PCD logo

Physical Activity–Related Policy and Environmental Strategies to Prevent Obesity in Rural Communities: A Systematic Review of the Literature, 2002–2013

SYSTEMATIC REVIEW — Volume 13 — January 7, 2016

Related Pages

M. Renée Umstattd Meyer, PhD, MCHES; Cynthia K. Perry, PhD, FNP-BC; Jasmin C. Sumrall, MPH; Megan S. Patterson, MPH; Shana M. Walsh, MEd; Stephanie C. Clendennen, MPH; Steven P. Hooker, PhD; Kelly R. Evenson, PhD, MS; Karin V. Goins, MPH; Katie M. Heinrich, PhD; Nancy O’Hara Tompkins, PhD; Amy A. Eyler, PhD, CHES; Sydney Jones, MSPH; Rachel Tabak, PhD, RD; Cheryl Valko, MPH, RD

Suggested citation for this article: Umstattd Meyer MR, Perry CK, Sumrall JC, Patterson MS, Walsh SM, Clendennen SC, et al. Physical Activity–Related Policy and Environmental Strategies to Prevent Obesity in Rural Communities: A Systematic Review of the Literature, 2002–2013. Prev Chronic Dis 2016;13:150406. DOI: http://dx.doi.org/10.5888/pcd13.150406external icon.

PEER REVIEWED

PEER REVIEWED

On This Page

Abstract

Introduction

Health disparities exist between rural and urban residents; in particular, rural residents have higher rates of chronic diseases and obesity. Evidence supports the effectiveness of policy and environmental strategies to prevent obesity and promote health equity. In 2009, the Centers for Disease Control and Prevention recommended 24 policy and environmental strategies for use by local communities: the Common Community Measures for Obesity Prevention (COCOMO); 12 strategies focus on physical activity. This review was conducted to synthesize evidence on the implementation, relevance, and effectiveness of physical activity–related policy and environmental strategies for obesity prevention in rural communities.

Methods

A literature search was conducted in PubMed, PsycINFO, Web of Science, CINHAL, and PAIS databases for articles published from 2002 through May 2013 that reported findings from physical activity–related policy or environmental interventions conducted in the United States or Canada. Each article was extracted independently by 2 researchers.

Results

Of 2,002 articles, 30 articles representing 26 distinct studies met inclusion criteria. Schools were the most common setting (n = 18 studies). COCOMO strategies were applied in rural communities in 22 studies; the 2 most common COCOMO strategies were “enhance infrastructure supporting walking” (n = 11) and “increase opportunities for extracurricular physical activity” (n = 9). Most studies (n = 21) applied at least one of 8 non-COCOMO strategies; the most common was increasing physical activity opportunities at school outside of physical education (n = 8). Only 14 studies measured or reported physical activity outcomes (10 studies solely used self-report); 10 reported positive changes.

Conclusion

Seven of the 12 COCOMO physical activity–related strategies were successfully implemented in 2 or more studies, suggesting that these 7 strategies are relevant in rural communities and the other 5 might be less applicable in rural communities. Further research using robust study designs and measurement is needed to better ascertain implementation success and effectiveness of COCOMO and non-COCOMO strategies in rural communities.

Top

Introduction

Rural residents have higher rates of chronic diseases and obesity than urban residents (1–5). Rural residents may have as much as 6.2% higher prevalence of obesity than urban residents (6,7). Physical inactivity is associated with higher rates of chronic diseases and obesity (7,8), and some research suggests that rural residents are less physically active than urban residents (9–11). Rural residents also have higher rates of poverty, fewer community resources, less access to preventive services and health care, greater geographic dispersion, and more transportation challenges (eg, lack of public transit, greater travel distance) than urban residents (12–18). Sixteen percent of Americans live in rural areas that encompass 72% of land in the United States. Although evidence supports the effectiveness of policy and environmental strategies to prevent obesity and promote health equity, much of this evidence is derived from nonrural settings (13,19,20).

