Sage Journals: Discover world-class research

Abstract

Introduction: Volume of moderate-to-vigorous physical activity completed during the elementary school day is insufficient, and associated with health risks. Improvements in theory-based psychosocial factors might facilitate increased out-of-school physical activity. Methods: A behaviorally based after-school care protocol, Youth Fit 4 Life, was tested for its association with increased voluntary, out-of-school physical activity and improvements in its theory-based psychosocial predictors in 9- to 12-year-olds. Results: Increases over 12 weeks in out-of-school physical activity, and improvements in self-regulation for physical activity, exercise self-efficacy, and mood, were significantly greater in the Youth Fit 4 Life group (n = 88) when contrasted with a typical care control group (n = 57). Changes in the 3 psychosocial variables significantly mediated the group–physical activity change relationship (R² = .31, P < .001). Change in self-regulation was a significant independent mediator, and had a reciprocal relationship with change in out-of-school physical activity. In the Youth Fit 4 Life group, occurrence of 300 min/wk of overall physical activity increased from 41% to 71%. Conclusions: Targeting theory-based psychosocial changes within a structured after-school care physical activity program was associated with increases in children’s overall time being physically active. After replication, large scale application will be warranted.

Keywords

voluntary physical activity social cognitive youth psychological

Introduction

Youth in industrialized nations are less physically active than in previous decades.¹ This could lead to increases in health risks such as obesity, type 2 diabetes, cardiovascular disease, and skeletal issues.² Although World Health Organization and US recommendations are for children to complete ≥60 minutes of moderate or greater intensity physical activity/day,^3,4 the volume of ≥300 min/wk has been used in both population⁵ and intervention⁶ research as a target. Based on accelerometer data, only 42% of US children aged 6 to 11 years reached that amount.⁵ Intervention effects have been particularly weak in 8- to 12-year-olds.⁷

Although there is presently an increased focus on child obesity prevention, physical education does not typically facilitate large amounts of moderate-to-vigorous activity during class time,^8-10 and after-school care usually lacks a structure for adequately administering physical activity to all participants.¹¹ It was proposed that programs would benefit from accepted behavior-change theory being leveraged to administer physical activity to children, while also improving evidence-based psychological predictors of their voluntary physical activity outside of the school setting.¹²

There has been only sporadic theory-based research with children in this area^13,14; most frequently incorporating social cognitive and self-efficacy theory.^15,16 These paradigms view individuals as capable of managing environmental challenges through self-regulation, and being influenced by their perceptions of competence (ie, self-efficacy).^15,16 A related model focused on health behavior-change proposed that the psychological factors of physical self-concept, mood, coping, self-regulation, self-esteem, self-efficacy, and body image were predictors of increased physical activity.¹⁷ That model was subsequently extended to guide treatments.^18-21 Based on a summarization of associated research, it was proposed that improvements in the 3 factors of (1) usage of self-regulatory/self-management skills, (2) self-efficacy to maintain behavioral changes, and (3) mood would explain large portions of the variance in the prediction of increased physical activity, and could be impacted by interventions that are accordingly tailored.²⁰ Improved mood had been associated with even minimally increased volumes of physical activity in children.²²

The above propositions were recently adapted for a youth-focused curriculum (ie, Youth Fit 4 Life) in which after-school care time was structured to simultaneously improve those targeted psychosocial variables; maximize time in moderate-to-vigorous physical activity during the treatment; and increase voluntary, free-time physical activity outside of school. Its development was a collaboration of a community-based health promotion organization and a pediatric medical organization. Curriculum architects included a primary care physician, a health psychologist, health educators, exercise physiologists, registered dieticians, and health promotion administrators. Although this new protocol was associated with increased physical activity during elementary after-school care,²³ its ability to increase physical activity outside of the school setting was untested.

