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This is the author’s version of a work that was submitted/accepted for publication in the following source:
Trost, Stewart G. & Hutley, Jan (2014) Use of physical activity selfmanagement strategies by high school students. Pediatric Exercise Science. (In Press)
This file was downloaded from: http://eprints.qut.edu.au/78781/
c Copyright 2014 Human Kinetics, Inc.
As accepted for publication
Notice: Changes introduced as a result of publishing processes such as
copy-editing and formatting may not be reflected in this document. For a
definitive version of this work, please refer to the published source:
http://dx.doi.org/10.1123/pes.2014-0089
Pediatric Exercise Science
Use of Physical Activity Self-Management Strategies in High
School Students
Journal:
Pediatric Exercise Science
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Manuscript ID:
Manuscript Type:
Keywords:
PES.2014-0089.R1
Research Note
exercise, exercise psychology, health behavior, health promotion, teaching
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Use of Physical Activity Self-Management Strategies by High School Students
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ABSTRACT
Teaching adolescents to use self-management strategies (SMS’s) may be an effective approach
to promoting lifelong physical activity (PA). However, the extent to which adolescents use
SMS’s and their impact on current PA have not been studied previously. The aims of this study
were: 1) describe the prevalence of SMS use in adolescents; and 2) determine relationships
between SMS use, PA self-efficacy, and PA participation. 197 students completed questionnaires
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measuring use of SMS’s, self-efficacy, and PA behavior. The most prevalent SMS’s (>30%)
were thinking about the benefits of PA, making PA more enjoyable, choosing activities that are
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convenient, setting aside time to do PA, and setting goals to do PA. Less than 10% reported
rewarding oneself for PA, writing planned activities in a book or calendar, and keeping charts of
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PA. SMS use was associated with increased self-efficacy (r = 0.47, P < .001) and higher levels of
PA (r = 0.34 P < .001). A one unit difference in SMS scores was associated with a ~ 4-fold
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increase in the probability of being active (OR = 3.7, 95% CI = 1.8-7.4). Although strongly
associated with PA, a relatively small percentage of adolescents routinely use SMS’s.
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Keywords: Adolescents, exercise, self-management skills
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INTRODUCTION
It is well-documented that among youth, regular physical activity is associated with a
range of favorable health outcomes, including reduced adiposity, increased bone mineral density,
and decreased depression and anxiety (10, 25). Yet, despite these benefits, a significant
percentage of adolescents fail to meet established guidelines for participation in physical activity.
Data from the 2011 CDC Youth Risk Behavior Survey indicates that, among 9th through 12th
graders, 59.9 % of males and 38.5% of females are physically active at least 60 minutes per day
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on five or more days per week (9). Objective monitoring data from the 2003-2004 NHANES
indicates that only 8% of young people between the ages of 12 and 19 years meet the current
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guideline of 60-minutes or more of moderate-to-vigorous physical activity daily (26). Thus,
effective strategies to promote regular physical activity among adolescents are needed.
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Social Cognitive Theory (SCT) is a widely used theoretical framework for health
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behavior change interventions (1). A key construct within SCT is self-efficacy, or confidence in
one’s ability to perform a specific behavior (2). Perceived self-efficacy is hypothesized to
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influence the activities that individuals choose to approach, the effort they expend on such
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activities, and the degree of persistence they demonstrate in the face of failure or aversive stimuli
(1,2). Self-efficacy cognitions are theorized to influence health behaviors through the use of
self-management strategies such as self-monitoring, self-reward, and positive self-talk (3).
Individuals who learn to monitor their activity behavior and use goals to motivate and guide their
participation have a better chance of adopting a physically active lifestyle. In addition,
individuals who learn to create incentives and enlist social support for physical activity have a
better chance of sustaining their efforts to engage in regular physical activity.
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Teaching adolescents to use cognitive and behavioral self-management strategies such as
self-monitoring, time management, and identifying and overcoming barriers may be an effective
approach to promoting physical activity in this population (6,7). However, the extent to which
adolescents regularly use physical activity self-management strategies has not been studied
previously. In addition, the relationship between self-efficacy perceptions and self-management
strategies has not been studied extensively in youth. To date, only one investigation has
specifically examined this issue. In a study involving approximately 600 female middle school
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students, Dishman and colleagues (8) showed that self-management strategies such as goal
setting and positive reinforcement mediated the relationship between self-efficacy perceptions
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and physical activity behavior. While the results from that study contributed significantly to our
understanding of how self-management strategies influence physical activity behavior in middle
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school students, it is unknown to what extent the findings are generalizable to male and female
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high school students, particularly those living in rural communities.
