Journal of Physical Activity and Health, 2016, 13, 87 -93 http://dx.doi.org/10.1123/jpah.2014-0555 © 2016 Human Kinetics, Inc.
ORIGINAL RESEARCH
Planning Mediates Between Self-Efficacy and Physical Activity Among Motivated Young Adults Guangyu Zhou, Dongmei Wang, Nina Knoll, and Ralf Schwarzer Background: Often, motivation to be physically active is a necessary precondition of action but still does not suffice to initiate the target behavior. Instead, motivation needs to be translated into action by a self-regulatory process. Self-efficacy and planning are considered to be useful constructs that help to facilitate such translations. Objective: The aim is to examine the roles of motivation, planning, and self-efficacy as well as the mechanisms that operate in the change of physical activity levels. Methods: In a longitudinal observation study with 249 young adults, self-efficacy, planning, motivation, and physical activity were assessed at 2 points in time, 3 months apart. Results: Planning served as a mediator between self-efficacy and physical activity, controlling for baseline activity. In addition to this indirect effect, a moderator effect was found between self-efficacy and stages of change on planning. The mediation operated only in motivated, but not in unmotivated students. Conclusions: A mediation from self-efficacy via planning to physical activity seems to be likely only when people are motivated to become more active. Keywords: health determinants, self-regulation, motivation, stages of behavior change
Engagement in physical activity is essential for both physical and psychological well-being,1 but only between 17% and 23% of 18- to 24-year-old adults report recommended levels of moderate and vigorous activity.2 Several studies have addressed various socialcognitive determinants of physical activity based on social cognitive theory (SCT),3 such as perceived self-efficacy, social support, perceived barriers, outcome expectancies, and self-regulatory skills.4–7 According to Bandura,3 perceived self-efficacy is defined as the belief in one’s capabilities to organize and execute the courses of action required to produce given attainments. In SCT, self-efficacy is proposed as a direct predictor of behavior. A meta-analysis of interventions promoting physical activity documented a close association of r = 0.69 between self-efficacy changes and physical activity changes.8 In addition to self-efficacy, self-regulatory strategies are also important for engaging in regular physical activity.3 Planning, as an essential element of self-regulatory skills, has been examined together with self-efficacy in the health action process approach (HAPA).9 The HAPA is a theoretical framework that can describe, explain, and predict health behavior change, suggesting a distinction between (1) preintentional motivation processes that lead to a behavioral intention and (2) postintentional volition processes that lead to the actual health behavior. Within the 2 phases, different patterns of social-cognitive predictors may emerge. Planning is considered a central mediator between intentions, self-efficacy, and behaviors. HAPA constitutes the theoretical backdrop of the current study.
Zhou ([email protected]) and Knoll are with the Dept of Educational Science and Psychology, Freie Universität Berlin, Germany. Wang is with the Peking University Shenzhen Graduate School, Shenzhen, China. Schwarzer is with the Institute for Positive Psychology and Education, Australian Catholic University, Strathfield, Australia and SWPS University of Social Sciences and Humanities, Warsaw, Poland.
Individuals with an intention to change their behavior are more likely to translate their self-efficacy expectations into action by first developing action plans and coping plans. Action plans refer to when, where, and how to perform a target behavior, whereas coping plans pertain to the anticipation of barriers and the construction of alternative plans.10 It has been found that planning changes have a medium to large relation with physical activity changes.11–13 Several reviews have summarized the findings on the effects of planning on health behaviors.14 Perceived self-efficacy is expected to facilitate the planningbehavior relation because people harboring self-doubts might fail to act upon their plans.3 In individuals with a high level of self-efficacy, planning might be more likely to facilitate their goal achievement because optimistic self-beliefs instigate the execution of plans. Planning was shown, for example, to have synergistic effects with self-efficacy on physical activity.15 However, the role that planning plays in the context of self-efficacy and behavior could also be that of a mediator. Self-efficacious individuals harbor optimistic self-beliefs when facing temptations or adopting a novel course of action.3 They might easily form their own plan on when, where, and how to act and then confidently execute physical activity. Although planning was specified as a mediator between self-efficacy and behaviors in the HAPA,9 few studies specifically examined this particular relationship in the context of physical activity. Some studies based on SCT have confirmed that self-regulatory behavior or self-management strategies mediated the self-efficacy–physical activity relationship,4,5,7 but the effects of planning were confounded with other self-regulatory constructs, such as goal formation. In the current study, planning is specified as a mediator between selfefficacy and physical activity. Whether self-efficacy predicts actual behavior via planning as a mediator might depend on individuals’ motivation levels.16 In line with stage models of health behavior change, the HAPA proposes that individuals pass through qualitatively different stages during the behavior change process. The HAPA distinguishes an initial motivation phase from a later volition phase. Individuals who have not formed an intention to increase physical activity are labeled 87
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“preintenders.” The volition phase includes those who have not yet translated their intentions into action, labeled “intenders,” and those who did, are labeled “actors.” Preintenders are identified by a low behavioral intention in combination with low physical activity levels. Although these unmotivated individuals may sometimes have high confidence in their capabilities to perform a health behavior, they do not see the point to form plans on when, where, and how to initiate actions. Intenders are defined by a combination of high intention and low activity, whereas actors combine higher levels in both variables. The latter 2 groups are often collapsed into 1 and contrasted with the preintenders who lack the readiness to plan and act. Thus, the main distinction is between the unmotivated preintenders and all others. In the current study, we examine whether the hypothesized self-efficacy—planning—behavior chain emerges in both groups or only in 1 of them. The primary objective of this study is to elucidate the mechanisms of behavior change by analyzing the putative mediating role of planning on the self-efficacy–behavior relationship as a function of the stages of behavior change. As previous studies had found the mediational chain from self-efficacy to behavior via planning in line with HAPA, the first research aim is the replication of this simple mediation with longitudinal data among young adults. The second aim addresses the putative moderator role of stages of behavior change, extending the single mediational model to a moderated mediational model. The second aim, therefore, is to analyze whether planning (the mediator, measured at time 2) mediates the effect of self-efficacy (the independent variable, measured at time 1) on behavior (the dependent variable, measured at time 2) as a function of the underlying level of readiness or motivation, as reflected by the stage of change (the moderator, measured at time 1). It is assumed that the moderator operates on the self-efficacy-planning-behavior relation, which is statistically reflected by an interaction between self-efficacy and stage of change. We expect that self-efficacy predicts behavior via planning only among the subgroup of motivated or active individuals. The current study focused on moderate/vigorous physical activity 3 to 5 times per week.
Methods Participants and Procedure Undergraduate students with different study backgrounds were recruited at a Chinese university. At the beginning of the spring semester, research assistants invited students to participate in a study on physical activity. At baseline (time 1 [T1]), 339 college students completed a set of questionnaires after giving informed consent. Three months later, 249 of them completed the measures at the second point in time (time 2 [T2]), constituting the final longitudinal sample for the data analyses. The time span of 3 months was chosen as an alignment to the school year. Most college students attend their courses for a 3-month period. Then, they return home or travel to celebrate their vacations. Thus, their lifestyle would be greatly changed after 3 months involving different levels of physical activity. Expecting such changes, we decided to choose a 3-month time period for the study. All assessments were done with a paperand-pencil format in the presence of a research assistant. Participants who completed both measurements were rewarded with 1 paper bookmark and 2 ball pens (around $0.5). They had an average age of 19.7 years (SD = 1.6, range 17–24 y), and 57.8% were men. The study was approved by the local participating college Institutional Review Board, and informed consent was obtained from all participants.