Thus, this study was designed to evaluate the Youth Fit 4 Life treatment’s association with increased out-of-school physical activity and improvement in its putative psychosocial predictors, as well as to determine if the proposed behavioral relationships from which it was based is viable in the age range of 9 to 12 years. The field research design that was used addressed the following hypotheses:

Hypothesis 1: The experimental after-school care treatment will be associated with greater improvements over 12 weeks in moderate-to-vigorous physical activity completed outside of school, self-regulation for physical activity, exercise self-efficacy, and overall negative mood than a control condition of typical after-school care processes.

Hypothesis 2: The relationship of treatment/control condition with changes over 12 weeks in out-of-school physical activity will be significantly mediated by changes in self-regulation, self-efficacy, and mood.

In addition, determining change in which of the psychosocial variables was the strongest mediator, and to what extent treatment effects related to the attainment of a total of 300 minutes of physical activity per week, was a concern.

Methods

Participants

Participants were 9- to 12-year-old enrollees of YMCA-managed after-school care in the southeastern United States. Written informed consent from each participant’s parent/legal guardian, and verbal assent from each participant was obtained. Sites randomized to the treatment condition had a higher number of participants, which yielded an unequal sample size among the treatment (n = 88) and control (n = 57) conditions. Independent t and χ² tests indicated no significant group difference in age (overall mean = 10.0 years; SD = 0.8), percentage of girls (overall 44.8%), race/ethnicity (overall 53.0% white, 40.0% African American, 4.8% Hispanic, 2.2% other), and body mass index (overall mean = 18.7 kg/m²; SD = 4.0) at baseline. Based on the location of their elementary schools, participants’ median family income was moderate at US$74 200 per year.

Measures

Guided by the extant research on administering behavioral self-report surveys to children,^24,25 the present set of instruments measuring physical activity, self-regulation, self-efficacy, and mood were modeled conceptually after previously validated self-reports intended for older ages (Table 1). However, their length and items were kept as brief and unambiguous as possible to minimize burden and increase accuracy with elementary school-age participants.²⁶ Pilot validity and reliability testing was completed prior to this investigation with a sample of 45, 9- to 11-year-olds. Because a meta-analysis of self-administered questionnaires indicated that the mean internal consistency (Cronbach’s α) for ages 9 to 10 was .65, and for ages 11 to 12 was .70,²⁷ based on the age range within this study the midpoint of .68 was set as the minimally acceptable internal consistency score. The traditional standard of ≥.70 was used for test-retest reliability (Table 1). Although accelerometers are a highly accurate measure of physical activity, and were used previously for the measurement of physical activity within an after-school care setting,²³ permission for use of this somewhat invasive measure was not granted by school administrators for this investigation. Therefore, a physical activity recall survey with acceptable reliability and validity for the present age range was instead incorporated.

Table 1.

Description and Validation Data for Study Measures.

Measure	Instrument Description	Reliability and Validity
Out-of-school, moderate-to-vigorous physical activity	This measure was adapted from the Godin Leisure-Time Exercise Questionnaire,²⁸ which was validated through correlations with accelerometry, peak volume of oxygen uptake, and heart rate results.^29-31 The first item required entry of number of days over the previous week (0-7) physical activities that “made you breathe hard” occurred (examples included running, swimming, soccer, and biking). Survey directions required exclusion of, “. . . physical activities completed in phys ed or after-school care.” The second item asks approximately how many minutes/bout the described exertion occurred (0-120 minutes). The 2 item responses were multiplied for an overall score	Test-retest reliability (1 week) was .75. Consistent with research on older participants,³⁰ correspondence of scores with accelerometer-measured physical activity outputs was significant (r = .42, P < .001)
Self-regulation for physical activity	Adapted from a previous scale,³² its 5 items assess the use of methods for self-regulation/self-management (eg, “I say positive things to myself about being physically active”). Responses ranged from 1 (never) to 4 (often), and were summed	Internal consistency was α = .69, and test-retest reliability over 1 week was .77. For the present sample, internal consistency was α = .71
Exercise self-efficacy	The 5 items of the abbreviated version of the Exercise Barriers Self-Efficacy Scale for Children³³ assessed self-efficacy beginning with the stem, “I am sure I can exercise most days of the week even if: . . .” Examples of item endings were, “I didn’t like the activity” and “exercise was not fun.” Response options ranged from 1 (not true) to 5 (definitely true), and were summed	Internal consistency was α = .78, and .77 for test-retest reliability over 1 week.³³ For the present sample, internal consistency was α = .75
Overall negative mood	An abbreviated version of the Total Mood Disturbance scale³⁴ was used. It is an aggregate measure of depression, tension, fatigue, confusion, anger, and vigor (eg, “worn-out,” “nervous,” “sad”). Responses ranged from 0 (not at all) to 5 (extremely), and were aggregated to obtain an overall score	Consistent with research on older participants,^34,35 each item loaded most significantly on the appropriate construct of the original Total Mood Disturbance scale (range = .60-.71). Test-retest reliability over 1 week was .72

