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. Author manuscript; available in PMC: 2008 Jun 23.
Published in final edited form as: Am J Prev Med. 2005 Jul;29(1):10–18. doi: 10.1016/j.amepre.2005.03.012

Self-Management Strategies Mediate Self-Efficacy and Physical Activity

Rod K Dishman 1, Robert W Motl 1, James F Sallis 1, Andrea L Dunn 1, Amanda S Birnbaum 1, Greg J Welk 1, Ariane L Bedimo-Rung 1, Carolyn C Voorhees 1, Jared B Jobe 1
PMCID: PMC2435261  NIHMSID: NIHMS53958  PMID: 15958246

Abstract

Background

Self-efficacy theory proposes that girls who have confidence in their capability to be physically active will perceive fewer barriers to physical activity or be less influenced by them, be more likely to pursue perceived benefits of being physically active, and be more likely to enjoy physical activity. Self-efficacy is theorized also to influence physical activity through self-management strategies (e.g., thoughts, goals, plans, and acts) that support physical activity, but this idea has not been empirically tested.

Methods

Confirmatory factor analysis was used to test the factorial validity of a measure of self-management strategies for physical activity. Next, the construct validity of the measure was tested by examining whether self-management strategies mediated the relationship between self-efficacy and self-reported physical activity, independently of several social-cognitive variables (i.e., perceived barriers, outcome expectancy value, and enjoyment), among cross-sectional samples of 6th grade (n =309) and 8th grade (n =296) girls tested between February 14 and March 17, 2002. Data were analyzed in 2004.

Results

Consistent with theory, self-efficacy had direct effects on the social-cognitive variables. The primary novel finding is that self-management strategies mediated the association of self-efficacy with physical activity in both samples.

Conclusions

The measure of self-management strategies for physical activity yields valid scores among adolescent girls and warrants experimental study as a mediator of the influence of efficacy beliefs on physical activity.

Introduction

Physical inactivity contributes to the increasing health burden of obesity and type 2 diabetes among youths in the United States.1,2 Recent estimates indicate that 26% of girls and 20% of boys aged 9 to 13 years do not participate in physical activity during their free time.3 Moreover, physical activity declines during adolescence, especially among girls.4–7 The public health significance of physical inactivity among adolescent girls underscores the importance of identifying mediators and moderators of physical activity that can be targeted by interventions to increase physical activity levels.8

Social-cognitive variables (i.e., beliefs that are formed by social learning and reinforcement history, such as self-efficacy, perceived barriers, outcome expectancy value, and affective experience) are putative influences on self-initiated change in health behavior.9 They may be especially important during early adolescence as physical activity increasingly becomes a leisure choice. A large number of social-cognitive correlates of physical activity have been identified among adolescents,10 but their independent utility for explaining physical activity has not been determined within the context of established theoretical models of behavior change.

Self-efficacy theory11,12 proposes that confidence in personal ability to carry out a behavior (i.e., self-efficacy) influences the direction, intensity, and persistence of behavior. Accordingly, girls who have high self-efficacy about physical activity would perceive fewer barriers to their physical activity or be less influenced by them, be more likely to act (i.e., pursue goals) on their expectations of desirable outcomes of being physically active (i.e., outcome expectancy value), and be more likely to enjoy physical activity. The causal path between self-efficacy and goal striving has been further elaborated by a mediating role of intervening processes12 or implementation strategies13 (e.g., instrumental acts) that consist of planning, monitoring, and guidance control of goal pursuit. Thus, self-efficacy might influence physical activity by self-management strategies (e.g., thoughts, goals, plans, and acts) that support physical activity, but this idea has not been tested. Nigg14 recently provided evidence of sequential, cross-sectional bivariate relationships across 3 years between exercise behavior and self-efficacy, outcome expectancy value, and a measure of processes of change among adolescents, but the independent and mediated relations of those variables with physical activity were not simultaneously evaluated in that report.