Measures All scales were translated from English into Chinese by 2 bilingual psychology researchers, and 2 others approved of the translations and back-translations. After evaluating an online pilot study it was ensured that all scales could be well understood. Stages of Change. Stages of change were measured at T1 with
an algorithm developed by Lippke et al17 that has been used in several previous studies.18 To assess stages of change, students were asked “During the past month, did you engage in physical activity at least 3 days per week for 40 minutes or more? Please choose the statement that describes you best.” Responses were designed as a rating scale with 5 choices: 1 = No, and I do not intend to start, 2 = No, but I am considering it, 3 = No, but I seriously intend to start, 4 = Yes, but only for a brief period of time, 5 = Yes, and for a long period of time. People indicating 1 and 2 would be categorized as preintenders, those answering 3 as intenders, and 4 and 5 as actors. Preintenders were coded as unmotivated individuals (0), whereas intenders and actors were jointly coded as being motivated (1). The validity of this procedure has been found useful in discriminating between the 3 HAPA stages of physical activity.19 Self-Efficacy. Self-efficacy was assessed at T1 using 3 items that
target action self-efficacy, developed by Schwarzer and Renner.20 The stem “Certain barriers make it hard to begin exercising. How sure are you that you can begin exercising regularly? I am sure that…,” was followed by 1 example item, such as “I can be physically active at least three times a week for 30 minutes each time.” The response scale ranged from 1 = definitely not to 5 = exactly true. The scale exhibited high reliability and validity in several longitudinal studies.21 Cronbach’s α in the current study was .79. Planning. Planning was measured at T2 with 8 items from Sniehotta et al.10 A 4-item scale assessed action planning with the stem “I have made a detailed plan regarding…,” followed by item examples such as “…when to do my exercise.” Coping planning was assessed by 4 items with the stem “I have made a detailed plan regarding…,” followed by item examples such as “…what to do if something intervenes.” Both subscales were collapsed into 1 planning scale, because they were highly interrelated in this study and did not show sufficient discriminant validity to be analyzed separately (r = 0.77, P < .01). The items had good psychometric properties in several samples.10,22 Cronbach’s α in the current study was .92 for the combined 8-item scale. Physical Activity. Physical activity was measured at T1 and T2
with an index developed by Renner et al.23 Students were asked how often they engaged in, on average, (1) cycling; (2) endurance activities (jogging, running, swimming, rowing, etc.); (3) walking, hiking; (4) calisthenics, gymnastics, aerobics, dancing; (5) strength and weight training; (6) games (baseball, soccer, volleyball, tennis, squash, etc.); and (7) martial arts (karate, judo, taekwondo, aikido, kendo, kickboxing, boxing, etc.). The responses were given on 5-point Likert-type scales:1 = almost every day, 2 = 2 to 3 times a week, 3 = once a week, 4 = 1 to 3 times a month, and 5 = less than once a month or never. They were recoded for each activity, respectively, as 0.0= never, 0.5 = 1 to 3 times a month, 1.0 = once a week, 2.5 = 2 to 3 times a week, and 5.0 = almost every day. Responses were summed up to a total index of physical activity because the focus of this research was not on particular types of activity but on the mechanisms that involved social-cognitive variables with overall activity levels. Results from previous studies show that the index had a medium correlation with the International Physical Activity
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Planning Mediates Between Self-Efficacy and Activity 89
Questionnaire (IPAQ) that was formerly validated with satisfactory psychometric properties in several countries including China.17,24 Age and Gender. Age and gender of participants were recorded
at T1.
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Data Analysis First, using SPSS 22, the independent-sample t-test and χ2 test were used for attrition analysis for continuous variables and categorical variables respectively. Second, a mediation analysis was performed using the PROCESS macro.25 Planning at T2 was specified as a mediator between self-efficacy T1 and physical activity at T2, controlling for gender, age, and baseline behavior (T1 physical activity). Bias-corrected bootstrapping with 5000 resamples was chosen to establish 95% confidence intervals (CIs) for direct, indirect, and total effects. A CI not including 0 as well as a P value < .05 for the indirect path indicated significant mediation. Third, a conditional process analysis was performed to explore possible moderation of the earlier mediation model. Moderated mediation was tested with planning at T2 as a mediator between self-efficacy and physical activity at T2 whereas T1 stage of change served as a moderator between self-efficacy and planning. To examine the interaction between self-efficacy and stage of change, the MODEPROBE macro26 was used. All analyses, based on the longitudinal sample with 249 participants, were conducted using SPSS 22. The expectation maximization method (EM) was chosen to impute missing data for self-efficacy T1 (0.8%), physical activity T1 (9.6%), physical activity T2 (10.4%). No other variable had missing values.
Results Dropout Analysis Of the initial sample, 26.5% were lost at T2 3 months later. Dropout analyses revealed no differences with regard to gender (χ2 = 0.83; P > .05) and with regard to continuous variables (age, self-efficacy, and physical activity) (all P > .05) between participants who took part in both assessments and those who dropped out after T1.
Descriptive Statistics Table 1 displays the descriptive information and a correlation matrix of all variables. All social-cognitive variables showed significant associations with physical activity at T1 and T2, but correlations were small to moderate, meaning that they were measuring different constructs. Gender and age had significant associations with physical activity at T1 and T2, respectively, and were, therefore, included as covariates in the analyses.
Evaluation of the Simple Mediation Model Self-efficacy (T1) predicted subsequent physical activity (T2) (β = .18, P < .01). After controlling for planning (T2), the relation between self-efficacy and subsequent physical activity was reduced to β = .18, P < .01, indicating full mediation (see Figure 1). The indirect effect was β = .08, 95% CI (0.03–0.15). With covariates (age, gender, and physical activity at T1), 27% of the variance in physical activity at T2 was explained.
Evaluation of the Moderated Mediation Model In a first step, planning at T2 was regressed on self-efficacy, stage, and the interaction between stage and self-efficacy at T1. Results indicated that self-efficacy and stage predicted planning with β = .26, P < .01 and β = .18, P < .05, respectively. An interaction of stage and self-efficacy predicted planning with β = .19, P < .01. In a second step, physical activity at T2 was regressed on age, gender, self-efficacy, and physical activity at T1 and planning at T2. T1 physical activity emerged as the strongest predictor of T2 physical activity (β = .34, P < .01), followed by planning (β = .25, P < .01). After controlling for T2 planning, the relation between T1 selfefficacy and T2 physical activity was reduced to β = .11, P > .05, indicating full mediation. The index of moderated mediation was 0.10, 95% CI (0.01–0.25). Altogether, these variables accounted for 19% and 27% of the variances in planning and T2 physical activity, respectively (see Figure 2). Figure 3 illustrates that the slope between planning and selfefficacy is steeper among the more motivated or active students (intenders and actors combined) than that among the less motivated ones (the preintenders). According to post hoc analyses, self-efficacy
Table 1 Means, Range, SD and Correlations of Gender, Age, Stages, Self-Efficacy, Planning, and Physical Activity at Time 1 and Time 2 1 Gender/female %
Range
Mean
SD
1
2
3
4
5
0–1
41.90
–
17–24
19.70
1.60
0.08
3 Stages/preintenders %
0–1
63.10
–
0.09
–0.14a
4 Self-efficacy
1–5
3.38
0.94
0.06
–0.13a
0.40b
5 Planning
1–5
2.94
0.91
0.09
0.01
0.35b
0.35b
6 PA1
0–35
7.70
4.47
0.17b
–0.06
0.24b
0.31b
0.18b
7 PA2
0–21
8.45
4.10
0.13
–0.15a
0.18b
0.29b
0.35b
2 Age/y
Abbreviations: PA1, physical activity at time 1; PA2, physical activity at time 2. Note. Female = 0, male = 1; preintender = 0, intender + actor = 1; variable 1–4 were measured at T1, variables 5–7 were measured at T2. a P < .05. b P < .01. JPAH Vol. 13, No. 1, 2016
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0.41b
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Figure 1 — Planning as a mediator in the relationship between self-efficacy and physical activity, controlling for gender, age and baseline physical activity. Standardized solution; bootstrapped with 5000 resamples (N = 249), using the PROCESS macro.25 * P < .05; ** P < .01.