Procedure

Institutional review board approval was obtained, and all research processes followed requirements of the Declaration of Helsinki. Existing after-school care counselors without previous training in physical education methods conducted treatment/control processes in either a gymnasium, all-purpose room, or outdoor area. For both the control and experimental conditions, the participant/counselor ratio was limited to 18:1, and 14 after-school sites were incorporated. According to after-school care policy, 45 min/d were dedicated to physical activity in both the treatment and control conditions.

During this allotted time within the control condition, physical activities were left mostly to the discretion of the after-school care counselor. Participants often had the option of being either being physically active or sedentary. Degree of participation in activities such as running or ball games varied based on counselors’ and participants’ interest in physical activity.

The structured Youth Fit 4 Life protocol (age 9-12 version) was administered 4 d/wk. Youth Fit 4 Life is described in more detail elsewhere,^23,36,37 and overviewed here. Completion of a 6-hour training, supported by a 311-page manual detailing each lesson, was required of counselors prior to administering the Youth Fit 4 Life protocol. The protocol’s segments include (1) a 5-minute warm-up, (2) 30 minutes of moderate-to-vigorous physical activity via structured games and tasks, and (3) 10 minutes of either the development of self-management/self-regulatory skills or nutrition education (on alternate days). Activities embedded in the protocol were intended to be especially inclusive of participants who were deconditioned and/or presently uninterested in sports or physical activities. The considerable attention given to self-management/self-regulatory skills were intended to foster self-efficacy through overcoming barriers to being more physically active and eating better. Instruction and practice in deep breathing and abbreviated muscle relaxation was also incorporated to help self-regulate tension, overall mood, and attentional focus. Personal goal setting, and continually assessing short-term goal progress (as opposed to competing with others), was a central treatment tenant. Periodic letters were sent/emailed to parents/guardians informing them of recent curriculum elements and how they could support their child in the physical activity and nutrition areas covered.

Trained wellness professionals administered surveys ≤5 days prior to study start (baseline), and at the end of the 12-week session. Structured fidelity checks were completed by study staff on approximately 10% of sessions, and any identified deviations from the protocol were promptly corrected primarily through interactions with the counselors.

Data Analyses

After first determining that data derived from the incorporated intention-to-treat format were missing at random,³⁸ and consistent with recent physical activity research for the present age range,³⁹ the expectation-maximization algorithm⁴⁰ was utilized for imputation of the 16% of missing cases. Both skewness and kurtosis values indicated an approximately normal distribution in the data.⁴¹ To detect a small-to-moderate effect of f² = .10 at the conservative statistical power of .90 (α = .05), a minimum of 145 participants total was required.⁴² Where a consistent directionality of scores was suggested by earlier related research,^20,22,43 statistical significance was set at α ≤ .05 (1-tailed). Otherwise, 2-tailed tests were used. Analyses were conducted using SPSS version 22 (IBM Corp, Armonk, NY).

