Regular Exercise Adoption:
Psychosocial Factors Influencing College Students
Melissa N. Womble
Elise E. Labbe
John F. Shelley-Tremblay
Phillip Norrell
University of South Alabama
This study compared predictors of stage of exercise adoption among college students.
Results showed significantly different scores on physical self-efficacy, decisional balance, resilience, and VOp1axfor individuals in higher stages. Physical self-efficacy. pros of a regular
exercise routine, resilience, and VO;nax predicted stage of exercise adoption. No significant
differences were found for mindfulness or BM/. Implications on how to help college students
adopt a regular exercise routine are discussed.
Address .correspondence to: Elise E. Labbe, Department of Psychology, University
South Alabama, Mobile, AL 36688. Email elabbe@usouthal.edu.
203
204 1 Jo11mal of Sport Belw vio1; Vol. 37, No. 2
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
l 0 minutes total per week of moderate or vigorous-intensity activities) , 9. l % 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 (TIM) 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 TIM 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 V0 2p<ak ml/kg/min. TIM
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 TIM (Prochaska, 2008 ; Marcus, Selby, Niaura, &
Rossi, 1992). By identifying these predicting factors and others in a college-aged sample, in-
PSYCHOSOCIAL FACTORS INFLUENCING EXERCISE ADOPTION. .. ! 205
tervention 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 VO,max
as Predictors of Stage of Exercise Adoption (SOEA)
In this study, we examined decisional balance, self-efficacy, mindfulness, resilience,
BMI and VO~max as predictors of SOEA in a college-aged sample. BMI and V02max 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 TIM, 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 TIM. 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
206 1 Journal ofSpori Behavior. Vol. 37, No. 2
greater participation and involvement in sports, higher levels of self-esteem, lower levels of
social anxiety, and better perfonnance 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 TIM, 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).
Spei;ifically, 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
Perfonnance Enhancement that was successful in enhancing underperformance for two
competitive athletes.
PSYCHOSOCIAL FACTORS INFLUENCING EXERCISE ADOPTION.. . ; 207
Resilience refers to the personal qualities thar 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. Sahnon (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
V0 2max during the Rockport One-Mile Walking Test, and 6) a lower BMI than those in lower stages of change.
2081 Journal olSport Behavio1; Vol. 37, No. 2
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
VO,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 I (if
you agree strongly) to 6 (ifyou 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).
lvfAAS. 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 l was adminis-
PSYCHOSOCIAL FACTORS INFLUENCING EXERCISE ADOPTION. .. I 209
tered. Part l 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 I-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 l (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 0.0 llbs (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 (VO,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 V0 2max 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 pennission 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 co~sent 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.
210 I Jou ma/ a/Sport BehaFio1; Vol. 3 7. No . 2
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 pa11icipant's
number. Next, the first author and an assistant escorted pa11icipants 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 infonned 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
MANOYAs. The cons, resilience, physical self-efficacy, and mindfulness were moderately to
strongly correlated and BMI and V0 2max 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 BMl and VO,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 l for the means and standard deviations of the variables according
to SOEA.
PSYCHOSOCIAL FACTORS INFLUENCING EXERCISE ADOPTION ... I 2ll
The results of a MANO VA related to Hypotheses I through 4 revealed significant differences among the SOEA for the dependent variables (resilience, physical self-efficacy, mindfulness, and cons), Wilks's A= .644, F(l2, 360) = 5.426, p < .001. A follow-up ANOVA was
conducted for each dependent variable. Regarding Hypothesis I, an AN OVA 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
ANO VA was run to determine whether the means on the pros varied across the levels of the
independent variable, or stages of change. The ANOYA for the pros was significan~ 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 < .00 I. 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 ,!39) = 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 (V0 2max and BMI), Wilks's A = .885, F(6, 272)
= 2.856, p = .010. A follow-up ANO VA was conducted for each dependent variable. Regarding
Hypothesis 5, an ANOVA revealed significant differences between groups for V0 2max, 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
A NOVA fo und 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 R2 =
.3 7, 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 VOpax, F(l,139) = 8.24, p < .05, adjusted R2 = .05, which explained about 5%
of the variance in SOEA. Beta values for V0 2max and BMI were .04 and .19, respectively.
Table I
c..,
.._
Descriptive Statistics for Groups and Dependent Vari ables
"
'"
Cons
Group
M
2. Contemplation 23 .26
SD
4.84
Pros
M
SD
37.40 6.23
Mindfulness
M
SD
3.82
0.82
Physical
Self- Efficacy
M
SD
~
Res ilience
vol max
~
BMI
--
.a
M
SD
M
77.89 13.22 76.11 15.85 37.46
SD
7.48
M
SD
24.18 6.08
~;:;.
Cl:>
"';::;-.
~
3. Preparation
23 .08
3.82
35.14 7.32
3.89
0.60
83 .50 15.31 73 .69 14.45 35 .36
6.35
26.01 5.32
4. Action
20.85
3.98
40.14 6.25
3.99
0.76
88.62 13.70 78.58 12.32 36.87
7.69
25 .28 5.86
5. Maintenance
20.58
4.05
41.65 6.30
4.22
0.70 102.48 14.58 82 .58 13.57 40 .32
5.87
24.52 4.96
Total .
22 .06
4.4 1 38.61 7.16
4.00
0.71
90.32 17.18 78 .01 14.33 37.63
6.86
25.11 5.39
l..,..
C'
,_
w
_....,
~
'"
PSYCHOSOCIAL FACTORS INFLUENCING EXERCISE ADOPTION... 1213
Table 2
Regression Analyses for Variable Predicting SOEA
~
Adjusted R2
F
.37
29.05*
Physical Self-Efficacy
.04*
.28
Pros
.os•
.08
Resilience
-.01 •
.02
Independent Variable
Psychosocial Variables
Cons
.11
Mindfulness
.10
.os.
Physiological Variables
vol max
BMI
.04*
8.34*
p
.004*
.OS
.19
*p < .05
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 VO~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 VO,max (Callaghan et al. 2002;
Cardinal, 1995; Prochaska et al., 1994; Marcus, Rakowski, et al., 1992; Mfil-cus, 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.
214 I )011mal of Sport BehaviOJ; Vol. 3 7, No. 2
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 BMl 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 resili ence
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 TIM (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.
PSYCHOSOCIAL FACTORS INFLUENCING EXERCISE ADOPTION .. . I 215
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 MANO VA 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 MANO VA 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. l11ese 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
216 I Jo11mal
<~/Sport
Belw 1'io1; Vol. 3 7, No. 2
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 vocm:L< 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.
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