Figure 2 — Moderation of stages on the self-efficacy into physical activity via planning, with controlling gender, age, and baseline of physical activity. Standardized solution; bootstrapped with 5000 resamples (N = 249), using the PROCESS macro.25 * P < .05; ** P < .01. 90
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Figure 3 — Interaction between stages of change and self-efficacy on planning.
predicted planning only for motivated individuals (β = .44, 95% CI [0.21–0.66]), whereas the relationship was not significant for less motivated ones (β = .11, 95% CI [–0.04 to 0.26]).
Discussion Perceived self-efficacy has been found to be a major predictor of forming intentions to exercise and maintaining the practice for an extended time period. Studies using constructs from different theories show that the effects of self-efficacy on physical activity are stronger than those of other psychosocial determinants.7 Nevertheless, research is needed that goes beyond bivariate relationships and helps to explain whether the well-established direct effect of self-efficacy on behavior is the only meaningful pathway. To elucidate the mechanisms under which self-efficacy predicts physical activity, the current study examined the interplay of self-efficacy with planning and subsequent physical activity change, embedded in a moderated mediation framework and based on a sample of 249 college students who were surveyed at 2 points in time t3 months apart. The first aim was to examine the self-efficacy → planning → activity chain. The findings provided evidence that planning mediated the self-efficacy–physical activity relationship, suggesting that people who hold optimistic self-beliefs engage more in planning and, thus, become more likely to change their behaviors. These results are in line with previous observational studies suggesting that self-efficacy predicts physical activity via self-management strategies.4,5,27 Results are also congruent with a study documenting that treatment effects on physical activity were mediated by
self-regulation and self-regulation mediated self-efficacy.28 Findings suggest that the direct pathway from self-efficacy toward activity has less explanatory power than the indirect pathway via planning. Being confident about one’s future activity may facilitate the generation of action plans. However, this is confounded with time points, as planning was specified as a proximal predictor whereas self-efficacy was specified as a distal predictor. The second aim of the present analyses was to explore the conditions under which this pathway operates. We assumed that the mediation model might not work among less motivated people. This is an assumption in line with stage theories that postulate readiness for change as a prerequisite for self-management strategy use. For example, according to the HAPA, individuals who are in the volitional stage (intenders and actors) are more likely to plan when, where, and how to practice physical activity, compared with those who reside in the motivational stage (preintenders).18 In line with the hypothesis and with previous work on medication adherence, the mediational path from self-efficacy to physical activity via planning emerged in later stage (volitional) individuals, not in less motivated ones.16 For self-efficacious people, the findings implied that the higher their motivation, the more likely they were to form plans and then to report more physical activity. Planning may serve as a mediator between self-efficacy and physical activity only under specific conditions, such as higher levels of motivation. Several previous studies have explored the psychological determinants of physical activity based on health behavior theories, but few of them covered self-efficacy, motivation, and planning together.29 The current study unveiled underlying mechanisms that point to a minimum level of motivation or readiness to change
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before self-efficacious beliefs and planning skills can unfold their power. The novel contribution of our study is the confirmation of the moderated mediation model over a period of 3 months with 2 measurement points in time.
or individuals’ relative position in the process of behavior change seems to be a prerequisite for this mediation chain to operate.
Implications for Practice
1. Warburton DE, Nicol CW, Bredin SS. Health benefits of physical activity: the evidence. CMAJ. 2006;174(6):801–809. PubMed doi:10.1503/ cmaj.051351 2. Department of Health and Human Services. Healthy People 2010: Understanding and Improving Health. Washington, DC: ERIC Clearinghouse; 2000. 3. Bandura A. Self-Efficacy: The Exercise of Control. New York, NY: Freeman; 1997. 4. Anderson ES, Wojcik JR, Winett RA, Williams DM. Social-cognitive determinants of physical activity: the influence of social support, self-efficacy, outcome expectations, and self-regulation among participants in a church-based health promotion study. Health Psychol. 2006;25:510–520. PubMed doi:10.1037/0278-6133.25.4.510 5. Ayotte BJ, Margrett JA, Hicks-Patrick J. Physical activity in middle-aged and young-old adults the roles of self-efficacy, barriers, outcome expectancies, self-regulatory behaviors and social support. J Health Psychol. 2010;15(2):173–185. PubMed doi:10.1177/1359105309342283 6. Mailey EL, McAuley E. Impact of a brief intervention on physical activity and social cognitive determinants among working mothers: a randomized trial. J Behav Med. 2014;37(2):343–355. PubMed doi:10.1007/s10865-013-9492-y 7. Rovniak LS, Anderson ES, Winett RA, Stephens RS. Social cognitive determinants of physical activity in young adults: a prospective structural equation analysis. Ann Behav Med. 2002;24(2):149–156. PubMed doi:10.1207/S15324796ABM2402_12 8. Williams SL, French DP. What are the most effective intervention techniques for changing physical activity self-efficacy and physical activity behaviour and are they the same? Health Educ Res. 2011;26:308–322. PubMed doi:10.1093/her/cyr005 9. Schwarzer R. Modeling health behavior change: how to predict and modify the adoption and maintenance of health behaviors. Appl Psychol Int Rev. 2008;57(1):1–29. doi:10.1111/j.1464-0597.2007.00325.x 10. Sniehotta FF, Schwarzer R, Scholz U, Schüz B. Action planning and coping planning for long-term lifestyle change: theory and assessment. Eur J Soc Psychol. 