The main purpose of this study was to examine the validity of a measure of self-management strategies for physical activity by testing whether it mediated the relationship between self-efficacy and physical activity, independently of selected social-cognitive variables (i.e., perceived barriers, outcome expectancy value, and enjoyment), among two samples of adolescent girls differing in age. Such a mediating influence would provide evidence for the construct validity of self-management strategies by confirming the functional, theoretical network15 among self-efficacy, self-management strategies, and physical activity.11–13

The validity of measures of the variables had not been reported among 6th grade girls, so confirmatory factor analytic procedures16,17 were used first to establish the factorial validity and the multigroup and longitudinal (i.e., 2 weeks) invariance of the measures in separate samples of 6th and 8th grade girls. Factorial validity is the degree to which the structure of a measure conforms to the theoretical definition of its construct.15,18–20 Factorial invariance is the degree to which a construct is measured similarly between groups of people or across points of time.18,21 Without evidence for factorial invariance, differences between groups or across time in scores on a measure might reflect variability in the measurement properties of the self-report instrument used rather than true differences in the latent variable.

Method

Participants

Adolescent girls in the 6th (n =309) and 8th (n =296) grades were recruited from one to four middle schools in each of six regions of the United States (Baltimore MD, Columbia SC, Minneapolis MN, New Orleans LA, San Diego CA, and Tucson AZ) for the pilot testing of social-cognitive measures to be employed as potential moderators, mediators, or secondary outcomes in the Trial of Activity for Adolescent Girls, a physical activity intervention study sponsored by the National Heart, Lung, and Blood Institute. The 6th-grade girls had a mean age of 11.5 (standard deviation [SD] =0.6) years and racial percentages of 45.6% white, 19.7% black, 14.2% Hispanic/Latino, 3.2% Asian/Pacific Islander, 1.9% American Indian, and 3.9% other; 11.3% of the 6th-grade girls did not report race/ethnicity. The 8th-grade girls had a mean age of 13.5 (SD=0.6) years and racial percentages of 51.0% white, 17.6% black, 13.9% Hispanic/Latino, 3.0% Asian/Pacific Islander, 1.0% American Indian, and 3.0% other; 10.5% of the 8th-grade girls did not report race/ethnicity. The race percentages did not differ (χ2=2.5 [df = 5, n =539], p =0.78) between 6th- and 8th-grade girls.

Measures

Self-management strategies were measured using a modified version of a scale derived from self-management theory, and previously developed for use with college students.22 The scale included eight items that represented cognitive and behavioral strategies. There were four items for cognitive strategies and four for behavioral strategies. Examples of cognitive and behavioral items were, respectively, “I say positive things to myself about physical activity,” and “I do things to make physical activity more enjoyable.” The items were rated on a five-point scale ranging from 1 (never) to 5 (very often). Table 1 contains a list of the scale items.

Table 1.

Items assessing self-management strategies, perceived barriers, outcome expectancy, and physical activity

Self-management strategies

  1. I do things to make physical activity more enjoyable.

  2. I think about the benefits I will get from being physically active.

  3. I try to think more about the benefits of physical activity and less about the hassles of being active.

  4. I say positive things to myself about physical activity.

  5. When I get off track with my physical activity plans, I tell myself I can start again and get right back on track.

  6. I try different kinds of physical activity so that I have more options to choose from.

  7. I set goals to do physical activity.

  8. I make backup plans to be sure I get my physical activity.

Perceived barriers to physical activity

  1. Physical activity is boring.

  2. The weather is bad.

  3. I don’t know how to do the physical activity that I want to do.

  4. I don’t have a place to do physical activity.

  5. I’m chosen last for teams.

  6. I don’t like to sweat.

  7. It would take time away from my friends.

  8. I might get hurt or sore.

  9. It would make me embarrassed.

  10. It would make me tired.

Outcome expectancy

  1. It would help me spend more time with my friends.

  2. It would help get or keep me in shape.

  3. It would help me control my weight.

  4. It would put me in a better mood.

  5. It would make me better in sports, dance, or other activities.

  6. It would be fun.

  7. It would make me look better.

  8. I would make new friends.

  9. I would feel better about myself.

Physical activity

  1. In the last 7 days, during PE classes, how often were you very active (playing hard, running, jumping, throwing)? 1=I did not have PE; 2=hardly ever; 3=sometimes; 4=quite often; 5=always