Mixed model, repeated-measures analyses of variance were used to assess group differences in baseline to week 12 changes in all measures. This was followed up by dependent t tests to assess significance of within-group changes. For the effect sizes for Cohen’s d and partial eta-squared (), .20, .50, .80; and .01, .06, .14 denote small, moderate, and large effects, respectively. As suggested for these conditions,⁴⁴ change scores from baseline to week 12 were unadjusted for baseline values.

Multiple mediation analysis (Figure 1), incorporating 20 000 bootstrapped resamples and controlling for sex and baseline scores,⁴⁵ assessed mediation of the relationship between treatment type (ie, treatment vs control) and change in out-of-school physical activity by changes in self-regulation, self-efficacy, and mood. Statistical significance of mediation is identified when its corresponding upper and lower confidence interval does not include 0. Where a simultaneously entered mediator was found to be significant, paired single mediation analyses (Figure 1) followed. A reciprocal, mutually reinforcing, relationship is identified if, after reversing the positions of the outcome and mediator variables, both equations demonstrate significant mediation.⁴⁶

Figure 1.

Relationships among variables within mediation analyses.

Results

There was no significant group difference on any study variable at baseline. There was a significant time × group difference in out-of-school physical activity, F(1, 143) = 36.35, P < .001, ηp2= .20; self-regulation for physical activity, F(1, 143) = 13.05, P < .001, ηp2 = .08; exercise self-efficacy, F(1, 143) = 4.89, P = .015, ηp2 = .03; and negative mood, F(1, 143) = 10.17, P = .001, ηp2 = .07, that was more favorable in the treatment group. Only the treatment group demonstrated significant improvements, which were found in each of the study variables (Table 2).

Table 2.

Changes in Study Measures Over 12 Weeks.

Measure	Baseline		Week 12		Change From Baseline to Week 12
	Mean	SD	Mean	SD	Mean	SD	t	P	d ^a
Out-of-school physical activity
Treatment group^b	226.19	197.11	360.00	255.44	133.81	238.16	5.27	<.001	0.68
Control group^c	276.32	170.22	186.32	179.60	−90.00	183.36	−3.71	<.001	−0.53
Self-regulation for physical activity
Treatment group	17.19	2.24	18.00	2.35	0.81	2.30	3.29	.001	0.36
Control group	17.25	2.18	16.86	2.26	−0.39	1.18	−2.48	.016	−0.18
Exercise barriers self-efficacy
Treatment group	16.77	5.11	17.94	4.53	1.17	5.06	2.17	.033	0.23
Control group	16.53	4.84	15.67	5.36	−0.86	5.89	−1.10	.275	−0.18
Overall negative mood
Treatment group	6.59	3.61	5.38	3.79	−1.22	3.47	−3.29	.001	0.38
Control group	5.44	3.64	6.04	3.36	0.57	3.13	1.44	.156	−0.16

Cohen’s effect size d for within-group change (mean_{week 12} − mean_baseline)/SD_baseline.

Treatment group n = 88 (df = 87).

Control group n = 57 (df = 56).

Changes over 12 weeks in self-regulation, self-efficacy, and mood significantly mediated the group–physical activity relationship, R² = .31, F(8, 136) = 7.62, P < .001 (Table 3). Only change in self-regulation was a significant independent mediator. In the planned follow-up analysis, change in self-regulation significantly mediated the treatment–physical activity change relationship, R² = .30, F(4, 140) = 15.16, P < .001, and change in physical activity significantly mediated the treatment–self-regulation change relationship, R² = .33, F(4, 140) = 16.87, P < .001 (Table 3). Thus, changes in self-regulation and out-of-school physical activity had a significant reciprocal, mutually reinforcing, relationship.

Table 3.