To address these gaps in the research literature, the aims of this study were three-fold: 1)
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describe the prevalence with which high school students use physical activity related self-
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management strategies; 2) examine the inter-relationships among the use of self-management
strategies, self-efficacy perceptions, and physical activity behavior in high school students; and
3) determine if self-management strategy use mediated the relationship between self-efficacy
perceptions and physical activity behavior.
METHODS
Participants and Setting
The study was conducted in two rural high schools from a single unified school district in
the mid-western United States. School 1 had an enrollment of 199 students (115 boys, 84 girls)
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and was predominantly white (87%) with a significant percentage of Native American students
(9%). School 2 had an enrollment of 168 students (83 boys, 85 girls), and was also
predominantly white (93%). Relative to school 1, school 2 had a higher percentage of students
eligible for free or reduced price lunch (45% vs. 22%) (See Table 1).
After receiving approval from the respective university and school district institutional
review boards, the parents and guardians of all 367 students enrolled in the schools were mailed
an informed consent packet containing detailed study information, a parental consent form, a
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child assent form, and a pre-paid self-addressed envelope for return of the informed consent
documents. Of the 367 households contacted, 191 (52%) consented to participate in the study.
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Relative to the combined student population, the study sample was comparable with respect to
gender, race/ethnicity, and grade level distribution. A somewhat lower percentage of students
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eligible for free or reduced price lunch chose to participate in the study.
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--Insert Table 1 near here-Instrumentation
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Self-management strategies. The frequency with which students used cognitive and
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behavioral self-management strategies related to physical activity participation (e.g., goal setting
and positive self-reinforcement) was assessed using the 14-item scale developed by Saelens and
colleagues (22). Items were rated on 4-point Likert scales anchored by 1 (“Never”) and 4
(“Often”). Strategies receiving the rating “often” were considered to be used regularly. A
continuous summary score for self-management strategy use was calculated by averaging the
responses to all 14 items (range 1 to 4). Internal consistency for this scale, as measured by
Cronbach’s alpha, was 0.84.
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Self-efficacy. Physical activity self-efficacy was measured using the eight-item scale
described by Motl and colleagues (16). The scale assessed the student’s confidence in their
ability to seek instrumental support for physical activity or overcome previously documented
barriers to activity participation. Sample items included “I can be physically active during my
free time on most days no matter how busy my day is” and “I can ask my parent or other adult to
do physically active things with me.” Items were rated on five point Likert scales anchored by 1
(“Disagree a lot”) and 5 (“Agree a lot”). Internal consistency for this scale, as measured by
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Cronbach’s alpha, was 0.85.
Physical activity. Physical activity was measured using a modification of the Physical
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Activity Questionnaire for Older Children (PAQ-C) (5). Six questions were used to assess
physical activity level in a variety of behavior settings and times, including physical education,
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lunch, after school, evenings, schooldays, and weekends. Each item was scored on 5-point Likert
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scales with higher scores reflecting a greater level of physical activity. Activity level for each
student was calculated by averaging the 5 items (range 1-5). Internal consistency for this scale,
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as measured by Cronbach’s alpha, was acceptable at 0.74. Previous studies have shown the
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PAQ-C to have acceptable reliability and concurrent validity. Test-retest reliability coefficients
range from r = 0.75 - 0.82, while correlations between the PAQ-C and objective measures of
physical activity with an accelerometer range from r = 0.45 - 0.53 (5,11,12).
Procedure
Students completed the survey during a regularly scheduled homeroom period. For
confidentiality purposes, the surveys were distributed and collected in sealed envelopes. Prior to
data collection, all homeroom teachers underwent a brief training session to familiarize
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themselves with study procedures and administered the survey with the aid of a standardized
script. The survey took approximately 15 minutes to complete.