  2. In the last 7 days, what did you normally do AT LUNCH (besides eating lunch)? 1=sat down (talking, reading, doing school work); 2=stood around; 3=walked around a little; 4=ran around and played quite a bit; 5=ran around and played hard most of the time

  3. In the last 7 days, RIGHT AFTER SCHOOL, how many days did you do sports, dance, or play games in which you were active? 1=none; 2=1 day; 3=2–3 days; 4=4 days; 5=every day

  4. In the last 7 days, on how many EVENINGS did you play sports, dance, or play games in which you were very active? 1=none; 2=1 evening; 3=2–3 evenings; 4=4–5 evenings; 5=6–7 evenings

  5. LAST WEEKEND, how many times did you play sports, dance, or play games in which you were very active? 1=none; 2=1 time; 3=2–3 times; 4=4–5 times; 5=6 or more times

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PE, physical education.

Self-efficacy about physical activity was measured using an eight-item questionnaire developed for use with 8th- and 9th-grade girls and reported elsewhere.16,17 The stability coefficient for the single factor across 1 year was 0.61. Example items on the self-efficacy measure follow: “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.” The items were rated on a five-point scale ranging from 1 (disagree a lot) to 5 (agree a lot).

Perceived barriers to physical activity were assessed by an abridged adaptation of a previously developed measure.23 Among 60 boys and girls in grades 6 to 8 (60% nonwhite), the internal consistency of that scale (Cronbach α) was 0.88, and test–retest reliability (intraclass correlation coefficient [ICC]) across 2 weeks was 0.90. Items were selected from the scale based on content analysis and formative assessment with the girls in this pilot study. The content or language of some items was simplified to facilitate readability (e.g., original items, “my friends tease me during exercise or sports” and “self-conscious about my looks when I do activities” were condensed as, “it would make me embarrassed.” “Lack of a convenient place to do physical activity” was modified as, “I don’t have a place to do physical activity”). A new item was added (i.e., “I might get hurt or sore”). The scale items were rated on a five-point scale ranging from 1 (never) to 5 (very often) (Table 1).

Outcome expectancy value about physical activity was measured using nine items that consisted of belief and corresponding value statements adapted from previously developed scales.16,17,23 Scores on these nine items obtained from separate pilot samples of 50 to 100 girls yielded acceptable internal consistency reliability (Cronbach α 0.72), and an ICC stability coefficient of 0.72 across 1 week. Belief statements were rated on a five-point scale ranging from 1 (disagree a lot) to 5 (agree a lot) (Table 1). Value statements were rated on a five-point scale with responses ranging from 1 (very unimportant) to 5 (very important). The outcome expectancy values were formed as a product of the belief and corresponding value item scores.24

Enjoyment of physical activity was measured using the seven negatively worded items from the modified 16-item version of the Physical Activity Enjoyment Scale reported elsewhere.25 Positively worded items were excluded to reduce participant burden, and to remove their methodologic effect, as described elsewhere.25 Example items were “When I am active I dislike it” and “When I am active it’s no fun at all.” The seven items were rated on a five-point scale ranging from 1 (disagree a lot) to 5 (agree a lot) and reverse scored.

Physical activity was measured using an abridged version of the Physical Activity Questionnaire for Older Children reported elsewhere.26,27 This measure was chosen because it has been validated for use with children of ages similar to the present sample. Also, its length and format minimized participant burden, which was a concern in this pilot study, because all questions had to be answered by the students within a single class period. Each item is scored on a five-point scale, and the sum of the item scores is used as the indicator of physical activity. Internal consistency coefficients (Cronbach α) ranged from 0.79 to 0.89, and the test–retest stability coefficients across 2 weeks were 0.75 for boys and 0.82 for girls.26 Physical activity was defined as “sports, games, or dance that make you breathe hard, make your legs feel tired, or make you sweat.” Five original items were used that specifically assess activity in physical education classes, during the lunch period, right after school, in the evenings, and on the weekend (Table 1). An item pertaining to recess was removed, which was not relevant to the sample,26,28 as were three other items that lacked specificity and judged as too time consuming.

