Regular exercise adoption: psychosocial factors influencing college students.

Womble, Melissa N. ; Labbe, Elise E. ; Shelley-Tremblay, John F. 等

Engaging in a regular physical activity routine is more important considering the well-documented benefits and rising obesity rates (Center for Disease Control [CDC], 2007). The CDC reported that 59.0% of males and females (aged 18 to 24) engaged in regular physical activity (i.e., moderate intensity activities for at least 30 minutes per day or vigorous-intensity activities for at least 20 minutes three days per week), 31.9% engaged in insufficient amounts of physical activity (i.e., less than the recommended level of activity, but more than 10 minutes total per week of moderate or vigorous-intensity activities), 9.1% were considered inactive (i.e., less than 10 minutes total per week of moderate or vigorous-intensity activities), and 18.4% engaged in no leisure-time physical activity (i.e., no reported physical activity or exercise in the previous month) (CDC, 2010). These low prevalence rates suggest a need to understand how to increase the number of college-aged individuals who adopt and adhere to a regular exercise routine.

The Transtheoretical Model (TTM) of Stages of Behavior Change has been used to understand the five stages individuals progress through in adopting a variety of health behaviors (Prochaska, Velicer, Rossi, Goldstein, Marcus, Rakowski, et al., 1994; Prochaska, 1994; Marcus & Simkin, 1993). In precontemplation, individuals have not performed the new behavior as they are not aware of the need to do so. In contemplation, individuals have not performed the new behavior, however, they are thinking about starting the new behavior in the future (next six months). In preparation, individuals are prepared to change within a short period of time (e.g., one month). In action, individuals have engaged in the new behavior for less than six months and have a high chance of regressing to previous stages. In maintenance, the individual has engaged regularly in the activity for a long time (e.g., over six months). The maintenance stage is different than the action stage because the individual has more confidence to resist temptation and they have a lower probability of regressing to previous stages (Prochaska et al., 1994). According to Prochaska et al. (1994), individuals move through these stages at different rates and may regress to earlier stages at any point.

Researchers have studied the application of the TTM to exercise. Marcus and Simkin (1993) found that participants in higher stages of change reported participating in significantly more minutes of vigorous activity during the last week than participants in the lower stages of change. Cardinal (1995) found significant differences between the five stages of change according to exercise level, physical activity level, and V[O.sub.2peak] ml/kg/min. TTM research has noted the importance of identifying predicting factors that will help determine who is likely or unlikely to adopt and adhere to a particular routine (Prochaska, 2008). The predicting factors of self-efficacy and decisional balance have been found to correlate with level of exercise stage of change on the TTM (Prochaska, 2008; Marcus, Selby, Niaura, & Rossi, 1992). By identifying these predicting factors and others in a college-aged sample, intervention programs can be developed to target these factors and help college-aged individuals learn strategies to adopt and adhere to a regular exercise routine (Prochaska, 2008).

Physical Self-Efficacy, Decisional Balance, Mindfulness, Resilience, BMI and V02max as Predictors of Stage of Exercise Adoption (SOEA)

In this study, we examined decisional balance, self-efficacy, mindfulness, resilience, BMI and V[O.sub.2]max as predictors of SOEA in a college-aged sample. BMI and VO2max were used as predictor variables in order to corroborate self-reported SOEA.

To make a decision to change behavior, individuals weigh the positive aspects (pros) against the negative aspects (cons) (Prochaska & Velicer, 1997). The difference is referred to as decisional balance. Based on the TTM, to move from the precontemplation and contemplation stages to the action and maintenance stages, the pros must increase and the cons must decrease (Marcus, Rakowski et al., 1992; Prochaska et al., 1994). At a specific stage, usually near the preparation stage, the increasing pros and decreasing cons levels meet. This crossing indicates that the individual is ready to change (Prochaska, 2008). To promote exercise behavior, interventions need to increase participants' pros associated with the new behavior in order to prepare them to move beyond the precontemplation stage. In addition, helping participants decrease their cons associated with the new behavior, when they are in the contemplation or preparation stage, can help them move towards the action and maintenance stages (Prochaska, 2008; Marcus, Rakowski et al., 1992). Research shows that individuals in higher stages of change in adopting exercise behavior will have higher scores on pros and lower scores on cons for adopting the exercise behavior relative to those in the lower stages. For example, Prochaska et al. (1994) found that the cons outweighed the pros in the precontemplation stage for 12 different problem behaviors and the pros outweighed the cons in the action stage for 11 of the 12 problem behaviors.