Data Analysis
All data were analyzed using SAS version 9.3 (SAS Institute, Cary NC). Group
differences in the percentage of students using a given self-management strategy were tested
using chi-square analyses using exact tests. Gender and grade level differences in self-
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management strategy scores, physical activity, and physical activity self-efficacy were tested
using two-way gender by grade level ANOVA. Where appropriate, a contrast for linear trend
was performed to test for systematic differences across grade levels. The relationships between
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self-management strategy use, self-efficacy perceptions, and physical activity participation was
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assessed using partial correlations (adjusting for gender and grade level). Logistic regression was
used to calculate the relative odds of being physically active (PAQ score ≥ 2.5) for every one
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unit difference in self-management strategy use. The logistic model included gender and grade
level as covariates. To determine if self-management strategy use mediated the relationship
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between self-efficacy perceptions and physical activity, a mediating variable analysis was
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conducted following the steps outlined by Baron and Kenny (4). For all analyses, significance
was set at an alpha level of 0.05.
RESULTS
Table 2 reports the prevalence of self-management strategy use in the entire sample and
in subgroups defined by gender and grade level. Prevalence estimates ranged from 1.6% to
45.6%. The most prevalent SMS’s (>30%) were thinking often about the benefits of PA, doing
things to make PA more enjoyable, choosing activities that are convenient to do, setting aside
special time to do PA, and setting goals to do PA. Less than 10% of students reported trying to
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get friends to be active instead of watching TV, rewarding oneself for being physically active,
trying to help others be physically active, writing planned activities in a book or calendar, putting
up reminders to be physically active, and keeping charts of PA. Although there were some
differences in prevalence between males and females and among grade levels, none of these
differences reached statistical significance.
--Insert Table 2 near here-Means and 95% confidence intervals for physical activity, self-efficacy perceptions, and
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self-management strategies are shown in Table 3. No gender by grade level interactions were
statistically significant, thus only main effects are reported. Boys were significantly more
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physically active than girls (F(1,183) = 11.75, p < .001), while girls tended to report greater overall
use of self-management strategies compared to boys; however, this difference just failed to reach
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statistical significance (F(1,183) = 3.68, p = 0.056). Although girls scored higher than boys on
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self-efficacy perceptions, this difference did not reach significance (F(1,183) = 0.96, p = 0.33).
With respect to grade related differences in physical activity, there was a significant linear trend
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(F(1,183) = 27.2, p < .001) indicating that physical activity levels decreased significantly with each
management strategies or self-efficacy perceptions.
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successive grade level. There were no significant grade related trends for either self-
--Insert Table 3 near here--
After controlling for grade level and gender, more frequent use of self-management
strategies was associated with increased physical activity self-efficacy (r = 0.47 , P < .001) and
higher levels of physical activity (r = 0.34 P < .001). The results of the logistic regression
analysis indicated that, for every one unit difference in the frequency of self-management
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strategy use, the odds of being physical active increased 3.7 times (95% CI: 1.8 -7.4),
independent of gender and grade level.
Significant correlations (adjusted for grade level and gender) were observed between
self-efficacy and self-management strategies (r = 0.47, P < .001), self-management strategies and
physical activity (r = 0.34 P < .001), and self-efficacy and physical activity (r = 0.35, P < .001),
thus meeting the first three conditions required for evidence of mediation (2). When selfmanagement strategy use and self-efficacy perceptions were included in the same regression
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model, the correlation between self-efficacy and physical activity decreased substantially from
0.35 to 0.23, providing preliminary evidence of at least partial mediation. The Sobel z-test (24)
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was then used to formally test whether self-management strategies mediated the relationship
between self-efficacy and physical activity. The results indicated that the indirect effect of self-
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efficacy on physical activity via self-management strategies was significantly different from zero
self-management strategies.
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(z = 3.70, p = 0.0002), providing confirmatory statistical evidence for the mediational role of
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DISCUSSION
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The results indicate that a relatively small percentage of high school adolescents use
physical activity self-management strategies on a regular basis. Of concern, less than 10% of
students reported regular use of common behavioral self-management strategies such as selfmonitoring and rewarding oneself. Despite the low prevalence, regular use of self-management
strategies was associated with increased physical activity self-efficacy perceptions and higher
levels of physical activity. Independent of the student’s gender and grade level, a one unit
difference in self-management strategy use was associated with nearly a 4-fold increase in the
likelihood of being physically active.