Procedure

The questionnaire administration was approved by the Institutional Review Board at each of the six universities participating in the project. All parents or guardians provided written informed consent, and all participants provided written consent. The scales were administered to participants in small groups of girls during class time by trained data collectors who used standardized protocols and scripts when obtaining responses. Nearly 80% of the 6th- (n =250) and 8th-(n =226) grade girls completed the measures again 2 weeks later, permitting an examination of the stability of the measurement instruments by longitudinal invariance analysis. Testing occurred between February 14 and April 17, 2002.

Data Analysis

Confirmatory factor analysis and path analysis were performed using full-information maximum likelihood (FIML) estimation in AMOS, version 4.0 (SmallWaters Corp., Chicago IL, 1999).29 FIML was selected because there were missing responses to items on the questionnaires, ranging from 1% for the measure of physical activity to 12.5% for the measure of physical activity enjoyment. FIML is an optimal method for the treatment of missing data29,30 that yields accurate fit indices and parameter estimates with up to 25% simulated missing data.31,32

The parameter estimates, standard errors, z-statistics, and squared multiple correlations were inspected for sign and/or magnitude. Parameters with nonsignificant z-statistics and/or a sign opposite of expected direction have no substantively meaningful interpretation.33,34 Large standard errors provide an indication that the parameter estimate is not reliable.35 Model fit was assessed using multiple indices. The χ2 statistic is too sensitive to sample size and assumes the correct model,20,33,35 so other fit indices are commonly used for judging model fit. Values of the root mean square error of approximation (RM-SEA) of 0.06 and zero (and the 90% confidence interval) represent close and exact fit, respectively.36 The comparative fit index (CFI) and non-normed fit index (NNFI) test the proportionate improvement in fit by comparing the target model with the independence model37; values approximating 0.90 and 0.95 indicate acceptable and good fit, respectively.36,37

The tests of multigroup and longitudinal invariance of the measures involved comparing models that imposed successive restrictions on model parameters for the equality of the overall structure, factor loadings, factor variances, and item uniquenesses.20,35 The comparison of nested models was based on χ2 difference tests and changes in the values of the RMSEA, CFI, and NNFI. The criterion of −0.01 for a change in the CFI (CFIconstrained model − CFIunconstrained model) is robust for testing multigroup and longitudinal invariance.38

Results

Descriptive Statistics

Descriptive statistics for the variables are presented in Table 2. The correlations among the variables are provided in Table 3.

Table 2.

Descriptive statistics for self-management strategies, social-cognitive variables, and self-reported physical activity

Measure Overall sample n = 605 6th graders n = 309 8th graders n = 296
Self-management strategies 29.8 (6.9) 30.5 (6.9) 29.1 (6.9)
Self-efficacy 29.4 (6.1) 30.2 (6.2) 28.6 (5.9)
Perceived barriers 21.5 (6.4) 20.9 (6.3) 22.2 (6.4)
Outcome expectancy 162.3 (41.1) 164.3 (41.9) 160.3 (40.3)
Enjoyment 29.5 (5.4) 29.7 (5.5) 29.2 (5.3)
Physical activity 14.9 (4.0) 15.9 (3.8) 13.9 (3.9)
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Note: Values represent mean score (standard deviation). Mean scores for each scale were computed by summing the individual item scores based on unity weights.

Table 3.