Self-efficacy was developed by Bandura as part of the Social Learning Theory (Bandura, 1977) and is also one of the predicting factors of the TTM. Self-efficacy represents how much people believe in their ability to do something, or change their behavior. Research shows that individuals with high self-efficacy levels are more likely to be in higher stages of change in various health behaviors (Marcus, Selby et al., 1992; Prochaska & Velicer, 1997). Perceived self-efficacy is unique to each behavior because the difficulties and temptations are different for different behaviors. For example, there are different difficulties and temptations in adopting a physical activity routine compared to beginning a healthy eating routine. Self-efficacy specific to exercise has been widely researched. Ryckman, Robbins, Thornton, & Cantrell (1982) found that subjects with stronger perceived physical ability reported greater participation and involvement in sports, higher levels of self-esteem, lower levels of social anxiety, and better performance on a dart-throwing task and on a motor-coordination task than those who rated their physical ability as poor. Gayton, Matthews, & Burchstead (1986) found physical self-efficacy scores and perceived physical ability to be significantly correlated with predicted and actual marathon finishing times. Results specifically showed the relationship between general physical self-efficacy and marathon-running performance to be mediated by perceived physical ability. McAuley and Gill (1983) found that self-efficacy levels were related to performance in gymnastics. Regarding the TTM, Marcus, Selby, Niaura, & Rossi (1992) found participants in the precontemplation stage to have lower self-efficacy scores than participants in the maintenance stage. Callaghan, Eves, Norman, Chang, & Cheung Yuk Lung (2002) researched the TTM in Chinese undergraduate students and found significant differences between the stages for self-efficacy, pros of adopting the exercise behavior, and frequency of exercise behavior.

Mindfulness is a meditation practice and health behavior that has been shown to reduce a number of psychological and physical symptoms (e.g., anxiety, depression, eating disorders) (Miller et al., 1995; Teasdale, Segal, Williams, Ridgeway, Soulsby, & Lau, 2000). Specifically, mindfulness is a way of directing attention, so you can focus on what you are experiencing (feelings, sensations, thoughts) in the present with a nonjudgmental attitude (Baer, 2006). The basic idea in practicing mindfulness is to keep oneself in the present moment by only responding (i.e., thoughtful) to feelings, sensations, and thoughts without reacting (i.e., impulsive) to them (Kabat-Zinn, 1990). Based on mindfulness, to find relief from suffering, individuals must realize that all events, including thoughts, feelings, sensations emotions, and consciousness, are not permanent (Kaplan, Goldenbergy, & Galvin-Nadeau, 1993). By responding without reacting, one is able to avoid suffering mainly because when you hold onto thoughts as enduring causes (by expressing judgmental attitudes) the result is suffering (Kaplan et al., 1993). With the success of mindfulness interventions in healthcare, mindfulness techniques are now being applied to the area of health and wellness (Ludwig & Kabat-Zinn, 2008). Some believe that mindfulness based weight loss and exercise programs may be the key to solve the problems of obesity and physical inactivity. Singh, Lancioni, Winton, Wahler, Singh, & Sage (2008) developed a mindfulness-based health wellness program for managing morbid obesity. The program helped one adult, who had suffered from weight problems since adolescence, lose 144 pounds and learn to engage in healthy behaviors such as physical exercise and eating healthy foods. During a 12-month follow-up period he had maintained his weight and healthy behaviors. Gardner and Moore (2004) developed a Mindfulness-Acceptance-Commitment-Based Approach to Athletic Performance Enhancement that was successful in enhancing underperformance for two competitive athletes.

Resilience refers to the personal qualities that allow an individual to overcome adversity. Resilience is a construct that varies according to age, gender, cultural origin, and life experiences (Connor & Davidson, 2003). With the rising health costs, researchers and practitioners are proposing that resilient functioning should be taught in order to hopefully teach individuals protective factors that will help individuals cope through illness, stress, etc (Sills, Cohan, & Stein, 2006). Connor, Davidson, and Lee (2003) looked at the relationship between spirituality, resilience, anger and health status, and posttraumatic symptom severity in trauma survivors and found resilience to be associated with both health status and posttraumatic symptom severity. Connor and Davidson (2003) found that a greater improvement in resilience level corresponded to higher levels of global improvement for two groups of patients with PTSD undergoing treatment. Chan, Julian, Lai, and Wong (2006) found that Chinese patients with coronary heart disease who were high in resilience achieved better outcomes during an 8-week rehabilitation program than those lower in resilience. Although these studies relate to recovery from illness, the findings from each of these studies demonstrate that resilience is an important factor for recovery and better health. This relates to the current study in that regular physical activity also is an important factor for better health. Although researchers have suggested that resilience could be an important factor in health and wellness, few studies have looked at physical activity related to resilience. Salmon (2000) suggested that physical activity may be similar to resilience (overcoming adversity) in that during physical activity you may be overcoming stress. Many studies have found that physical activity can lead to stress reduction. This suggests that both physical activity and resilience may help an individual overcome negative experiences (stress or adversity). Therefore, level of resilience may be an important predicting factor and strong determinant of which stage of change of physical activity an individual will obtain.