2005;35:565–576. doi:10.1002/ejsp.258 11. Carraro N, Gaudreau P. Implementation planning as a pathway between goal motivation and goal progress for academic and physical activity goals. J Appl Soc Psychol. 2011;41:1835–1856.doi:10.1111/j.15591816.2011.00795.x 12. Carraro N, Gaudreau P. Spontaneous and experimentally induced action planning and coping planning for physical activity: a metaanalysis. Psychol Sport Exerc. 2013;14:228–248.doi:10.1016/j. psychsport.2012.10.004 13. Kwasnicka D, Presseau J, White M, Sniehotta FF. Does planning how to cope with anticipated barriers facilitate health-related behaviour change? A systematic review. Health Psychol Rev. 2013;7(2):129–145. PubMed doi:10.1080/17437199.2013.766832 14. Hagger MS, Luszczynska A. Implementation intention and action planning interventions in health contexts: state of the research and proposals for the way forward. Appl Psychol Health Well-Being. 2014;6:1–47. PubMed doi:10.1111/aphw.12017 15. Koring M, Richert J, Lippke S, Parschau L, Reuter T, Schwarzer R. Synergistic effects of planning and self-efficacy on physical activity. Health Educ Behav. 2012;39(2):152–158. PubMed doi:10.1177/1090198111417621 16. Pakpour AH, Gellert P, Asefzadeh S, Updegraff JA, Molloy GJ, Sniehotta FF. Intention and planning predicting medication adherence following coronary artery bypass graft surgery. J Psychosom Res. 2014;77(4):287–295. PubMed doi:10.1016/j.jpsychores.2014.07.001 17. Lippke S, Schwarzer R, Ziegelmann JP, Scholz U, Schüz B. Testing stagespecific effects of a stage-matched intervention: a randomized controlled
This study provides further insight into the maintenance of physical activity levels by uncovering for whom and how self-efficacy may affect behavior. Results may also have implications for the design of future interventions to improve physical activity. Although self-efficacy is 1 of the strongest psychosocial correlates of physical activity,30–32 increasing self-efficacy might not successfully improve planning for exercise without considering the stages of behavior change. Therefore, practical interventions should target motivated individuals with a sufficient level of self-efficacy or increase motivation of low intenders before promoting the creation of plans on where, when, and how to initiate behaviors. Evidence for stage-matched interventions demonstrated that goal setting and planning facilitated behavior change in intenders as opposed to nonintenders.17,33 Planning is an alterable variable. It can be easily communicated to individuals with self-regulatory action deficits but a certain level of self-efficacy is needed in addition. Randomized controlled trials have documented evidence in support of such planning interventions.14 Future research should further investigate the interplay of selfefficacy, motivation, and planning to explore whether their relationships remain stable across other age groups (eg, middle-aged person) and other health behaviors (eg, dietary behaviors). Meanwhile, more experimental research should be conducted to manipulate these variables and replicate the results from the observational study.
Limitations Some limitations need to be mentioned. First, the measurement of physical activity was self-reported, and there were no objective data to validate the measurement. However, in other studies, this physical activity index was found to be moderately correlated with IPAQ which had been validated with objective measures of physical activity.17,24 Second, the sample of university students receiving small gifts as a reward for participation may limit the generalizability of the study findings. Future research should include more representative samples to replicate the present results. Third, conclusions from this study were based on longitudinal correlations that do not allow for causal inferences. To further elucidate the mechanism of behavior change, randomized controlled trials need to be done. Fourth, conceptual limitations should be considered. Although there has been a great deal of evidence in favor of distinction between action planning and coping planning,10 subscales of such 2 kinds of planning were collapsed into 1 planning construct because of their high intercorrelation. Fifth, to test mediational processes, a design with at least 3 measurement points in time would have been superior because predictors, mediators, and outcomes could then be tested in the implied temporal order.
Conclusions The research contributes to the investigation of psychological mechanisms between self-efficacy and physical activity. Results demonstrated that planning may serve as a mediator between selfefficacy and behavior. Moreover, accounting for stage membership
References
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trial targeting physical exercise and its predictors. Health Educ Behav. 2010;37(4):533–546. PubMed doi:10.1177/1090198109359386 18. Schwarzer R, Lippke S, Luszczynska A. Mechanisms of health behavior change in persons with chronic illness or disability: the Health Action Process Approach (HAPA). Rehabil Psychol. 2011;56(3):161– 170. PubMed doi:10.1037/a0024509 19. Duan Y, Lippke S, Wagner P, Brehm W. Testing two stage assessments in a Chinese college student sample: correspondences and discontinuity patterns across stages. Psychol Sport Exerc. 2011;12(3):306–313. doi:10.1016/j.psychsport.2010.12.002 20. Schwarzer R, Renner B. Social-cognitive predictors of health behavior: action self-efficacy and coping self-efficacy. Health Psychol. 2000;19(5):487–495. PubMed doi:10.1037/0278-6133.19.5.487 21. Schwarzer R, Luszczynska A, Ziegelmann JP, Scholz U, Lippke S. Social-cognitive predictors of physical exercise adherence: three longitudinal studies in rehabilitation. Health Psychol. 2008;27(Suppl 1):S54–S63. PubMed doi:10.1037/0278-6133.27.1(Suppl.).S54 22. Araújo-Soares V, McIntyre T, Sniehotta FF. Predicting changes in physical activity among adolescents: the role of self-efficacy, intention, action planning and coping planning. Health Educ Res. 2009;24(1):128–139. PubMed doi:10.1093/her/cyn005 23. Renner B, Spivak Y, Kwon S, Schwarzer R. Does age make a difference? Predicting physical activity of South Koreans. Psychol Aging. 2007;22(3):482–493. PubMed doi:10.1037/0882-7974.22.3.482 24. Deng HB, Macfarlane DJ, Thomas GN, et al. Reliability and validity of the IPAQ-Chinese: the Guangzhou Biobank Cohort study. Med Sci Sports Exerc. 2008;40(2):303–307. PubMed doi:10.1249/ mss.0b013e31815b0db5 25. Hayes AF. Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach. New York, NY: The Guilford Press; 2013.