Correlations among self-management strategies, social-cognitive variables, and self-reported physical activity

Variables Sample 1 2 3 4 5 6
1. Self-management strategies Overall —
6th grade —
8th grade —
2. Self-efficacy Overall 0.58 —
6th grade 0.56 —
8th grade 0.59 —
3. Barriers Overall −0.34 −0.42 —
6th grade −0.26 −0.36 —
8th grade −0.41 −0.45 —
4. Outcome expectancy Overall 0.46 0.44 −0.25 —
6th grade 0.44 0.42 −0.17 —
8th grade 0.48 0.46 −0.32 —
5. Enjoyment Overall 0.34 0.44 −0.59 0.33 —
6th grade 0.28 0.40 −0.61 0.27 —
8th grade 0.39 0.47 −0.56 0.40 —
6. Physical activity Overall 0.37 0.38 −0.28 0.18 0.26 —
6th grade 0.36 0.38 −0.19 0.15 0.19 —
8th grade 0.34 0.33 −0.33 0.20 0.31 —
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Note: The correlations were computed using a confirmatory factor analytic approach using AMOS 4.0 with all measures modeled as observed variables. All correlations were statistically significant (p<0.05).

Factorial Validity of Measures

Results of the confirmatory factor analyses of responses to the questionnaires supported the factorial validity of the measures. The multigroup and longitudinal invariance analyses indicated that the factor structure and factor loadings were invariant between the samples of 6th and 8th grade girls, and across time in the combined sample, for the self-management strategies and the social-cognitive variables. The factor structure, factor loadings, and factor variances were invariant between groups and across time for the physical activity measure. The measures each conformed to a single factor structure. The fit indices, internal consistency, and stability coefficients for each scale are provided in Table 4.

Table 4.

Fit indices and reliabilities for the self-management strategies, social–cognitive, enjoyment, and physical activity measures

Combined samples
Measure sample χ2 (df) RMSEA (90% CI) CFI NNFI Cronbach α Stability
Self-management strategies
 6th grade 28.1 (19) 0.04 (0.00–0.07) 0.98 0.97 0.83 0.84
 8th grade 45.0 (19) 0.07 (0.04–0.09) 0.97 0.94
Self-efficacy
 6th grade 43.4 (20) 0.06 (0.04–0.09) 0.96 0.94 0.79 0.78
 8th grade 32.5 (20) 0.05 (0.01–0.07) 0.97 0.96
Perceived barriers
 6th grade 41.5 (35) 0.02 (0.00–0.05) 0.99 0.98 0.77 0.77
 8th grade 69.2 (34) 0.06 (0.04–0.08) 0.94 0.92
Outcome expectancy value
 6th grade 59.8 (24) 0.07 (0.05–0.09) 0.97 0.95 0.86 0.64
 8th grade 72.0 (24) 0.08 (0.06–0.11) 0.96 0.93
Enjoyment
 6th grade 50.6 (14) 0.09 (0.07–0.12) 0.94 0.91 0.84 0.73
 8th grade 35.1 (14) 0.07 (0.04–0.10) 0.97 0.96
Physical activity
 6th grade 1.3 (5) 0.00 (0.00–0.02) 1.00 1.04 0.61 0.81
 8th grade 5.5 (5) 0.02 (0.00–0.09) 1.00 0.99
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CI, confidence interval; CFI, comparative fit index; NNFI, non-normed fit index; RMSEA, root mean square error of approximation.

The scale items for outcome expectancy value were best represented by a single substantive factor plus three pairs of correlated uniquenesses between similarly worded items. The self-management strategies items were best represented by two correlated factors (i.e., a cognitive strategies factor and a behavioral strategies factor) in each sample. The size of the correlations (0.89 and 0.80, in 6th- and 8th-grade girls, respectively) supported the existence of a single, second-order factor underlying the two, first-order factors. In the combined sample, internal consistency (Cronbach α) was 0.74 and 0.75 for the cognitive and behavioral first-order factors, respectively. The stability coefficients across 2 weeks were 0.76 and 0.77 for the cognitive and behavioral factors, respectively.

Construct Validity of Self-Management Strategies

Results of the path analysis were similar for the 6th- and 8th-grade girls and provided supporting evidence that the relations among self-efficacy, self-management strategies, and the social-cognitive variables were consistent with the functional network hypothesized by self-efficacy theory. Self-efficacy and self-management strategies had direct, independent effects on physical activity. Moreover, the measure of self-management strategies partially mediated the relationship between self-efficacy and physical activity, supporting its construct validity.