With much of the research on the TTM, the psychosocial factors of self-efficacy and decisional balance have been investigated. The current study attempted to validate these findings in a college aged sample and also determine if other psychosocial variables (i.e., mindfulness and resilience) which have not been previously investigated as predictor variables are important in predicting who is likely or unlikely to adhere to a regular exercise routine. On the basis of the literature, we made the following six hypotheses: Participants in higher stages of change in adopting regular exercise behavior will have 1) higher scores on pros and lower scores on cons for adopting the regular exercise behavior, 2) higher physical self-efficacy scores, 3) greater mindfulness levels, 4) greater resilience scores, 5) a higher V[O.sub.2]max during the Rockport One-Mile Walking Test, and 6) a lower BMI than those in lower stages of change.

Method

Participants

Participants were 152 college students recruited from a university in the Southeast United States. The sample comprised 70 male students and 82 female students. The mean age of the sample was 19.14 years. Race of participants was 62.5% Caucasian, 22.4% African American, 5.3% Asian, 3.9% Hispanic, 0.7% Native American, and 5.3% other. Year in school was 61.2% freshmen, 34.2% sophomores, 2.6% juniors, 1.3% seniors, 0.7% other.

Measures

Data were collected using a questionnaire containing demographic information, the Physical Self-Efficacy Scale (PSE), Mindfulness Attention Awareness Scale (MAAS), Resilience Questionnaire, SOEA, and Decisional Balance Questionnaire (DBQ). Data was also collected using the physiological measures of weight, height, Body Mass Index (BMI), and V[Q.sub.2]max.

PSE. Perceived physical self- confidence was measured by the PSE (Ryckman, Robbins, Thornton, and Cantrell, 1982), a 22-item, 6-point Likert-type scale ranging from 1 (if you agree strongly) to 6 (if you disagree strongly) that measures Perceived Physical Ability (PPA) and Physical Self-Presentation Confidence (PSPC). Internal consistency as measured by coefficient alpha was found to be .84 for the PPA subscale, .74 for the PSPC subscale, and .81 for the composite PSE. Test-retest reliabilities were found to be .85 for the PPA subscale, .69 for the PSPC subscale, and .80 for the composite PSE scale. Predictive validity was determined by examining the relationship between physical self-efficacy before a marathon and actual finishing time (Gayton et al., 1986).

MAAS. Open or receptive awareness of and attention to what is taking place in the present was measured by the MAAS (Brown & Ryan, 2003), a 15-item, 6-point Likert-type scale ranging from 1 (almost always) to 6 (almost never) that measures mean level of dispositional mindfulness. Internal consistency as measured by coefficient alpha was found to be .82 in college students and .87 in noncollege adults. Test-retest reliability as measured by an intraclass correlation was found to be .81. Also, test-retest score agreement showed that participant's scores over repeated assessments were not significantly different. Convergent and discriminant validity was found by positive correlations with openness to experience, emotional intelligence, and well-being and negative correlations with social anxiety (Baer, 2006).

Resilience Questionnaire. An individual's ability to handle adversity was measured by the Resilience Questionnaire (Sideroff, 2004). The Resilience Questionnaire includes two different forms, however, for this study, only the Resilience Questionnaire Part 1 was administered. Part 1 is a 40-item, 4-point Likert-type scale ranging from 0 (not at all true) to 3 (very true) that measures nine different subcategories: physiological balance, emotional balance, cognitive balance, relationship with self, relationship with others, relationship with something greater, presence, flexibility, and power. The measure results in three main outcome category scores: organismic, relational, and process.

SOEA. An individual's stage of change regarding adopting a regular physical activity routine was measured by the SOEA Scale (Marcus, Banspach et al., 1992), a l-item scale with five possible answer choices. Subjects were placed into one of the five stages of change based upon their answer. This study focused on adults between 18 and 24 years of age. Therefore, regular exercise was operationally defined, for the purposes of this study, as 150 minutes a week of moderate-intensity physical activity or 75 minutes a week of vigorous-intensity aerobic physical activity, based upon the physical activity recommendations of the CDC (CDC, 2008). Face and content validity have been reported. Two week test-retest reliability was found to be .78.