26. Hayes AF, Matthes J. Computational procedures for probing interactions in OLS and logistic regression: SPSS and SAS implementations. Behav Res Methods. 2009;41:924–936. PubMed doi:10.3758/ BRM.41.3.924 27. Dishman RK, Motl RW, Sallis JF, et al. Self-management strategies mediate self-efficacy and physical activity. Am J Prev Med. 2005;29:10–18. PubMed doi:10.1016/j.amepre.2005.03.012 28. Anderson ES, Winett RA, Wojcik JR, Williams DM. Social cognitive mediators of change in a group randomized nutrition and physical activity intervention social support, self-efficacy, outcome expectations and self-regulation in the Guide-to-Health Trial. J Health Psychol. 2010;15(1):21–32. PubMed doi:10.1177/1359105309342297 29. Amireault S, Godin G, Vézina-Im LA. Determinants of physical activity maintenance: a systematic review and meta-analyses. Health Psychol Rev. 2013;7(1):55–91.doi:10.1080/17437199.2012.701060 30. Netz Y, Raviv S. Age differences in motivational orientation toward physical activity: an application of social-cognitive theory. J Psychol. 2004;138:35–48. PubMed doi:10.3200/JRLP.138.1.35-48 31. Scholz U, Keller R, Perren S. Predicting behavioral intentions and physical exercise: a test of the health action process approach at the intrapersonal level. Health Psychol. 2009;28(6):702–708. PubMed doi:10.1037/a0016088 32. Sherwood NE, Jeffery RW. The behavioral determinants of exercise: implications for physical activity interventions. Annu Rev Nutr. 2000;20:21–44. PubMed doi:10.1146/annurev.nutr.20.1.21 33. Schwarzer R, Cao DS, Lippke S. Stage-matched minimal interventions to enhance physical activity in Chinese adolescents. J Adolesc Health. 2010;47(6):533–539. PubMed doi:10.1016/j.jadohealth.2010.03.015
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ORIGINAL RESEARCH
Planning Mediates Between Self-Efficacy and Physical Activity Among Motivated Young Adults Guangyu Zhou, Dongmei Wang, Nina Knoll, and Ralf Schwarzer Background: Often, motivation to be physically active is a necessary precondition of action but still does not suffice to initiate the target behavior. Instead, motivation needs to be translated into action by a self-regulatory process. Self-efficacy and planning are considered to be useful constructs that help to facilitate such translations. Objective: The aim is to examine the roles of motivation, planning, and self-efficacy as well as the mechanisms that operate in the change of physical activity levels. Methods: In a longitudinal observation study with 249 young adults, self-efficacy, planning, motivation, and physical activity were assessed at 2 points in time, 3 months apart. Results: Planning served as a mediator between self-efficacy and physical activity, controlling for baseline activity. In addition to this indirect effect, a moderator effect was found between self-efficacy and stages of change on planning. The mediation operated only in motivated, but not in unmotivated students. Conclusions: A mediation from self-efficacy via planning to physical activity seems to be likely only when people are motivated to become more active. Keywords: health determinants, self-regulation, motivation, stages of behavior change
Engagement in physical activity is essential for both physical and psychological well-being,1 but only between 17% and 23% of 18- to 24-year-old adults report recommended levels of moderate and vigorous activity.2 Several studies have addressed various socialcognitive determinants of physical activity based on social cognitive theory (SCT),3 such as perceived self-efficacy, social support, perceived barriers, outcome expectancies, and self-regulatory skills.4–7 According to Bandura,3 perceived self-efficacy is defined as the belief in one’s capabilities to organize and execute the courses of action required to produce given attainments. In SCT, self-efficacy is proposed as a direct predictor of behavior. A meta-analysis of interventions promoting physical activity documented a close association of r = 0.69 between self-efficacy changes and physical activity changes.8 In addition to self-efficacy, self-regulatory strategies are also important for engaging in regular physical activity.3 Planning, as an essential element of self-regulatory skills, has been examined together with self-efficacy in the health action process approach (HAPA).9 The HAPA is a theoretical framework that can describe, explain, and predict health behavior change, suggesting a distinction between (1) preintentional motivation processes that lead to a behavioral intention and (2) postintentional volition processes that lead to the actual health behavior. Within the 2 phases, different patterns of social-cognitive predictors may emerge. Planning is considered a central mediator between intentions, self-efficacy, and behaviors. HAPA constitutes the theoretical backdrop of the current study.
Zhou ([email protected]) and Knoll are with the Dept of Educational Science and Psychology, Freie Universität Berlin, Germany. Wang is with the Peking University Shenzhen Graduate School, Shenzhen, China. Schwarzer is with the Institute for Positive Psychology and Education, Australian Catholic University, Strathfield, Australia and SWPS University of Social Sciences and Humanities, Warsaw, Poland.
Individuals with an intention to change their behavior are more likely to translate their self-efficacy expectations into action by first developing action plans and coping plans. Action plans refer to when, where, and how to perform a target behavior, whereas coping plans pertain to the anticipation of barriers and the construction of alternative plans.10 It has been found that planning changes have a medium to large relation with physical activity changes.11–13 Several reviews have summarized the findings on the effects of planning on health behaviors.14 Perceived self-efficacy is expected to facilitate the planningbehavior relation because people harboring self-doubts might fail to act upon their plans.3 In individuals with a high level of self-efficacy, planning might be more likely to facilitate their goal achievement because optimistic self-beliefs instigate the execution of plans. Planning was shown, for example, to have synergistic effects with self-efficacy on physical activity.15 However, the role that planning plays in the context of self-efficacy and behavior could also be that of a mediator. Self-efficacious individuals harbor optimistic self-beliefs when facing temptations or adopting a novel course of action.3 They might easily form their own plan on when, where, and how to act and then confidently execute physical activity. Although planning was specified as a mediator between self-efficacy and behaviors in the HAPA,9 few studies specifically examined this particular relationship in the context of physical activity. Some studies based on SCT have confirmed that self-regulatory behavior or self-management strategies mediated the self-efficacy–physical activity relationship,4,5,7 but the effects of planning were confounded with other self-regulatory constructs, such as goal formation. In the current study, planning is specified as a mediator between selfefficacy and physical activity. Whether self-efficacy predicts actual behavior via planning as a mediator might depend on individuals’ motivation levels.16 In line with stage models of health behavior change, the HAPA proposes that individuals pass through qualitatively different stages during the behavior change process. The HAPA distinguishes an initial motivation phase from a later volition phase. Individuals who have not formed an intention to increase physical activity are labeled 87
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“preintenders.” The volition phase includes those who have not yet translated their intentions into action, labeled “intenders,” and those who did, are labeled “actors.” Preintenders are identified by a low behavioral intention in combination with low physical activity levels. Although these unmotivated individuals may sometimes have high confidence in their capabilities to perform a health behavior, they do not see the point to form plans on when, where, and how to initiate actions. Intenders are defined by a combination of high intention and low activity, whereas actors combine higher levels in both variables. The latter 2 groups are often collapsed into 1 and contrasted with the preintenders who lack the readiness to plan and act. Thus, the main distinction is between the unmotivated preintenders and all others. In the current study, we examine whether the hypothesized self-efficacy—planning—behavior chain emerges in both groups or only in 1 of them. The primary objective of this study is to elucidate the mechanisms of behavior change by analyzing the putative mediating role of planning on the self-efficacy–behavior relationship as a function of the stages of behavior change. As previous studies had found the mediational chain from self-efficacy to behavior via planning in line with HAPA, the first research aim is the replication of this simple mediation with longitudinal data among young adults. The second aim addresses the putative moderator role of stages of behavior change, extending the single mediational model to a moderated mediational model. The second aim, therefore, is to analyze whether planning (the mediator, measured at time 2) mediates the effect of self-efficacy (the independent variable, measured at time 1) on behavior (the dependent variable, measured at time 2) as a function of the underlying level of readiness or motivation, as reflected by the stage of change (the moderator, measured at time 1). It is assumed that the moderator operates on the self-efficacy-planning-behavior relation, which is statistically reflected by an interaction between self-efficacy and stage of change. We expect that self-efficacy predicts behavior via planning only among the subgroup of motivated or active individuals. The current study focused on moderate/vigorous physical activity 3 to 5 times per week.
Methods Participants and Procedure Undergraduate students with different study backgrounds were recruited at a Chinese university. At the beginning of the spring semester, research assistants invited students to participate in a study on physical activity. At baseline (time 1 [T1]), 339 college students completed a set of questionnaires after giving informed consent. Three months later, 249 of them completed the measures at the second point in time (time 2 [T2]), constituting the final longitudinal sample for the data analyses. The time span of 3 months was chosen as an alignment to the school year. Most college students attend their courses for a 3-month period. Then, they return home or travel to celebrate their vacations. Thus, their lifestyle would be greatly changed after 3 months involving different levels of physical activity. Expecting such changes, we decided to choose a 3-month time period for the study. All assessments were done with a paperand-pencil format in the presence of a research assistant. Participants who completed both measurements were rewarded with 1 paper bookmark and 2 ball pens (around $0.5). They had an average age of 19.7 years (SD = 1.6, range 17–24 y), and 57.8% were men. The study was approved by the local participating college Institutional Review Board, and informed consent was obtained from all participants.