Model specification

The model tested with path analysis is presented in Figures 1 and 2, and was tested in the separate samples of 6th and 8th grade girls. Path analysis, which modeled observed variables (i.e., summed scores from the items for each scale), was used rather than latent variable structural equation modeling because of the high ratio of sample moments (n =1769) in the augmented variance–covariance matrix to the number of participants in each sample (n =309 and n =296). Item parcels were considered for use, but there is no uniform agreement about their appropriateness in covariance modeling because they can bias parameter estimates and influence fit statistics.39 The model included paths (i.e., γs) between the exogenous variable of self-efficacy and the endogenous variables of self-management strategies, perceived barriers, outcome expectancy value, enjoyment, and physical activity. There were paths (i.e., βs) between the self-management strategies, perceived barriers, outcome expectancy value, enjoyment, and physical activity endogenous variables. There were correlated disturbance terms among the self-management strategies, perceived barriers, outcome expectancy value, and enjoyment endogenous variables to account for unexplained common variance that was not of a hypothesized directional nature.

Figure 1.

Figure 1

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Model depicting the hypothesized associations among self-efficacy, self-management strategies, perceived barriers, outcome expectancy value, enjoyment, and physical activity among 6th grade girls. Coefficients are provided for the significant paths. D1 to D5 represent disturbance terms.

Figure 2.

Figure 2

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Model depicting the hypothesized associations among self-efficacy, self-management strategies, perceived barriers, outcome expectancy value, enjoyment, and physical activity among 8th grade girls. Coefficients are provided for the significant paths. D1 to D5 represent disturbance terms.

Model fit: 6th-grade girls

The model in Figure 1 provided a perfect fit because it was completely saturated (i.e., χ2=0, df=0, CFI=1.00). Significant paths are depicted in Figure 1. There were direct effects of self-efficacy on all variables, and self-efficacy and self-management strategies exhibited direct effects on physical activity independently of their relations with the other social-cognitive variables. Self-efficacy also exhibited an indirect effect on physical activity that was partially mediated by self-management strategies.

Model fit: 8th-grade girls

The model in Figure 2 provided a perfect fit because it too was completely saturated (i.e., χ2=0, df=0, CFI=1.00). Significant paths are depicted in Figure 2. There were direct effects of self-efficacy on the social-cognitive variables, and direct effects of self-management strategies and perceived barriers on physical activity. Self-efficacy exhibited indirect effects on physical activity that were mediated by self-management strategies and perceived barriers.

Multigroup invariance analysis

To provide a statistical test of possible differences in the magnitude of the path coefficients between age groups, two nested models were compared. The first model constrained the five common, statistically significant paths between (1) self-efficacy and self-management strategies, (2) self-efficacy and perceived barriers, (3) self-efficacy and outcome expectancy value, (4) self-efficacy and enjoyment, and (5) self-management strategies and physical activity to be equal between the 6th- and 8th-grade girls. This model provided an excellent fit (χ2=3.31, df=5, p =0.65, RMSEA [90% CI]= 0.00 [0.00–0.05], CFI=1.00, NNFI=1.02). The second model did not constrain any of the paths to be equal between groups, and provided a perfect fit as the model was saturated (i.e., χ2=0, df=0, CFI=1.00). There was not a statistically significant difference between the fit of the two nested models (χ2diff = 3.31, df=5, p =0.65), indicating that the five common paths were similar in magnitude between groups of 6th- and 8th-grade girls.

Discussion

This study provides the initial test by covariance modeling of the validity and usefulness of the self-management strategies questionnaire among adolescent girls. Construct validity was supported by the evidence for factorial validity and invariance of the self-management strategies measure and its independent relationship with physical activity in both samples of 6th- and 8th-grade girls.