DBQ. The number of pros and cons the participant associates with while participating in a regular exercise routine was measured by the DBQ (Marcus, Rakowski, and Rossi, 1992), a 16-item, 5-point Likert type scale ranging from 1 (not at all important) to 5 (extremely important). Internal consistency as measured by coefficient alpha was found to be .79 for the cons items and .95 for the pros items.

Physiological Measures. Height (HT) was measured to the nearest 0.2 inch and weight (WT) to the nearest 0.0libs (using a Seca [Hamburg, Germany] Mechanical Platform-Beam Medical Scale with Height Rod). Body mass index (BMI) was calculated using the formula: WT(lb) /[HT(in)]2 x 703 (ACSM, 2009; ACSM, 2005). Estimated maximal oxygen consumption (V[O.sub.2]max) was determined using the Rockport One Mile Walking Test on an outdoor track using the protocol of Kline, Porcari, Hintermeister, et al. (1987). The participants V[O.sub.2]max values were estimated from the equation created by Dolgener, Hensley, Marsh, and Fjelstul (1994) which was developed specifically for college-aged adults.

Procedures

The first author obtained permission from the University of South Alabama Institutional Review Board to recruit participants. Participants signed up for the study via the university's subject pool on the Psychology department's website. Next, they signed a consent form or obtained a parental consent form prior to participation from the Psychology Department's office. Participants were then given an invitation code that provided access to the online portion where they completed the demographic questionnaire, PSE, MAAS, RQ, SOEA Scale, and DBQ in counter-balanced order. After completion, each participant signed up to complete the second part.

During the second part, five or six participants met the researcher at assigned times at the university's psychological clinic. Participants were assigned a number and given a name tag to wear with that number. Participant's height and weight were measured on the same scale by the first author. Weight and height was recorded in a notebook next to the participant's number. Next, the first author and an assistant escorted participants to the outdoor track on campus. Upon arrival, participants were instructed to stretch for approximately five minutes. Next, the examiner gave each participant a Polar Heart Rate Monitor watch and instructed participants on proper operation. Participants were then informed of the task: walk a pre-measured one mile course as quickly as possible while maintaining a constant pace. Additionally, the participants were instructed to look at their heart rate on the Polar Heart Rate Monitor watch immediately after crossing the finish line. Participants were then brought to the starting line. Participants were stagger started in order to help researchers keep track of each participant's completion time. Researchers kept track of the lap number and time to complete each lap for each participant. Researchers recorded the completion time and heart rate for each participant in a notebook next to the participant's number. Afterwards, participants were again asked to stretch for five minutes before leaving. The second part of the study took approximately 40 minutes.

Results

Participants were placed in one of five groups according to their self-reported SOEA. Not enough participants endorsed precontemplation which was Group 1, therefore only Groups 2 (contemplation), 3 (preparation), 4 (action), and 5 (maintenance) were compared to the dependent variables. Participants totaled 151 for Groups 2 (n = 20), 3 (n = 51), 4 (n = 28), and 5 (n = 52), however, due to missing variables the total number for each analysis varied.

Bivariate correlations determined which variables should be grouped together for the MANOVAs. The cons, resilience, physical self-efficacy, and mindfulness were moderately to strongly correlated and BMI and V[O.sub.2]max were moderately correlated. Therefore, two MANOVAs were conducted: One using the dependent variables of mindfulness, resilience, physical self-efficacy, and cons and one using the dependent variables of BMI and V[O.sub.2]max. Since the pros were not strongly correlated with the other dependent variables a univariate analysis of variance (ANOVA) was conducted for this dependent variable. This method reduced the number of excluded participants if one MANOVA had been performed with all seven dependent variables. See Table 1 for the means and standard deviations of the variables according to SOEA.

The results of a MANOVA related to Hypotheses 1 through 4 revealed significant differences among the SOEA for the dependent variables (resilience, physical self-efficacy, mindfulness, and cons), Wilks's [LAMBDA] = .644, F(12, 360) = 5.426, p < .001. A follow-up ANOVA was conducted for each dependent variable. Regarding Hypothesis 1, an ANOVA for the cons was significant, F(3,139) = 3.822, p = .011. Follow up post-hoc comparisons revealed significant differences between the means for the cons between Group 3 and Group 5 (p = .037). An ANOVA was run to determine whether the means on the pros varied across the levels of the independent variable, or stages of change. The ANOVA for the pros was significant, F(3, 147) = 8.991, p < .001. Follow up post-hoc comparisons revealed significant differences between the means for the pros between Group 3 and Group 4 (p = .01) and also between Group 3 and Group 5 (p < .001). Regarding Hypothesis 2, an ANOVA revealed significant differences between groups for physical self-efficacy, F(3,139) = 19.997, p < .001. Follow up post-hoc comparisons revealed significant differences between the means for physical self-efficacy of Group 2 and Group 5 (p < .001), Group 3 and Group 5 (p < .001), and Group 4 and Group 5 (p = .001). Regarding Hypothesis 3, no significant differences were found between groups for dispositional mindfulness when an ANOVA was conducted, F(3,139) = 2.499, p = .062. Regarding Hypothesis 4, significant differences were found between groups for resilience, F(3,139) = 3.445, p = .019. Follow up post-hoc comparisons, revealed significant differences between the means for resilience of Group 3 and Group 5 (p = .012).