Results From Multiple Mediation and Reciprocal Effects Analyses (N = 145).^a

Predictor	Mediator	Outcome	Path a; β (SE)	Path b; β (SE)	Path c; β (SE)	Path c′; β (SE)	Indirect effect; β (SE), 95% CI (1-Tailed)
Multiple mediation analysis (simultaneous entry of mediators)
Treatment	Δ Self-regulation	Δ Physical activity	1.13 (0.31)^†	44.20 (11.57)^†	222.42 (38.27)^†	171.96 (38.27)^†	49.85 (23.32), 21.23 to 94.56
Treatment	Δ Self-efficacy	Δ Physical activity	2.27 (0.78)**	2.61 (4.22)	222.42 (38.27)^†	171.96 (38.27)^†	5.94 (11.34), −8.28 to 30.09
Treatment	Δ Negative mood	Δ Physical activity	−1.29 (0.52)*	4.13 (6.52)	222.42 (38.27)^†	171.96 (38.27)^†	−5.32 (9.23), −24.65 to 8.18
Single mediation tests for reciprocal effects
Treatment	Δ Self-regulation	Δ Physical activity	1.08 (0.30)^†	42.87 (9.88)^†	225.24 (37.35)^†	178.92 (36.78)^†	46.32 (19.38) 21.52 to 82.55
Treatment	Δ Physical activity	Δ Self-regulation	225.24 (37.35)^†	0.003 (0.001)^†	1.08 (0.30)^†	0.46 (0.32)	0.62 (0.15), 0.40 to 0.88

Δ = change from baseline to week 12. Path a, predictor → mediator; Path b, mediator → outcome; Path c, predictor → outcome; Path c′, predictor → outcome, controlling for the mediator.

P < .05; **P < .01; ^†P < .001.

Based on evidence regarding (1) the state policy for minutes per week that each participant received physical education,⁴⁷ (2) the portion of that time being in moderate or higher physical activity,^8,10 (3) min per week of moderate-to-vigorous physical activity associated with after-school care using either the Youth Fit 4 Life or typical-care condition,²³ and (4) the presently measured volume of out-of-school physical activity; attainment of ≥300 min/wk of physical activity in the treatment group increased from 40.9% (baseline) to 70.5% (week 12). It was marginally reduced in the control group.

Discussion

Findings supported both hypotheses. The Youth Fit 4 Life treatment, but not the control, was associated with a significant improvement over 12 weeks in voluntary, out-of-school physical activity. Also, only the treatment group demonstrated significant improvements in the targeted theory-based psychosocial variables of self-regulation for physical activity, exercise self-efficacy, and mood. In agreement with research on adults,²⁰ the relationship of treatment/control condition with changes in out-of-school physical activity was significantly mediated by changes in self-regulation, self-efficacy, and mood. Also consistent with previous research,²⁰ change in the use of self-regulatory skills was the strongest mediator within a multiple mediation analysis. The finding that the significant increases in self-regulation and physical activity reinforced one another in a reciprocal manner affirmed the importance that was placed on self-regulatory skill building within the Youth Fit 4 Life treatment.

The small but significant reduction in self-regulation in the control group might indicate that for children who are overwhelmed by a competitive environment—one in which their skills do not match perceived challenges/capabilities—their attempts at self-management became diminished through discouragement. Further research on this premise, how it might impact confidence to be physically active at these and other times and places throughout the lifespan, and what practitioners could do to counter that situation, is warranted. Within the Youth Fit 4 Life treatment, the practice of competing primarily with one’s self (through short-term goal setting and progress feedback) might encourage self-regulation because it fosters perceptions of incremental progress, self-efficacy, and motivation to persevere.