Two novel findings of the study are consistent with self-efficacy theory, but had not been previously demonstrated for physical activity. First, the association between self-efficacy and physical activity was mediated by self-management strategies. A recent school-based intervention that increased physical activity among adolescent girls also increased self-efficacy and goal setting, but only self-efficacy mediated the increased physical activity.40 That finding and the present results suggest that self-management strategies other than goal setting are a possible mechanism by which self-efficacy influences self-initiated physical activity. Second, no direct association between self-efficacy and physical activity was observed among the 8th-grade girls. Rather, the association of self-efficacy with physical activity was indirect, mediated by self-management strategies and perceived barriers. This suggests that interventions should specifically target self-management strategies and perceived barriers to physical activity as girls progress during adolescence.

With the exception of perceived barriers among 8th graders, the other social-cognitive variables (i.e., outcome expectancy value, perceived barriers among 6th graders, and enjoyment) did not exhibit direct associations with physical activity. In contrast, physical activity has been inversely related to perceived barriers41– 43 and positively related to outcome expectancy value44 –50 and enjoyment25,45,51–53 in other studies of children and adolescents. However, those studies did not directly compare the independent associations of self-efficacy, perceived barriers, outcome expectancy value, and enjoyment with physical activity among girls of different ages. The present results suggest that self-efficacy may account for the influence of those variables on physical activity among young girls. Self-efficacy exhibited a direct relationship with physical activity among the 6th grade sample, consistent with previous cross-sectional44,49,54 and longitudinal40,47,50,52 analyses of samples of adolescent girls and boys.

The used measure focuses on self-efficacy for overcoming barriers to being physically active. Bandura12 has proposed that efficacy beliefs about overcoming barriers should predict exercise adoption, whereas efficacy beliefs about self-regulation of behavior should predict long-term exercise adherence. Accordingly, the results suggest that self-efficacy about overcoming barriers might represent an important initial target for a physical activity intervention during early adolescence, but it is recognized that other forms of efficacy, such as self-regulatory efficacy, might be more important for long-term changes in physical activity.

Bandura11,12 has proposed that self-efficacy operates on behaviors through mediating effects of self-management strategies comprised of cognitive, motivational, affective, and selection dimensions such as strategies for choosing and controlling activities and environments. Those self-management strategies overlap conceptually with several processes of change included in the transtheoretical model (TTM) of stages of change.55 Although self-efficacy has been incorporated within the TTM for studies of exercise among adults,56 no other studies were found to have examined how self-management strategies may differentially mediate the effects of self-efficacy on long-term variations in physical activity among adolescent girls. Because it remains controversial whether fluctuations in self-initiated physical activity occur in distinct stages or represent a continuum,57,58 studies of self-efficacy and self-management strategies should compare methodologies that manipulate or otherwise model changes in physical activity according to discrete stages or a behavioral continuum.59

The cross-sectional design of the study precludes the temporal sequencing of the measures with sufficient time (e.g., >2-week test–retest period) and the experimental manipulation needed to draw inferences about the causal nature of the paths observed between self-efficacy, self-management strategies, and physical activity. Nonetheless, the directional path model examined was derived from self-efficacy11 and self-management13 theories. Because the results are fully consistent with those theories, they are sufficiently positive to encourage experimental research which can confirm that self-management strategies mediate efficacy beliefs about barriers to physical activity among adolescent girls. The findings do not exclude the possibility that the use of self-management strategies might enhance self-efficacy. Research is also needed to examine whether efficacy beliefs about self-regulation influence long-term physical activity and are similarly mediated by self-management strategies.

What This Study Adds

Physical activity declines among girls during adolescence.

Self-efficacy is a putative mediator of successful intervention to increase physical activity.

This correlational study of 6th and 8th grade girls shows that self-management strategies (e.g., thoughts, goals, plans, and acts) that support physical activity can be measured validly, and help explain the relation between self-efficacy and physical activity.

Interventions designed to increase physical activity among adolescent girls by increasing self-efficacy should target and experimentally evaluate self-management strategies.

Acknowledgments

This research was supported by grants from the National Heart, Lung, and Blood Institute of the National Institutes of Health (U01HL66858, U01HL66857, U01HL66845, U01HL66856, U01HL66855, U01HL66853, and U01HL66852).

Footnotes

No financial conflict of interest was reported by the authors of this paper.

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