between the latent variables are also outlined in Figure 2. Results indicate negative correlations between positive and negative affect (.35) and fatigue and tranquility (.28). This result adds support to the theoretical basis of the PAAS because the negative correlations suggest that the latent variables fall at opposite ends of the hedonic tone continuum proposed by Russell (1980). Positive correlations between positive affect and tranquility (.39) and negative affect and fatigue (.18) were also found. The correlations were expected as these latent variables have the same level of hedonic tone (e.g., positive affect and tranquility = positive hedonic tone) but different levels of activation (e.g., positive affect = high activation versus tranquility = low activation). High internal consistency values were reported for each subscale of the PAAS including positive affect (.94), negative affect (.86), fatigue (.91), and tranquility (.84). Results indicate that each construct within the PAAS accounts for significant 01Driver(1) 9 12/11/05, 9:13:22 AM

10 10 Driver Figure 2 Confirmatory Factor Analytic Model and standardized estimates for the PAAS for adults with brain injuries. amounts of covariance among all items. The Pearson r correlations were significant (p <.001) for each subscale including positive affect (.71), tranquility (.82), negative affect (.76), and fatigue (.85). Findings indicate a suitable level of stability of the subscales overtime. Discussion Based on the previous work of Lox et al. (2000), the purpose of this study was to provide evidence of the validity and reliability of the PAAS as a measure of exercise induced affect for adults with brain injuries. Results provide evidence 01Driver(1) 10 12/11/05, 9:13:24 AM

11 Affect and Brain Injuries 11 of the face and factorial validity of the measure as well as internal and test-retest reliability. The factor structure of the PAAS is also theoretically supported by the quadrants of Russellʼs (1980) circumplex model of affect. For example, negative affect items (e.g., crummy, discouraged, awful) would fall within the negative tone, high activation quadrant, whereas positive affect items (e.g., enthusiastic, upbeat, energetic) would fall within the positive tone, high activation quadrant. Similarly, fatigue items (e.g., fatigued, tired, worn-out) would fall within the negative tone, low activation quadrant and tranquility items (e.g., relaxed, peaceful, calm) would fall within the positive tone, low activation quadrant. These results are supported by the correlations between the latent variables. Results have useful implications for individuals involved in the rehabilitation and education of people with brain injuries as findings provide evidence of the appropriateness of the PAAS as a measure of exercise induced affect. If exercise is to be included within an individualʼs multidisciplinary rehabilitation and/or education program, then it is important that researchers collect evidence of the positive affective changes that can result from participation. Documented evidence of the affective benefits that can accompany exercise participation will increase the likelihood that specialists prescribe physical activity as a means of overcoming the negative affective reaction experienced post injury. Future research should focus on administering the PAAS to adults with brain injuries before and after participation in physical activity. For example, studies could involve administering the PAAS pre and post physical activity session to assess changes in affective states. Multiple assessments of affect could then be recorded by taking measurements before and after each activity session (e.g., three times a week) over the course of an entire training program (e.g., 12-week program). This would allow researchers to graph scores and then determine whether physical activity has had an impact on acute (e.g., immediately afterwards) and chronic (e.g., long term) affective states of adults with brain injuries. Since there is a negative disturbance in affect post injury (e.g., increased depression, anxiety, fear; Armstrong, 1991), it is important to collect evidence from different activity modes that have a positive influence on rehabilitation. One benefit of the PAAS includes its brevity, which will allow for multiple assessments of affect (e.g., pre, during, and post participation) without disrupting the activity session. However, the PAAS still needs to undergo continued validation work. For example, future research could focus on the factor structure of the PAAS and whether it varies under different physical activity conditions such as intensity (e.g., high vs. low), duration (e.g., 40 minutes vs. 15 minutes), or mode of exercise (e.g., familiar vs. unfamiliar activity). Other participant characteristics may also influence results such as gender, age, fitness level (Lox et al., 2000), level of cognitive functioning (e.g., < Level VI), time since injury, and severity of injury. For example, an individual who is six months post injury or has a severe physical disability may have different levels of affect than an individual who is five years post injury and has a mild physical disability. Establishing the validity and reliability of the PAAS for adolescents is also an important future research study due to the high incidence of brain injury for this age group (Bigge, Best, & Heller, 2001; Hill, 1999). However, it is critical to recognize the importance of completing similar validity and reliability analyses for this and any other instruments used for individuals with disabilities (Yun & Ulrich, 2002). Using this level of analysis for 01Driver(1) 11 12/11/05, 9:13:25 AM