Only the Youth Fit 4 Life group was associated with an increase in frequency of participants attaining the total of 300 min/wk of moderate or greater intensity physical activity. This improvement was noteworthy. The 41% baseline frequency closely corresponded to the 42% of children aged 6 to 11 years attaining the ≥300 min/wk in US population–based research.⁵ Given that congruity, a similar demographic profile within this and that research,⁵ and a comparable operational structure across most after-school care programs, generalizability of the present findings was supported within the present difficult-to-change age group.⁷

Limitations also require consideration. For example, although pilot findings were acceptable, the present surveys require more thorough validation research. As was suggested above, accelerometry will be a more objective measure of physical activity in extensions of this research. Although attempts were made to mask the goals of this study, the high level of treatment structure might have biased survey responses through expectation effects. While the high structure of the treatment fostered experimental control, there still might have been some nesting effects related to instructor characteristics that should be better accounted for in larger-sample replications. Also, the inclusion of follow-up analyses is required to evaluate retention of effects. It is suggested that the present foci of research be extended to account for sex, additional age ranges (eg, very young, adolescents), and ethnic and socioeconomic subgroups because conditions for increasing physical activity within each might differ.⁴⁸

Summary

Although within-school and after-school physical activity interventions are commonly suggested to address physical inactivity in children,^1,49 attainment of recommended volumes^3,4 remains exceedingly low.⁵ Improving children’s abilities to overcome personal and environmental challenges, and feelings of competence around being more physically active, is essential for increasing their overall physical activity—especially for those at most risk for inactivity and its associated health problems. The present evidence-based treatment, Youth Fit 4 Life, demonstrated promise for both supplying moderate-to-vigorous physical activity during class time and inducing free-time activity in 9- to 12-year-olds. With replication, extension, and, ultimately, wide spread application through both the community health and medical communities, positive impacts on the present and future health of many children could be considerable.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

James J. Annesi, PhD, is director of Wellness Advancement with the YMCA of Metro Atlanta, and Professor of Health Promotion at Kennesaw State University. His research program incorporates health behavior-change theory into community-based treatment development related to exercise adherence, weight management, and psychosocial and self-image improvements across age ranges.

Stephanie M. Walsh, MD, is medical director of Child Wellness at Children’s Healthcare of Atlanta, and Associate Professor of Surgery and Pediatrics at Emory University’s School of Medicine. Her research is on methods for the prevention and treatment of childhood obesity both within clinical pediatric practices and throughout the community.

Brittney L. Greenwood, is a health educator with the YMCA of Metro Atlanta specializing in community-based applications of evidence-based treatments.

References

Smith

Biddle

SJH

, eds. Youth Physical Activity and Sedentary Behavior. Champaign, IL: Human Kinetics; 2008.

Magarey

Daniels

Boulton

Cockington

. Predicting obesity in early adulthood from childhood and parental obesity. Int J Obes Relat Metab Disord. 2003;57:505-513.

US Department of Health and Human Services. Physical Activity Guidelines for Americans Midcourse Report: Strategies to Increase Physical Activity Among Youth. Washington, DC: US Department of Health and Human Services; 2012. http://health.gov/paguidelines/midcourse/pag-mid-course-report-final.pdf. Accessed June 3, 2016.

World Health Organization. Global Strategy on Diet, Physical Activity and Health: Physical Activity and Young People. Geneva, Switzerland: World Health Organization; 2016. http://www.who.int/dietphysicalactivity/factsheet_young_people/en/. Accessed June 3, 2016.

Troiano

Berrigan

Dodd

Mâsse

Tilert

McDowell

. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40:181-188.

Iverson

Nigg

Titchenal

. The impact of elementary after-school nutrition and physical activity program on children’s fruit and vegetable intake, physical activity, and body mass index: Fun 5. Hawaii Med J. 2011;70(suppl 1):37-41.

Stice

Shaw

Marti

. A meta-analytic review of obesity prevention programs for children and adolescents. Psychol Bull. 2006;132:667-691.

US Department of Health and Human Services. Strategies to Improve the Quality of Physical Education. Washington, DC: US Department of Health and Human Services; 2010. http://www.cdc.gov/healthyschools/pecat/quality_pe.pdf. Accessed June 3, 2016.

National Association for Sport and Physical Education. 2012 Shape of the Nation Report: Status of Physical Education in the USA, 2012. Reston, VA: American Alliance for Health, Physical Education, Recreation and Dance. http://www.shapeamerica.org/advocacy/son/2012/upload/2012-shape-of-nation-full-report-web.pdf. Accessed June 3, 2016.