Results of a MANOVA related to Hypotheses 5 and 6 revealed significant differences among the SOEA on the dependent measures (V[O.sub.2]max and BMI), Wilks's [LAMBDA] = .885, F(6,272) = 2.856, p = .010. A follow-up ANOVA was conducted for each dependent variable. Regarding Hypothesis 5, an ANOVA revealed significant differences between groups for V[O.sub.2]max, F(3, 137) = 4.700, p = .004. Follow up post hoc analyses, using the Bonferonni approach, revealed significant differences between Group 3 and Group 5 (p = .002). Regarding Hypothesis 6, an ANOVA found no significant differences between groups for BMI, F(3,137) = .831, p = .479.

The results of two multiple regression analyses are presented in Table 2. For the psychosocial variables, multiple regression results showed that the effective predictors of SOEA were physical self-efficacy, pros, and resilience, F(3,139) = 29.05, p < .05, adjusted [R.sup.2] = .37, which explained 37.2% of the variance in SOEA. Beta values for physical self-efficacy, pros, resilience, cons, and mindfulness were .04, .05, -.01, .11, and .10, respectively. For the physiological variables, multiple regression results showed that the only effective predictor of SOEA was V[O.sub.2]max, F(1,139) = 8.24, p < .05, adjusted [R.sup.2] = .05, which explained about 5% of the variance in SOEA. Beta values for V[O.sub.2]max and BMI were .04 and .19, respectively.

Discussion

The current study was conducted to determine the predisposing factors that lead college students to engage or not engage in a regular exercise routine. Participants were placed in one of five groups according to their self-reported SOEA. The findings showed that participants in higher stages of change in adopting regular exercise behavior had higher scores of the pros and lower scores on the cons for adopting the exercise behavior, greater physical self-efficacy scores, greater resilience scores, and higher V[O.sub.2]max during the Rockport One-Mile Walk Test than those in the lower stages of change which is consistent with Hypotheses 1, 2, 4 and 5. Some of these findings are consistent with previous researchers' findings showing differences between SOEA and decisional balance, self-efficacy, and V[O.sub.2]max (Callaghan et al, 2002; Cardinal, 1995; Prochaska et al., 1994; Marcus, Rakowski, et al., 1992; Marcus, Selby et al., 1992). The current study attempted to expand the current literature regarding resilience and physical activity and results add support to the literature indicating that greater resilience helps an individual overcome the negative experiences associated with physical activity (Salmon, 2000). Additionally, this is the first study to use physiological measures, rather than self-reported measures of physiological functioning, to corroborate self-reported SOEA. Results indicated that individuals who reported being in higher stages of exercise change actually had higher maximal oxygen uptake than those in the lower stages of change.

Results did not support Hypothesis 3, participants in higher stages of change in adopting regular exercise behavior had higher mindfulness levels than those in lower stages. The lack of significant differences for mindfulness could be due to use of a non-clinical sample. Participants consisted of college students who are most likely "healthy" individuals. Aspects of mindfulness, such as reduced stress, more adaptive functioning, and heightened awareness of experiences would be expected in "healthy" individuals. Mindfulness characteristics are not consistent with poorer mental health or clinical populations, including those suffering from psychological disorders. In fact, research suggests that greater mindfulness may reduce and eliminate various psychological disorders and physical symptoms (Kabat-Zinn, 1982; Teasdale et al., 2000).

Results did not support Hypothesis 6, participants in higher stages of change in adopting regular exercise behavior obtained a lower BMI that those in lower stages of change. The lack of significant differences could be due to the small sample size or that BMI fails to recognize differences between body fat, muscle mass, or bone density in its formula (ACSM, 2009). Nooyens, Koppes, Visscher, Twisk, Kemper, & Schuit (2007) discuss that BMI does not account for the differences between fat mass and lean body mass and therefore it may be better used a measure of body build rather than physical fitness.