Measures All scales were translated from English into Chinese by 2 bilingual psychology researchers, and 2 others approved of the translations and back-translations. After evaluating an online pilot study it was ensured that all scales could be well understood. Stages of Change. Stages of change were measured at T1 with
an algorithm developed by Lippke et al17 that has been used in several previous studies.18 To assess stages of change, students were asked “During the past month, did you engage in physical activity at least 3 days per week for 40 minutes or more? Please choose the statement that describes you best.” Responses were designed as a rating scale with 5 choices: 1 = No, and I do not intend to start, 2 = No, but I am considering it, 3 = No, but I seriously intend to start, 4 = Yes, but only for a brief period of time, 5 = Yes, and for a long period of time. People indicating 1 and 2 would be categorized as preintenders, those answering 3 as intenders, and 4 and 5 as actors. Preintenders were coded as unmotivated individuals (0), whereas intenders and actors were jointly coded as being motivated (1). The validity of this procedure has been found useful in discriminating between the 3 HAPA stages of physical activity.19 Self-Efficacy. Self-efficacy was assessed at T1 using 3 items that
target action self-efficacy, developed by Schwarzer and Renner.20 The stem “Certain barriers make it hard to begin exercising. How sure are you that you can begin exercising regularly? I am sure that…,” was followed by 1 example item, such as “I can be physically active at least three times a week for 30 minutes each time.” The response scale ranged from 1 = definitely not to 5 = exactly true. The scale exhibited high reliability and validity in several longitudinal studies.21 Cronbach’s α in the current study was .79. Planning. Planning was measured at T2 with 8 items from Sniehotta et al.10 A 4-item scale assessed action planning with the stem “I have made a detailed plan regarding…,” followed by item examples such as “…when to do my exercise.” Coping planning was assessed by 4 items with the stem “I have made a detailed plan regarding…,” followed by item examples such as “…what to do if something intervenes.” Both subscales were collapsed into 1 planning scale, because they were highly interrelated in this study and did not show sufficient discriminant validity to be analyzed separately (r = 0.77, P < .01). The items had good psychometric properties in several samples.10,22 Cronbach’s α in the current study was .92 for the combined 8-item scale. Physical Activity. Physical activity was measured at T1 and T2
with an index developed by Renner et al.23 Students were asked how often they engaged in, on average, (1) cycling; (2) endurance activities (jogging, running, swimming, rowing, etc.); (3) walking, hiking; (4) calisthenics, gymnastics, aerobics, dancing; (5) strength and weight training; (6) games (baseball, soccer, volleyball, tennis, squash, etc.); and (7) martial arts (karate, judo, taekwondo, aikido, kendo, kickboxing, boxing, etc.). The responses were given on 5-point Likert-type scales:1 = almost every day, 2 = 2 to 3 times a week, 3 = once a week, 4 = 1 to 3 times a month, and 5 = less than once a month or never. They were recoded for each activity, respectively, as 0.0= never, 0.5 = 1 to 3 times a month, 1.0 = once a week, 2.5 = 2 to 3 times a week, and 5.0 = almost every day. Responses were summed up to a total index of physical activity because the focus of this research was not on particular types of activity but on the mechanisms that involved social-cognitive variables with overall activity levels. Results from previous studies show that the index had a medium correlation with the International Physical Activity
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Planning Mediates Between Self-Efficacy and Activity 89
Questionnaire (IPAQ) that was formerly validated with satisfactory psychometric properties in several countries including China.17,24 Age and Gender. Age and gender of participants were recorded
at T1.
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Data Analysis First, using SPSS 22, the independent-sample t-test and χ2 test were used for attrition analysis for continuous variables and categorical variables respectively. Second, a mediation analysis was performed using the PROCESS macro.25 Planning at T2 was specified as a mediator between self-efficacy T1 and physical activity at T2, controlling for gender, age, and baseline behavior (T1 physical activity). Bias-corrected bootstrapping with 5000 resamples was chosen to establish 95% confidence intervals (CIs) for direct, indirect, and total effects. A CI not including 0 as well as a P value < .05 for the indirect path indicated significant mediation. Third, a conditional process analysis was performed to explore possible moderation of the earlier mediation model. Moderated mediation was tested with planning at T2 as a mediator between self-efficacy and physical activity at T2 whereas T1 stage of change served as a moderator between self-efficacy and planning. To examine the interaction between self-efficacy and stage of change, the MODEPROBE macro26 was used. All analyses, based on the longitudinal sample with 249 participants, were conducted using SPSS 22. The expectation maximization method (EM) was chosen to impute missing data for self-efficacy T1 (0.8%), physical activity T1 (9.6%), physical activity T2 (10.4%). No other variable had missing values.
Results Dropout Analysis Of the initial sample, 26.5% were lost at T2 3 months later. Dropout analyses revealed no differences with regard to gender (χ2 = 0.83; P > .05) and with regard to continuous variables (age, self-efficacy, and physical activity) (all P > .05) between participants who took part in both assessments and those who dropped out after T1.
Descriptive Statistics Table 1 displays the descriptive information and a correlation matrix of all variables. All social-cognitive variables showed significant associations with physical activity at T1 and T2, but correlations were small to moderate, meaning that they were measuring different constructs. Gender and age had significant associations with physical activity at T1 and T2, respectively, and were, therefore, included as covariates in the analyses.
Evaluation of the Simple Mediation Model Self-efficacy (T1) predicted subsequent physical activity (T2) (β = .18, P < .01). After controlling for planning (T2), the relation between self-efficacy and subsequent physical activity was reduced to β = .18, P < .01, indicating full mediation (see Figure 1). The indirect effect was β = .08, 95% CI (0.03–0.15). With covariates (age, gender, and physical activity at T1), 27% of the variance in physical activity at T2 was explained.
Evaluation of the Moderated Mediation Model In a first step, planning at T2 was regressed on self-efficacy, stage, and the interaction between stage and self-efficacy at T1. Results indicated that self-efficacy and stage predicted planning with β = .26, P < .01 and β = .18, P < .05, respectively. An interaction of stage and self-efficacy predicted planning with β = .19, P < .01. In a second step, physical activity at T2 was regressed on age, gender, self-efficacy, and physical activity at T1 and planning at T2. T1 physical activity emerged as the strongest predictor of T2 physical activity (β = .34, P < .01), followed by planning (β = .25, P < .01). After controlling for T2 planning, the relation between T1 selfefficacy and T2 physical activity was reduced to β = .11, P > .05, indicating full mediation. The index of moderated mediation was 0.10, 95% CI (0.01–0.25). Altogether, these variables accounted for 19% and 27% of the variances in planning and T2 physical activity, respectively (see Figure 2). Figure 3 illustrates that the slope between planning and selfefficacy is steeper among the more motivated or active students (intenders and actors combined) than that among the less motivated ones (the preintenders). According to post hoc analyses, self-efficacy
Table 1 Means, Range, SD and Correlations of Gender, Age, Stages, Self-Efficacy, Planning, and Physical Activity at Time 1 and Time 2 1 Gender/female %
Range
Mean
SD
1
2
3
4
5
0–1
41.90
–
17–24
19.70
1.60
0.08
3 Stages/preintenders %
0–1
63.10
–
0.09
–0.14a
4 Self-efficacy
1–5
3.38
0.94
0.06
–0.13a
0.40b
5 Planning
1–5
2.94
0.91
0.09
0.01
0.35b
0.35b
6 PA1
0–35
7.70
4.47
0.17b
–0.06
0.24b
0.31b
0.18b
7 PA2
0–21
8.45
4.10
0.13
–0.15a
0.18b
0.29b
0.35b
2 Age/y
Abbreviations: PA1, physical activity at time 1; PA2, physical activity at time 2. Note. Female = 0, male = 1; preintender = 0, intender + actor = 1; variable 1–4 were measured at T1, variables 5–7 were measured at T2. a P < .05. b P < .01. JPAH Vol. 13, No. 1, 2016
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0.41b
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Figure 1 — Planning as a mediator in the relationship between self-efficacy and physical activity, controlling for gender, age and baseline physical activity. Standardized solution; bootstrapped with 5000 resamples (N = 249), using the PROCESS macro.25 * P < .05; ** P < .01.