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Although the prevalence of self-management strategies was low overall, cognitive
strategies were more prevalent than behavioral strategies. The prevalence of cognitive strategies
ranged from 11.5% to 45.6%, while regular use of behavioral strategies ranged from 1.6% to
43.5%. The most prevalent cognitive strategy was comprehending the benefits of physical
activity (“I think often about the benefits I will get from being physically active”), while the least
prevalent cognitive strategy was positive self talk (“I praise myself for doing physical activity”).
The most prevalent behavioral strategy was “I do things to make physical activity more
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enjoyable,” and the least prevalent behavioral strategy was self-monitoring (“I keep a chart of
how much physical activity I do”). These findings are consistent with those obtained in college
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students. In a study of 256 university seniors, Saelens and colleagues (22) found cognitive
strategies to be used more regularly than behavioral strategies. Cognitive strategies may be more
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prevalent in young people because they are more intuitively acquired, while behavioral skills
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involve actions or strategies that typically must be learned.
The limited use of self-management strategies, particularly those that can be regarded as
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behavioral skills, has implications for school-based health promotion programs. First and
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foremost, schools can empower students to become more physically active by providing
opportunities to learn and practice cognitive and behavioral self-management skills. In support of
this concept, school-based intervention studies that have focused on teaching behavioral skills to
children and adolescent have reported positive increases in physical activity and/or established
correlates of physical activity behavior (18-21). School-based physical education programs are a
logical setting to provide behavioral skills training (14,23); and curriculum frameworks such as
Conceptual Physical Education (6,7), and NASPE’s Fitness Education Project (17) encourage the
teaching self-management skills such as goal setting and self-monitoring. Unfortunately,
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traditional curricula, emphasizing competitive team sports over true lifetime activities, motor
skill acquisition over of behavioral skills, and physical fitness instead of physical activity,
continue to dominate physical education (13, 27). Therefore, contemporary approaches to
teaching and learning in secondary physical education may need to be reevaluated before it can
play a more significant role in providing and promoting physical activity (15,23,27).
Consistent with the central tenets of Social Cognitive Theory (1), the use of selfmanagement strategies was associated with increased self-efficacy and greater participation in
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physical activity. Moreover, self-management strategies mediated the positive influence of selfefficacy perceptions on physical activity behavior. This latter finding replicates the results of
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Dishman and colleagues (8) who observed self-management strategies to mediate the
relationship between self-efficacy and physical activity behavior in middle school girls. The
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consistency of this finding in both middle school and high school students suggests that
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interventions to promote physical activity in school settings could enhance self-efficacy
perceptions by teaching students basic self-management strategies such as goal setting, time
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management, and enlisting social support. As stated above, school physical education programs
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are a logical outlet for this process; however, other content areas such as health education and
family and consumer sciences would also be suitable venues.
There are several strengths in this study. First, to the best of our knowledge, this is first
study to evaluate the prevalence of self-management strategy use among high school students.
Second, the results contributed new information about inter-relationships among physical
activity self-efficacy, self-management skills, and physical activity participation in high school
students. Finally, the results have important implications for the design of school-based physical
activity programs targeting adolescents. Offsetting these strengths was a number of limitations.
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First, the study was conducted in a single rural school district in the mid-western United States.
As such, the results may not be generalizable to other populations of adolescent youth. Second,
the data obtained was cross-sectional and therefore cannot be used to infer causal relationships.
Third, although the sample reflected the demographics of the region, it lacked racial/ethnic
diversity. Fourth, the study relied on self-report measures, thus increasing the threat of recall and
social desirability bias.
In summary, the prevalence of physical activity self-management strategies in high
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school students was low, with cognitive strategies used more frequently than behavioral
strategies. Self-management strategy use was associated with greater physical activity
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participation and mediated the relationship between self-efficacy perceptions and physical
activity behavior. Additional studies employing longitudinal study designs are needed to
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establish the causal relationship between self-management strategy use and physical activity
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participation. Additional intervention studies evaluating strategies to facilitate the teaching of
self-management strategies in school physical education and other educational settings are also
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warranted.