Self-Efficacy, Pros, and Resilience Predicts Stage of Exercise Adoption

The finding that self-efficacy, pros of adopting a regular exercise routine, and resilience were the most effective predictors of SOEA was somewhat similar to previous researchers' reports that have consistently demonstrated self-efficacy and decisional balance as predictors of stage of exercise adoption on the TTM (Prochaska, 2008; Marcus, Selby, Niaura, & Rossi, 1992). It was somewhat unusual that the cons of adopting a regular exercise routine were not found to be an effective predictor of SOEA, however, this same finding was found in a study of Chinese undergraduate students (Callaghan et al., 2002). Resilience as one of the most effective predictors of SOEA adds to the literature, and supports Salmon's (2000) research suggesting that greater resilience helps an individual overcome the negative experiences associated with physical activity.

Limitations

One significant limitation of the current study is there were not enough participants in the precontemplation stage to include in the statistical analyses. Including those individuals would have given more comprehensive results and would have provided more information on the differences between the SOEA and the psychosocial and physiological measures. Another limitation is the small number of participants in each group, particularly in the contemplation stage (n = 20). Due to errors in online data collection and heart rate monitor failure for several participants, the MANOVA did not include all of the participants because it excludes participants with missing data from the analyses. To utilize as much of the collected data as possible, we decided to run two MANOVAs and one ANOVA after looking at the correlation coefficients among the seven dependent variables. By using this method we did not exclude as many participants as would have been excluded if one MANOVA had been performed with all seven dependent variables. Additionally, although one goal of this study was to specifically investigate the college-aged student population, this limits the generalizability of the results to a more diverse population. This is due to the fact that college students tend to consist of a specific age group, intelligence, and socioeconomic status. Finally, there may be common variance between resilience and V02max as predictors of the maintenance stage of change. Greater resilience would involve better physical fitness when considering the biopsychosocial construct. This may need to be investigated in future studies.

Future Investigations

Future investigations could include both college-aged and other age groups to verify whether the results of this study are generalizable to other age groups. In addition, researchers could examine to what extent there are gender differences among the psychosocial variables important in predicting who is likely or unlikely to adhere to a regular exercise routine. These results could provide additional insight for sport psychologists working with specifically male or female populations. Finally, in this study only total domain scores were investigated. By teasing apart aspects of the psychosocial factors (e.g., relationship with self component of resilience) important for moving between the five SOEA, researchers would be better able to develop interventions that could help individuals be more ready and successfully adopt and maintain a regular exercise routine at crucial periods of change.

Strengths of Research

The current study addressed how psychosocial factors can influence a college-aged individual to adopt and maintain a regular exercise routine. Some of these factors have been examined in previous research and some have not been previously investigated in relation to SOEA (i.e., mindfulness and resilience). However, the current study included many psychosocial variables (mindfulness, resilience, physical self-efficacy, and decisional balance) in one cohesive study. Also, the current study included two physiological measures to help validate the self-reported SOEA. Since most of the hypotheses for the current study were supported, the foundation of this research can help other researchers understand which psychosocial factors influence exercise adoption. It also allows researchers to know that they can rely on participants to properly classify themselves in the five SOEA. It seems that this research has contributed to the study of exercise adoption and maintenance in that it has identified new variables important in adopting and maintaining a regular exercise routine (i.e., resilience), verified variables previously found to influence exercise adoption and maintenance (i.e., decisional balance, self-efficacy), and provided validity for a common self-report classification system--the Transtheoretical Model of Stage of Behavior Change.

Implication for Sport Psychologists

Findings from this study suggest that therapists might increase their effectiveness with students seeking help with their problems by assessing their level of readiness to change. A brief questionnaire like the SOEA might be helpful in gauging the student's stage of change which would suggest different types of interventions (Marcus, Banspach et al., 1992). For students in precontemplation they might benefit from screenings and feedback about their current functioning. For students who might need to increase their exercise to improve health, receiving feedback on their V[O.sub.2]max and what a healthier level of respiratory functioning should be for them may motivate them to consider changing their behavior.

For those students in the contemplation stage, providing education and discussion about pros and cons of change might be more appropriate. This study suggests emphasizing the pros for exercise adoption might be more effective than focusing on the cons. Students who are in preparation would benefit from goal setting and discussion of what types of intervention they would be willing to commit to. Also, assessing self-efficacy and resilience for students who want to begin exercising might provide insight into additional interventions that might facilitate readiness for taking action.

Students who are ready to take action would benefit from counseling focused on making changes in the here and now. Other interventions that increase self-efficacy and resilience such as social support might be helpful for students who have just started an exercise program. Finally students in the maintenance stage would benefit from booster sessions and other interventions to enhance the changes they have made.

References

American College of Sports Medicine. (2009). ACSM's guidelines for exercise testing and prescription (8"' ed.). Baltimore, MD: Lippincott Williams & Wilkins.