Figure 2 — Moderation of stages on the self-efficacy into physical activity via planning, with controlling gender, age, and baseline of physical activity. Standardized solution; bootstrapped with 5000 resamples (N = 249), using the PROCESS macro.25 * P < .05; ** P < .01. 90
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Figure 3 — Interaction between stages of change and self-efficacy on planning.
predicted planning only for motivated individuals (β = .44, 95% CI [0.21–0.66]), whereas the relationship was not significant for less motivated ones (β = .11, 95% CI [–0.04 to 0.26]).
Discussion Perceived self-efficacy has been found to be a major predictor of forming intentions to exercise and maintaining the practice for an extended time period. Studies using constructs from different theories show that the effects of self-efficacy on physical activity are stronger than those of other psychosocial determinants.7 Nevertheless, research is needed that goes beyond bivariate relationships and helps to explain whether the well-established direct effect of self-efficacy on behavior is the only meaningful pathway. To elucidate the mechanisms under which self-efficacy predicts physical activity, the current study examined the interplay of self-efficacy with planning and subsequent physical activity change, embedded in a moderated mediation framework and based on a sample of 249 college students who were surveyed at 2 points in time t3 months apart. The first aim was to examine the self-efficacy → planning → activity chain. The findings provided evidence that planning mediated the self-efficacy–physical activity relationship, suggesting that people who hold optimistic self-beliefs engage more in planning and, thus, become more likely to change their behaviors. These results are in line with previous observational studies suggesting that self-efficacy predicts physical activity via self-management strategies.4,5,27 Results are also congruent with a study documenting that treatment effects on physical activity were mediated by
self-regulation and self-regulation mediated self-efficacy.28 Findings suggest that the direct pathway from self-efficacy toward activity has less explanatory power than the indirect pathway via planning. Being confident about one’s future activity may facilitate the generation of action plans. However, this is confounded with time points, as planning was specified as a proximal predictor whereas self-efficacy was specified as a distal predictor. The second aim of the present analyses was to explore the conditions under which this pathway operates. We assumed that the mediation model might not work among less motivated people. This is an assumption in line with stage theories that postulate readiness for change as a prerequisite for self-management strategy use. For example, according to the HAPA, individuals who are in the volitional stage (intenders and actors) are more likely to plan when, where, and how to practice physical activity, compared with those who reside in the motivational stage (preintenders).18 In line with the hypothesis and with previous work on medication adherence, the mediational path from self-efficacy to physical activity via planning emerged in later stage (volitional) individuals, not in less motivated ones.16 For self-efficacious people, the findings implied that the higher their motivation, the more likely they were to form plans and then to report more physical activity. Planning may serve as a mediator between self-efficacy and physical activity only under specific conditions, such as higher levels of motivation. Several previous studies have explored the psychological determinants of physical activity based on health behavior theories, but few of them covered self-efficacy, motivation, and planning together.29 The current study unveiled underlying mechanisms that point to a minimum level of motivation or readiness to change
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before self-efficacious beliefs and planning skills can unfold their power. The novel contribution of our study is the confirmation of the moderated mediation model over a period of 3 months with 2 measurement points in time.
or individuals’ relative position in the process of behavior change seems to be a prerequisite for this mediation chain to operate.
Implications for Practice
1. Warburton DE, Nicol CW, Bredin SS. Health benefits of physical activity: the evidence. CMAJ. 2006;174(6):801–809. PubMed doi:10.1503/ cmaj.051351 2. Department of Health and Human Services. Healthy People 2010: Understanding and Improving Health. Washington, DC: ERIC Clearinghouse; 2000. 3. Bandura A. Self-Efficacy: The Exercise of Control. New York, NY: Freeman; 1997. 4. Anderson ES, Wojcik JR, Winett RA, Williams DM. Social-cognitive determinants of physical activity: the influence of social support, self-efficacy, outcome expectations, and self-regulation among participants in a church-based health promotion study. Health Psychol. 2006;25:510–520. PubMed doi:10.1037/0278-6133.25.4.510 5. Ayotte BJ, Margrett JA, Hicks-Patrick J. Physical activity in middle-aged and young-old adults the roles of self-efficacy, barriers, outcome expectancies, self-regulatory behaviors and social support. J Health Psychol. 2010;15(2):173–185. PubMed doi:10.1177/1359105309342283 6. Mailey EL, McAuley E. Impact of a brief intervention on physical activity and social cognitive determinants among working mothers: a randomized trial. J Behav Med. 2014;37(2):343–355. PubMed doi:10.1007/s10865-013-9492-y 7. Rovniak LS, Anderson ES, Winett RA, Stephens RS. Social cognitive determinants of physical activity in young adults: a prospective structural equation analysis. Ann Behav Med. 2002;24(2):149–156. PubMed doi:10.1207/S15324796ABM2402_12 8. Williams SL, French DP. What are the most effective intervention techniques for changing physical activity self-efficacy and physical activity behaviour and are they the same? Health Educ Res. 2011;26:308–322. PubMed doi:10.1093/her/cyr005 9. Schwarzer R. Modeling health behavior change: how to predict and modify the adoption and maintenance of health behaviors. Appl Psychol Int Rev. 2008;57(1):1–29. doi:10.1111/j.1464-0597.2007.00325.x 10. Sniehotta FF, Schwarzer R, Scholz U, Schüz B. Action planning and coping planning for long-term lifestyle change: theory and assessment. Eur J Soc Psychol. 2005;35:565–576. doi:10.1002/ejsp.258 11. Carraro N, Gaudreau P. Implementation planning as a pathway between goal motivation and goal progress for academic and physical activity goals. J Appl Soc Psychol. 2011;41:1835–1856.doi:10.1111/j.15591816.2011.00795.x 12. Carraro N, Gaudreau P. Spontaneous and experimentally induced action planning and coping planning for physical activity: a metaanalysis. Psychol Sport Exerc. 2013;14:228–248.doi:10.1016/j. psychsport.2012.10.004 13. Kwasnicka D, Presseau J, White M, Sniehotta FF. Does planning how to cope with anticipated barriers facilitate health-related behaviour change? A systematic review. Health Psychol Rev. 2013;7(2):129–145. PubMed doi:10.1080/17437199.2013.766832 14. Hagger MS, Luszczynska A. Implementation intention and action planning interventions in health contexts: state of the research and proposals for the way forward. Appl Psychol Health Well-Being. 2014;6:1–47. PubMed doi:10.1111/aphw.12017 15. Koring M, Richert J, Lippke S, Parschau L, Reuter T, Schwarzer R. Synergistic effects of planning and self-efficacy on physical activity. Health Educ Behav. 2012;39(2):152–158. PubMed doi:10.1177/1090198111417621 16. Pakpour AH, Gellert P, Asefzadeh S, Updegraff JA, Molloy GJ, Sniehotta FF. Intention and planning predicting medication adherence following coronary artery bypass graft surgery. J Psychosom Res. 2014;77(4):287–295. PubMed doi:10.1016/j.jpsychores.2014.07.001 17. Lippke S, Schwarzer R, Ziegelmann JP, Scholz U, Schüz B. Testing stagespecific effects of a stage-matched intervention: a randomized controlled
This study provides further insight into the maintenance of physical activity levels by uncovering for whom and how self-efficacy may affect behavior. Results may also have implications for the design of future interventions to improve physical activity. Although self-efficacy is 1 of the strongest psychosocial correlates of physical activity,30–32 increasing self-efficacy might not successfully improve planning for exercise without considering the stages of behavior change. Therefore, practical interventions should target motivated individuals with a sufficient level of self-efficacy or increase motivation of low intenders before promoting the creation of plans on where, when, and how to initiate behaviors. Evidence for stage-matched interventions demonstrated that goal setting and planning facilitated behavior change in intenders as opposed to nonintenders.17,33 Planning is an alterable variable. It can be easily communicated to individuals with self-regulatory action deficits but a certain level of self-efficacy is needed in addition. Randomized controlled trials have documented evidence in support of such planning interventions.14 Future research should further investigate the interplay of selfefficacy, motivation, and planning to explore whether their relationships remain stable across other age groups (eg, middle-aged person) and other health behaviors (eg, dietary behaviors). Meanwhile, more experimental research should be conducted to manipulate these variables and replicate the results from the observational study.