10.

Tudor-Locke

Lee

Morgan

Beighle

Pangrazi

. Children’s pedometer-determined physical activity during the segmented school day. Med Sci Sports Exerc. 2006;38:1732-1738.

11.

Beets

Huberty

Beighle

. Physical activity of children attending afterschool programs: research- and practice-based implications. Am J Prev Med. 2012;42:180-184.

12.

Beets

Beighle

Erwin

Huberty

. After-school program impact on physical activity and fitness. Am J Prev Med. 2009;6:527-537.

13.

Atkin

Gorely

Biddle

SJH

Cavill

Foster

. Interventions to promote physical activity in young people conducted in the hours immediately after school: a systematic review. Int J Behav Med. 2011;18:176-187.

14.

Brown

Hume

Pearson

Salmon

. A systematic review of intervention effects on potential mediators of children’s physical activity. BMC Public Health. 2013;13:165. http://www.biomedcentral.com/1471-2458/13/165. Accessed June 3, 2016.

15.

Bandura

. Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, NJ: Prentice Hall; 1986.

16.

Bandura

. Self-efficacy: The Exercise of Control. New York, NY: Freeman; 1997.

17.

Baker

Brownell

. Physical activity and maintenance of weight loss: physiological and psychological mechanisms. In Bouchard

, ed. Physical Activity and Obesity. Champaign, IL: Human Kinetics; 2000:311-328.

18.

Teixeira

Silva

Coutinho

. Mediators of weight loss and weight loss maintenance in middle-aged women. Obesity (Silver Spring). 2010;18:725-735.

19.

Hankonen

Absetz

Haukkala

Uutela

. Socioeconomic status and psychosocial mechanisms of lifestyle change in a type 2 diabetes prevention trial. Ann Behav Med. 2009;38:160-165.

20.

Annesi

. Supported exercise improves controlled eating and weight through its effects on psychosocial factors: extending a systematic research program toward treatment development. Perm J. 2012;16(1):7-18.

21.

Annesi

Unruh

Marti

Gorjala

Tennant

. Effects of The Coach Approach intervention on adherence to exercise in obese women: assessing mediation of social cognitive theory factors. Res Q Exerc Sport. 2011;82:99-108.

22.

Annesi

. Correlations of depression and total mood disturbance with physical activity and self-concept in preadolescents enrolled in an after-school exercise program. Psychol Rep. 2005;96:891-898.

23.

Annesi

Vaughn

. Evidence-based referral: effects of the revised “Youth Fit 4 Life” protocol on physical activity outputs. Perm J. 2015;19(3):48-53.

24.

Borgers

de Leeuw

Hox

. Children as respondents in survey research: cognitive development and response quality. Bull Sociol Methodol. 2000;66:60-75.

25.

Saklofske

Reynolds

Schwean

, eds. The Oxford Handbook of Child Assessment. New York, NY: Oxford University Press; 2013.

26.

Holoday

Turner-Henson

. Response effects in surveys with school-age children. Nurs Res. 1989;38:248-250.

27.

Borgers

de Leeuw

Hox

. Surveying children: cognitive development and response quality in questionnaire research. In: Christianson

, ed. Official Statistics in a Changing World: Proceedings of the 3rd International Conference on Methodological Issues in Official Statistics. Stockholm, Sweden: SCB; 1999:99-140.

28.

Godin

. The Godin-Shephard Leisure-Time Physical Activity Questionnaire. Health Fitness J Can. 2011;4:18-22.

29.

Jacobs

Ainsworth

Hartman

Leon

. A simultaneous evaluation of 10 commonly used physical activity questionnaires. Med Sci Sports Exerc. 1993;25:81-91.

30.

Miller

Freedson

Kline

. Comparison of activity levels using Caltrac accelerometer and five questionnaires. Med Sci Sport Exerc. 1994;26:376-382.