American College of Sports Medicine. (2005). ACSM's guidelines for exercise testing and prescription (7"' ed.). Baltimore, MD: Lippincott Williams & Wilkins.

Baer, R. A. (2006) Mindfulness-based treatment Approaches: Clinician's guide to evidence base and applications. Burlington, M.A.: Academic Press I,M

Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84, 191-215.

Brown, K., & Ryan, R. (2003). The benefits of being present: Mindfulness and its role in psychological well-being. Journal of Personality and Social Psychology, 84, 822-848.

Callaghan, P., Eves, R R, Norman, P., Chang, A. M., & Lung, C. K. (2002). Applying the Transtheoretical Model of Change to exercise in young Chinese people. British Journal of Health Psychology, 7, 267-282.

Cardinal, B. J. (1995). The stages of exercise scale and stages of exercise behavior in female adults. The Journal of Sports Medicine and Physical Fitness, 35(2), 87-92.

Center for Disease Control. (2008, December 17). How much physical activity do adults need?. Retrieved April 8, 2009 from http://www.cdc.gov/physicalactivity/everyone/guidelines/adults.html

Center for Disease Control. (2007). U.S. Physical Activity Statistics. Retrieved April 21, 2009 from http://apps.nccd.cdc.gov/PASurveillance/StateSumV.asp7YeaF2007

Chan, I. W. S., Lai, J. C. L., & Wong, K. W. N. (2006). Resilience is associated with better recovery in Chinese people diagnosed with coronary heart disease. Psychology and Health, 21, 335-349.

Connor, K. M., & Davidson, J. R. T. (2003). Development of a new resilience scale: The Connor-Davidson Resilience Scale (CD-RISC). Depression and Anxiety, 18, 76-82.

Connor, K. M., Davidson, J. R. T., & Lee, L. (2003). Spirituality, resilience, and anger in survivors of violent trauma: A community survey. Journal of Traumatic Stress, 16, 487-494.

Dolgener, F. A., Hensley, L. D., Marsh, J. J., & Fjelstul, J. K. (1994). Validation of the Rockport Fitness Walking Test in college males and females. Research Quarterly for Exercise and Science, 65, 152- 158.

Gardner, F. L., & Moore, Z. E. (2004). A mindfulness-acceptance-commitment-based approach to athletic performance enhancement: Theoretical considerations. Behavior Therapy, 35, 707-723.

Gayton, W. F., Matthews, G. R., & Burchstead, G. N. (1986). An investigation of the validity of the Physical Self-Efficacy Scale in predicting marathon performance. Perceptual and Motor Skills, 63, 752-754.

Kabat-Zinn, J. (1982). An out-patient program in Behavioral Medicine for chronic pain patients based on the practice of mindfulness meditation: theoretical considerations and primary results. General Hospital Psychiatry, 4, 33-47.

Kabat-Zinn, J. (1990) Full catastrophe living: Using the wisdom of your body and mind to face stress, pain, and illness. New York, N.Y.: Bantam Dell. I, M

Kaplan, K., Goldenberg, D., & Galvin-Nadeau, M. (1993). The impact of a meditation-based stress reduction program on fibromyalgia. General Hospital Psychiatry, 15(5), 284-289.

Kline, G.M., Porcari, J. P., Hintermeister, R., Freedom, P. S., Ward, A., McCarron, R. F., Ross, J., & Rippe, J. M. (1987). Estimation of V[O.sub.2]max from a one-mile track walk, gender, age, and body weight. Medicine and Science in Sports and Exercise, 19, 253-259.

Ludwig, D. S., & Kabat-Zinn, J. (2008). Mindfulness and medicine. JAMA, 11, 1350-1352.

Luthar, S. S., & Cicchetti, D. (2000). The construct of resilience: Implications for interventions and social policies. Development and Psychopathology, 12, 857-885.

Marcus, B. H., Banspach, S. W., Lefebvre, R. C., Rossi, J. S., Carleton, R. A., Abrams, D.

B. (1992). Using the stages of change model to increase the adoption of physical activity among community participants. American Journal of Health Promotion, 6, 424-429.

Marcus, B. H., Selby, V. C., Niaura, R. S., & Rossi, J. S. (1992). Self-Efficacy and the Stages of Exercise Behavior Change. Research Quarterly for Exercise and Sport, 63, 60-66.

Marcus, B. H., Rakowski, W., Rossi, J. S. (1992). Assessing motivational readiness and decision making for exercise. Health Psychology, 11, 257-261.

Marcus, B. H., & Simkin, L. R. (1993). The stages of exercise behavior. The Journal of Sports Medicine and Physical Fitness, 33(1), 83-88.