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REFERENCES
1. Bandura A. Social Foundations of Thought and Action. Englewood Cliffs, NJ: Prentice Hall;
1986
2. Bandura A. Self-efficacy: The Exercise of Control. New York, NY: W.H. Freeman; 1997.
3. Bandura A. The primacy of self-regulation in health promotion. Appl Psychol 2005;54:245254
4. Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological
research: conceptual, strategic, and statistical considerations. J Person Soc Psychol
1986;51(6):1173-1182.
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5. Crocker PR, Bailey DA, Faulkner RA, et al. Measuring general levels of physical activity:
preliminary evidence for the physical activity questionnaire for older children. Med Sci
Sports Exerc 1997;29(10):1344-1349.
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6. Dale D, Corbin CB. Physical activity participation of high school graduates following
exposure to conceptual or traditional physical education. Res Q Exerc Sport 2000;71(1):6168.
7. Dale D, Corbin CB, Cuddihy TF. Can conceptual physical education promote physically
active lifestyles? Ped Exerc Sci 1998;10(2):97-109.
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8. Dishman RK, Motl RW, Sallis JF, et al. Self-management strategies mediate self-efficacy
and physical activity. Am J Prev Med 2005;29(1):10-18.
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9. Eaton DK, Kann L Kinchen S, et al. Youth risk behavior surveillance - United States, 2011.
MMWR.2012; 61(4).
10. Janssen I, Leblanc AG. Systematic review of the health benefits of physical activity and
fitness in school-aged children and youth. Int J Behav Nutr Phys Act 2010;7:40.
11. Kowalski KC, Crocker PR, Faulkner RA. Validation of the physical activity questionnaire
for older children. Ped Exerc Sci 1997;9:174-186.
12. Kowalski KC, Crocker PR, Kowalski NP. Convergent validity of the physical activity
questionnaire for adolescents. Ped Exerc Sci 1997;9:342-352.
13. Lee SM, Burgeson, CR, Fulton, JE, et al. Physical education and physical activity: Results
from the school health policies and programs study 2006. J Sch Health 2007;77(8):435-463.
14. Lounsbery MA, McKenzie TL, Trost SG, et al. Facilitators and barriers to adopting
evidence-based physical education in elementary schools. J Phys Act Health. 2011;8 Suppl
1:S17-25.
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15. McKenzie TL, Lounsbery MA. School physical education: the pill not taken. J Lifestyle Med.
2009;3:219–225.
16. Motl RW, Dishman RK, Trost SG, et al. Factorial validity and invariance of questionnaires
measuring social-cognitive determinants of physical activity among adolescent girls. Prev
Med 2000;31(5):584-594.
17. National Association for Sport and Physical Education. Instructional Framework for Fitness
Education. Reston VA, National Association for Sport and Physical Education; 2012.
18. Neumark-Sztainer D, Story M, Hannan PJ, et al. New Moves: a school-based obesity
prevention program for adolescent girls. Prev Med 2003;37(1):41-51.
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19. Pangrazi RP, Beighle A, Vehige T, et al. Impact of promoting lifestyle activity for youth
(PLAY) on children's physical activity. J Sch Health 2003 73(8):317-321.
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20. Pate RR, Saunders R, Dishman RK, et al. Long-term effects of a physical activity
intervention in high school girls. Am J Prev Med 2007;33(4):276-280.
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21. Pate RR, Ward DS, Saunders RP, et al. Promotion of physical activity among high-school
girls: a randomized controlled trial. Am J Public Health 2005;95(9):1582-1587.
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22. Saelens BE, Gehrman CA, Sallis JF, et al. Use of self-management strategies in a 2-year
cognitive-behavioral intervention to promote physical activity Behav Ther 2000;31:365-379.
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23. Sallis JF, McKenzie TL, Beets MW, et al. Physical education's role in public health: steps
forward and backward over 20 years and HOPE for the future. Res Q Exerc Sport
2012;83(2):125-135.
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24. Sobel ME. Asymptotic confidence intervals for indirect effects in structural equation models.
In: Sociological Methodology. S. Leinhardt S (Ed.). American Sociological Association,
1982, pp. 290-312.
25. Strong WB, Malina RM, Blimkie CJ, et al. (2005). Evidence based physical activity for
school-age youth. J Pediatr 2005;146(6):732-737.