12 12 Driver determining the appropriateness of other physical activity instruments will increase the likelihood that researchers complete accurate and meaningful studies. In conclusion, if investigators are to conduct research that is valid and reliable, then it is essential that the appropriate measures are utilized. Results from the current study provide evidence of the validity and reliability of the PAAS for adults with brain injuries, although future efforts should continue to examine the properties of the measure due to the considerable variability in functioning in people with brain injuries. References Armstrong, C. (1991). Emotional change following brain injury: Psychological and neurological components of depression, denial, and anxiety. Journal of Rehabilitation, 17, Baker-Roth, S., McLaughlin, E., Weitzenkamp, D., & Womeldorff, L. (1995). The impact of a therapeutic recreation community liaison on successful re-integration of individuals with traumatic brain injury. Therapeutic Recreation Journal, 25, Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall. Biddle, S.J.H., & Mutrie, N. (2001). Psychology of physical activity: determinants, wellbeing and interventions. London: Routledge. Bigge, J.L., Best, S.J., & Heller, K.W. (2001). Teaching individuals with physical, health, or multiple disabilities. Upper Saddle River, NJ: Merrill/Prentice Hall. Bollen, K.A. & Long, J.S. (1994). Testing structural equation models. London: Sage. Brown, M., & Vandergoot, D. (1998). Quality of life for individuals with traumatic brain injury: Comparison with others living in the community. Journal of Head Trauma Rehabilitiation, 13, Cronbach, L.J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297, 334. Driver, S., & OʼConnor, J. (2003). Exercise participation, self-esteem, and affective experiences of people with a brain injury. Journal of Cognitive Rehabilitation, 21(4), Driver, S., OʼConnor, J., Lox., C.J., & Rees, K. (2003). The effect of aquatic exercise on psychosocial experiences of individuals with brain injuries. Journal of Cognitive Rehabilitation, 21(1), Ekkekakis, P. & Petruzzello, S.J. (2002). Analysis of the affect measurement conundrum in exercise psychology. IV. A conceptual case for the affect circumplex. Psychology of Sport and Exercise, 3, Gauvin, L. & Rejeski, W.J. (1993). The exercise induced feeling inventory: Development and initial validation. Journal of Sport and Exercise Psychology, 15, Gauvin, L., & Spence, J.C. (1998). Measurement of exercise-induced changes in feeling states, affect, mood, and emotions. In J.L. Duda (Ed.), Advances in sport and exercise psychology measurement (pp ). Morgantown, WV: Fitness Information Technology. Hagan, C., Malkmus, D., & Durham, P. (1979). Rehabilitation of the head injured adult: Comprehensive physical management. Downey, CA: Professional Staff Association of Rancho Los Amigos Hospital, Inc. Hill, J.L. (1999). Meeting the needs of students with special physical and health needs. Upper Saddle River, NJ: Prentice Hall/Merrill. Howell, D.C. (2001). Statistical methods in psychology (5th ed.). Pacific Grove, CA; Duxbury. Joreskog, K., & Sorbom, D. (2002). LISREL 8: Structural equation modeling with the SIM- PLIS command language. Hillsdale, NJ: Lawrence Erlbaum Associates. 01Driver(1) 12 12/11/05, 9:13:27 AM

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