McAuley, E., & Gill, D. (1983). Reliability and validity of the Physical Self-Efficacy Scale in a competitive sport setting. Journal of Sport Psychology, 5, 410-418.

Miller, J., Fletcher, K., & Kabat-Zinn, J. (1995). Three-year follow-up and clinical implications of a mindfulness meditation-based stress reduction intervention in the treatment of anxiety disorders. General Hospital Psychiatry, 17, 192-200.

Nooyens, A. C. J., Koppes, L. L. J., Visscher, T. L. S., Twisk, J. W. R., Kemper, H. C. G., Schuit, A. J., ...Seidell, J. C. (2007). Adolescent skinfold thickness is a better predictor of high body fatness in adults than is body mass index: the Amsterdam Growth and Health Longitudinal Study. American Society for Nutrition, 85, 1533-1539.

Prochaska, J. O. (1994). Strong and weak principles for progressing from precontemplation to action on the basis of twelve problem behaviors. Health Psychology, 13, 47-51.

Prochaska, J. O. (2008). Decision making in the transtheoretical model of behavior change. Med Decis Making, 28, 845-849.

Prochaska, J. O., & Velicer, W. F. (1997). The transtheoretical model of health behavior change. American Journal of Health Promotion, 12, 38-48.

Prochaska, J. O., Velicer, W. F., Rossi, J. S., Goldstein, M. G., Marcus, B. H., Rakowski, W., et al. (1994). Stages of change and decisional balance for 12 problem behaviors. Health Psychology, 13, 39-46.

Ryckman, R. M., Robbins, M. A., Thornton, B., & Cantrell, P. (1982). Development and validation of a Physical Self-Efficacy Scale. Journal of Personality and Social Psychology, 42, 891-900.

Salmon, P. (2001). Effects of physical exercise on anxiety, depression, and sensitivity to stress: A unifying theory. Clinical Psychology Review, 21, 33-61.

Sideroff, Stephen I. (2004). Resilience: A Functional approach to stress. California Biofeedback, 20 (1).

Singh, N., Lancioni, G., Winton, A., Wahler, R., Singh, J., & Sage, M. (2004). Mindful caregiving increases happiness among individuals with profound multiple disabilities. Research in Developmental Disabilities, 25, 207-218.

Teasdale, J.D., Segal, Z.V., Williams, J.M.G., Ridgeway, V.A., Soulsby, J.M., Lau, M.A. (2000). Prevention of relapse/recurrence in major depression by mindfulness-based cognitive therapy. Journal of Consulting and Clinical Psychology, 68(4), 615-623.

Melissa N. Womble

Elise E. Labbe

John F. Shelley-Tremblay

Phillip Norrell

University of South Alabama

Address correspondence to: Elise E. Labbe, Department of Psychology, University South Alabama, Mobile, AL 36688. Email elabbe@usouthal.edu.

Table 1
Descriptive Statistics for Groups and Dependent Variables

                        Cons              Pros           Mindfulness

Group                M        SD       M        SD       M        SD

2. Contemplation   23.26     4.84    37.40     6.23     3.82     0.82
3. Preparation     23.08     3.82    35.14     7.32     3.89     0.60
4. Action          20.85     3.98    40.14     6.25     3.99     0.76
5. Maintenance     20.58     4.05    41.65     6.30     4.22     0.70
Total              22.06     4.41    38.61     7.16     4.00     0.71

                      Physical         Resilience
                    Self-Efficacy

Group                M        SD       M        SD

2. Contemplation   77.89    13.22    76.11    15.85
3. Preparation     83.50    15.31    73.69    14.45
4. Action          88.62    13.70    78.58    12.32
5. Maintenance     102.48   14.58    82.58    13.57
Total              90.32    17.18    78.01    14.33

                    V[O.sub.2]max          BMI

Group                M        SD       M        SD

2. Contemplation   37.46     7.48    24.18     6.08
3. Preparation     35.36     6.35    26.01     5.32
4. Action          36.87     7.69    25.28     5.86
5. Maintenance     40.32     5.87    24.52     4.96
Total              37.63     6.86    25.11     5.39

Table 2
Regression Analyses for Variable Predicting SOEA

Independent Variable         [beta]   Adjusted       F         P
                                      [R.sup.2]

Psychosocial Variables                   .37       29.05 *   .000 *
  Physical Self-Efficacy     .04 *       .28
  Pros                       .05 *       .08
  Resilience                -.01 *       .02
  Cons                        .11
  Mindfulness                 .10
Physiological Variables                  .05       8.34 *    .004 *
  V[O.sub.2]max              .04 *       .05
  BMI                         .19

* p < .05