Limitations Some limitations need to be mentioned. First, the measurement of physical activity was self-reported, and there were no objective data to validate the measurement. However, in other studies, this physical activity index was found to be moderately correlated with IPAQ which had been validated with objective measures of physical activity.17,24 Second, the sample of university students receiving small gifts as a reward for participation may limit the generalizability of the study findings. Future research should include more representative samples to replicate the present results. Third, conclusions from this study were based on longitudinal correlations that do not allow for causal inferences. To further elucidate the mechanism of behavior change, randomized controlled trials need to be done. Fourth, conceptual limitations should be considered. Although there has been a great deal of evidence in favor of distinction between action planning and coping planning,10 subscales of such 2 kinds of planning were collapsed into 1 planning construct because of their high intercorrelation. Fifth, to test mediational processes, a design with at least 3 measurement points in time would have been superior because predictors, mediators, and outcomes could then be tested in the implied temporal order.
Conclusions The research contributes to the investigation of psychological mechanisms between self-efficacy and physical activity. Results demonstrated that planning may serve as a mediator between selfefficacy and behavior. Moreover, accounting for stage membership
References
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trial targeting physical exercise and its predictors. Health Educ Behav. 2010;37(4):533–546. PubMed doi:10.1177/1090198109359386 18. Schwarzer R, Lippke S, Luszczynska A. Mechanisms of health behavior change in persons with chronic illness or disability: the Health Action Process Approach (HAPA). Rehabil Psychol. 2011;56(3):161– 170. PubMed doi:10.1037/a0024509 19. Duan Y, Lippke S, Wagner P, Brehm W. Testing two stage assessments in a Chinese college student sample: correspondences and discontinuity patterns across stages. Psychol Sport Exerc. 2011;12(3):306–313. doi:10.1016/j.psychsport.2010.12.002 20. Schwarzer R, Renner B. Social-cognitive predictors of health behavior: action self-efficacy and coping self-efficacy. Health Psychol. 2000;19(5):487–495. PubMed doi:10.1037/0278-6133.19.5.487 21. Schwarzer R, Luszczynska A, Ziegelmann JP, Scholz U, Lippke S. Social-cognitive predictors of physical exercise adherence: three longitudinal studies in rehabilitation. Health Psychol. 2008;27(Suppl 1):S54–S63. PubMed doi:10.1037/0278-6133.27.1(Suppl.).S54 22. Araújo-Soares V, McIntyre T, Sniehotta FF. Predicting changes in physical activity among adolescents: the role of self-efficacy, intention, action planning and coping planning. Health Educ Res. 2009;24(1):128–139. PubMed doi:10.1093/her/cyn005 23. Renner B, Spivak Y, Kwon S, Schwarzer R. Does age make a difference? Predicting physical activity of South Koreans. Psychol Aging. 2007;22(3):482–493. PubMed doi:10.1037/0882-7974.22.3.482 24. Deng HB, Macfarlane DJ, Thomas GN, et al. Reliability and validity of the IPAQ-Chinese: the Guangzhou Biobank Cohort study. Med Sci Sports Exerc. 2008;40(2):303–307. PubMed doi:10.1249/ mss.0b013e31815b0db5 25. Hayes AF. Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach. New York, NY: The Guilford Press; 2013.
26. Hayes AF, Matthes J. Computational procedures for probing interactions in OLS and logistic regression: SPSS and SAS implementations. Behav Res Methods. 2009;41:924–936. PubMed doi:10.3758/ BRM.41.3.924 27. Dishman RK, Motl RW, Sallis JF, et al. Self-management strategies mediate self-efficacy and physical activity. Am J Prev Med. 2005;29:10–18. PubMed doi:10.1016/j.amepre.2005.03.012 28. Anderson ES, Winett RA, Wojcik JR, Williams DM. Social cognitive mediators of change in a group randomized nutrition and physical activity intervention social support, self-efficacy, outcome expectations and self-regulation in the Guide-to-Health Trial. J Health Psychol. 2010;15(1):21–32. PubMed doi:10.1177/1359105309342297 29. Amireault S, Godin G, Vézina-Im LA. Determinants of physical activity maintenance: a systematic review and meta-analyses. Health Psychol Rev. 2013;7(1):55–91.doi:10.1080/17437199.2012.701060 30. Netz Y, Raviv S. Age differences in motivational orientation toward physical activity: an application of social-cognitive theory. J Psychol. 2004;138:35–48. PubMed doi:10.3200/JRLP.138.1.35-48 31. Scholz U, Keller R, Perren S. Predicting behavioral intentions and physical exercise: a test of the health action process approach at the intrapersonal level. Health Psychol. 2009;28(6):702–708. PubMed doi:10.1037/a0016088 32. Sherwood NE, Jeffery RW. The behavioral determinants of exercise: implications for physical activity interventions. Annu Rev Nutr. 2000;20:21–44. PubMed doi:10.1146/annurev.nutr.20.1.21 33. Schwarzer R, Cao DS, Lippke S. Stage-matched minimal interventions to enhance physical activity in Chinese adolescents. J Adolesc Health. 2010;47(6):533–539. PubMed doi:10.1016/j.jadohealth.2010.03.015
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