In 2009, the Centers for Disease Control and Prevention (CDC) recommended 24 strategies for local communities to use in planning and monitoring obesity-related policy and environmental changes using preexisting data sources: the Common Community Measures for Obesity Prevention (COCOMO) (21). Twelve strategies focus on physical activity (PA) (Table 1): 4 strategies to “encourage physical activity or limit sedentary activity among children and youth” (strategy nos. 12–15) and 8 strategies to “create safe communities that support physical activity” (strategy nos. 16–23). The purpose of this study was to conduct a systematic literature review of the implementation, relevance, and effectiveness of physical activity–related policy and environmental strategies for obesity prevention in rural communities, including both COCOMO and non-COCOMO approaches. A secondary aim was to synthesize the evidence on the implementation success of the 12 physical activity–related COCOMO strategies in rural communities.

Top

Methods

Data sources

A literature search was conducted in the following databases: PubMed, PsycINFO, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINHAL), and Public Affairs Information Service (PAIS). The search included articles published in English from 2002 through May 2013 and focused on findings from PA–related policy or environmental interventions. Each search used the following terms: “rural” AND “physical activity or exercise or sedentary or inactivity” AND “community or environment or policy.” Searches were repeated in a secondary literature search using search terms for Native American communities (“tribal” OR “reservation” OR “Native American” OR “indigenous”) and predominantly rural states. “Predominantly rural states” were identified where 1) most (half or greater) of the state was identified as rural using the Rural-to-Urban Continuum Codes and the Office of Management and Budget maps or 2) substantial portions of a state were identified as frontier using the Rural Assistance Center’s Frontier map (22,23). The following states were designated as predominantly rural: Alaska, Idaho, Kansas, Maine, Mississippi, Montana, Nebraska, Nevada, New Hampshire, New Mexico, North Dakota, Oklahoma, Oregon, South Dakota, Texas, Utah, Vermont, West Virginia, and Wyoming. Relevant references cited in each identified study were also included in the secondary literature search. Methods mirrored a companion literature review describing application of nutrition-related COCOMO strategies in rural communities (24).

Study selection

At least 2 researchers reviewed titles, abstracts, and complete texts of articles for inclusion. Studies were included that reported findings from empirical formative, process, or outcome research with strategies aimed at changing policy or environments to support PA in rural US or Canada communities. Publications were excluded if 1) both rural and urban communities were included and rural-specific findings were not reported, 2) the primary focus was on instrument development or individual-level behavioral change, or 3) studies were descriptive or did not describe an intervention.

Data extraction

The article extraction team consisted of 18 trained researchers. Data for each article were extracted independently by 2 trained researchers. We used a customized Qualtrics (Qualtrics LLC) online survey as a tool to extract information about study setting, geographic location, sample characteristics, rural definition, design, methods, results, and bias-risk assessment (25–27). After independent extraction, results were compared and discrepancies were resolved by consensus. Study quality was examined for randomized control trials (RCTs) using Cochrane Collaboration’s assessment tool. We used GRADE guidelines of bias risk for observational studies to assess non-RCTs, including formative studies, because the Cochrane tool focuses only on RCTs (25–27). The Cochrane tool assesses risk of bias across 6 categories: sequence generation, allocation concealment, blinding, incomplete outcome data, selective outcome reporting, and other sources of bias (25,26); all categories were assessed as designed. GRADE guidelines assess risk of bias across 4 categories: appropriate eligibility criteria, measurement of exposure and outcome, control of confounding, and incomplete follow-up (27); all categories were assessed as designed. Risk of bias was rated as low (score of 1), high (score of 0), or unclear (score of 0) for each Cochrane or GRADE category based on study type (25); overall summary scores for bias risk were calculated and categorized as low, medium, or high (RCTs: low risk = 5 or 6, medium risk = 2–4, and high risk = 0 or 1; non-RCTs: low risk = 3 or 4, medium risk = 2, and high risk = 0 or 1). Extraction data entered into Qualtrics were downloaded into Excel for synthesis. We organized data into the following categories: 1) study location, setting, approach, and bias-risk assessment; 2) COCOMO strategies used; 3) non-COCOMO strategies used; 4) measurement of policy and environmental strategies; and 5) intervention effects on policy, environment, behavioral, and health outcomes.