26. Troiano RP, Berrigan D, Dodd KW, et al. Physical activity in the United States measured by
accelerometer. Med Sci Sports Exerc 2008;40(1):181-188.
27. Trost SG. School physical education in the post report era: an analysis from public health. J
Teach Phys Educ 2004;23:316–335.
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Table 1. Demographic information for the both schools and the combined sample
Variable
School
School
#1
#2
All
Students
Final
Sample
(n = 367)
(n = 191)
Grade (%)
20
27
23
26
10
27
23
25
26
11
29
23
26
23
12
24
27
25
23
58
49
54
49
42
51
46
51
22
45
33
27
78
55
67
73
93
90
91
2
6
4
4
4
4
Sex (%)
Male
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9
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Female
Free/Reduced Price Lunch (%)
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Yes
No
87
Native American
9
Hispanic
4
0.5
0
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African American
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White
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Race/Ethnicity (%)
0.3
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Table 2 – Prevalence (%) of regular cognitive and behavioral self-management strategies in high school students.
Self-Management Strategy
Total
Male
Female
Yr 9
Yr 10
Yr 11
Yr 12
31.4
31.9
30.9
33.3
34.0
31.1
26.7
I set aside a special time to do physical activity.
33.0
29.8
36.1
39.2
38.0
28.9
24.4
I praise myself for doing physical activity.
11.5
11.7
11.3
7.8
12.0
11.1
15.6
I think often about the benefits I will get from being physically active.
45.6
41.5
49.5
43.1
42.0
48.9
48.9
I choose activities that are convenient to do.
35.1
28.7
41.2
41.2
30.0
40.0
28.9
24.6
26.6
22.7
23.5
28.0
22.2
24.4
1.6
2.1
1.0
0.0
0.0
4.4
2.2
20.4
20.2
20.6
23.5
22.0
15.6
20.0
I try to get my friends to do physical activity instead of watching TV.
9.4
9.6
9.3
13.7
8.0
8.9
6.7
I put up reminders to be physically active around the house.
2.6
1.1
4.1
2.0
2.0
4.4
2.2
I reward myself for being physically active.
8.4
11.7
5.2
7.8
8.0
6.7
11.1
I do things to make physical activity more enjoyable.
43.5
38.3
48.5
41.2
48.0
46.7
37.8
I write my planned PA sessions in an appointment book or calendar.
4.7
1.1
8.3
5.9
4.0
6.7
2.2
I try to help other people be physically active.
5.8
5.3
6.2
9.8
6.0
6.7
0.0
Cognitive
I set goals to do physical activity.
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I say positive things to myself about physical activity.
Behavioral
I keep a chart of how much physical activity I do.
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I ask a friend to do physical activity with me.
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Table 3. Means (95% confidence intervals) for physical activity, self-efficacy perceptions, and
scores for self-management strategy use.
Physical Activity
Overall (N=191)
Boys (N=94)
Girls (N=97)
Self-management
Strategies
(Range 1 - 5)
(Range 1 - 5)
(Range 1 - 4)
2.55 (2.45, 2.64)
3.62 (3.51, 3.73)
2.49 (2.41, 2.56)
2.69 (2.56, 2.80)
3.57 (3.41, 3.73)
2.41 (2.31, 2.52)
2.39 (2.28, 2.51) *
3.67 (3.52, 3.83)
2.55 (2.45, 2.65)
3.63 (3.42, 3.85)
2.52 (2.38, 2.66)
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Gender
Self-efficacy
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Grade Level
2.87 (2.71, 3.03)
YR10 (N=50)
2.57 (2.41, 2.74)
3.76 (3.42, 3.85)
2.48 (2.34, 2.62)
YR11 (N=45)
2.47 (2.30, 2.64)
3.57 (3.42, 3.85)
2.52 (2.37, 2.67)
YR12 (N=45)
2.24 (2.07, 2.42) #
3.51 (3.42, 3.85)
2.41 (2.26, 2.56)
er
YR 9 (N=51)
# denotes significant linear trend for grade level, p < .001
ew
vi
Re
* denotes significant gender difference, p <. 001
Human Kinetics, 1607 N Market St, Champaign, IL 61825

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