31.

Sallis

Buono

Roby

Micale

Nelson

. Seven-day recall and other physical activity self-reports in children and adolescents. Med Sci Sports Exerc. 1993;25:99-108.

32.

Saelens

Gehrman

Sallis

Calfas

Sarkin

Caparosa

. Use of self-management strategies in a 2-year cognitive-behavioral intervention to promote physical activity. Behav Ther. 2000;31:365-379.

33.

Annesi

Westcott

Faigenbaum

Unruh

. Effects of a 12-week physical activity protocol delivered by YMCA after-school counselors (Youth Fit For Life) on fitness and self-efficacy changes in 5-12-year-old boys and girls. Res Q Exerc Sport. 2005;76:468-476.

34.

McNair

Heuchert

. Profile of Mood States Technical Update. North Tonawanda, NY: Multi-Health Systems; 2009.

35.

Terry

Lane

Keohane

. Development and validation of a mood measure for adolescents. J Sports Sci. 1999;17:861-872.

36.

Annesi

Smith

Walsh

Mareno

Smith

. Effects of an after-school care-administered physical activity and nutrition protocol on body mass index, fitness levels, and targeted psychological factors in 5- to 8-year-olds. Transl Behav Med. 2016;6:347.

37.

National Cancer Institute. Research-tested Intervention Programs (RTIPs): Youth Fit 4 Life. http://rtips.cancer.gov/rtips/programDetails.do?programId=24604970. Accessed June 3, 2016.

38.

Allison

. Missing Data. Thousand Oaks, CA: Sage; 2002.

39.

Rhodes

Spence

Berry

. Understanding action control of parental support behavior for child physical activity. Health Psychol. 2016;35:131-140.

40.

Schafer

Graham

. Missing data: our view of the state of the art. Psychol Methods. 2002;7:147-177.

41.

Kline

. Principles and Practice of Structural Equation Modeling. New York, NY: Guilford Press; 2011.

42.

Cohen

West

Aiken

. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. 3rd ed. Mahwah, NJ: Erlbaum; 2003.

43.

Annesi

. Relations of physical self-concept and self-efficacy with frequency of voluntary physical activity in preadolescents: implications for after-school care programming. J Psychosom Res. 2006;61:515-520.

44.

Glymour

Weuve

Berkman

Kawachi

Robins

. When is baseline adjustment useful in analyses of change? An example with education and cognitive change. Am J Epidemiol. 2005;162:267-278.

45.

Preacher

Hayes

. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav Res Methods. 2008;40:879-891.

46.

Palmeira

Markland

Silva

. Reciprocal effects among changes in weight, body image, and other psychological factors during behavioral obesity treatment: a mediation analysis. Int J Behav Nutr Phys Act. 2009;6:9. http://www.ijbnpa.org/content/pdf/1479-5868-6-9.pdf. Accessed June 3, 2016.

47.

Kibbe

Campos

Khalaf

. Physical Activity and Nutrition Toolkit for Georgia Public Schools and School Districts. Atlanta, GA: Georgia Health Policy Center; 2014. https://dph.georgia.gov/sites/dph.georgia.gov/files/PAN_toolkit_2.pdf. Accessed June 3, 2016.

48.

Malina

. Biocultural factors in developing physical activity levels. In: Smith

Biddle

SJH

, eds. Youth Physical Activity and Sedentary Behavior. Champaign, IL: Human Kinetics; 2008:141-166.

49.

Branscum

Sharma

. After-school based obesity prevention intervention: a comprehensive review of the literature. Int J Environ Res Public Health. 2012;9:1438-1457.

Increasing Children’s Voluntary Physical Activity Outside of School Hours Through Targeting Social Cognitive Theory Variables

Abstract

Keywords

Introduction

Methods

Participants

Measures

Procedure

Data Analyses

Results

Discussion

Summary

Footnotes

Declaration of Conflicting Interests

Funding

Author Biographies

References