Top

Results

Searches returned 9,879 articles, of which 2,002 were identified as relevant for further screening based on title and abstract. Duplicates were removed, leaving 488 records for full-text screening; 443 of these did not meet inclusion criteria. The remaining 45 articles represented 41 distinct studies and were assigned for data extraction; 15 articles were excluded during extraction for various reasons (Figure). Thus, 30 articles representing 26 distinct studies were included in the final synthesis.

Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) flow diagram for study inclusion in a systematic review of physical activity–related policy and environmental strategies for obesity prevention in rural communities. Abbreviations: CINAHL, Cumulative Index to Nursing and Allied Health Literature; PAIS, Public Affairs Information Service; COCOMO, Common Community Measures for Obesity Prevention (21).

 

Figure. Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) flow diagram for study inclusion in a systematic review of physical activity–related policy and environmental strategies for obesity prevention in rural communities. Abbreviations: CINAHL, Cumulative Index to Nursing and Allied Health Literature; PAIS, Public Affairs Information Service; COCOMO, Common Community Measures for Obesity Prevention (21). [A text description of this figure is also available.]

Study location, setting, approach, and bias-risk assessment

Of the 26 studies, 3 were conducted in Canada and 23 in the United States; 4 studies were conducted with American Indian tribes or First Nations of Canada ( Table 2). Rural location of 19 studies was defined by authors as “rural,” “small town,” or “remote”; 4 studies provided information about population density to define rurality, and 3 were identified as rural only through descriptions of tribal or reservation areas. Study settings included schools (n = 18), communities (defined broadly without identification of an entity, organization, or institution; n = 12), worksites (n = 5), churches (n = 1), homes (n = 2), and childcare (n = 1); 5 interventions targeted multiple settings. In the 18 school-setting studies, interventions resulted in changes that affected students (n = 14), changes in the use of facilities for the community (n = 3), or changes that affected employees (n = 1). Study designs included formative (n = 7), process (n = 16), or outcome (n = 20) evaluations; 15 included 2 or more types of evaluation. Only 3 studies were RCTs. Bias-risk assessments showed that 19 studies had high risk of bias, 4 studies had medium risk, and 3 studies had low risk ( Table 3). None of the 23 non-RCTs adequately controlled for confounding, and 5 non-RCTs had flawed measurement. Six non-RCTs developed and applied appropriate eligibility criteria, and 7 non-RCTs had complete follow-up. Of the 3 RCTs, one had medium risk and 2 had high risk of bias (Table 3). Sequence generation was absent in all RCTs, and all reported selective outcome data and had other sources of bias.

Use of COCOMO strategies

Although only one study referenced CDC’s COCOMO strategies (48), 22 of the 26 studies applied at least one PA-related COCOMO strategy (Table 2), and 4 studies did not apply any COCOMO strategies. The mean number of COCOMO strategies applied was 2.0 (standard deviation [SD], 2.3; range, 0–12). The 2 most commonly applied COCOMO strategies were no. 18, “enhance infrastructure supporting walking” (n = 11), and no. 14, “increase opportunities for extracurricular physical activity” (n = 9). Fourteen studies applied at least one COCOMO strategy to “encourage physical activity or limit sedentary activity among children and youth,” 12 studies applied at least one COCOMO strategy to “create safe communities that support physical activity,” and 4 studies applied at least one of each. Identified barriers to implementing these strategies in rural areas were staff turnover and lack of staff buy-in, organizational or community support, resources, and political will. Identified facilitators were communication, accountability, training, and ease of implementation.

Use of non-COCOMO strategies

One or more non-COCOMO strategies were mentioned in 21 studies (Table 2). Four studies incorporated only non-COCOMO strategies and 6 studies incorporated 2 or more non-COCOMO strategies. The mean number of non-COCOMO strategies applied was 1.1 (SD, 0.8). Eight non-COCOMO strategies were identified: increase PA opportunities at school outside of physical education (PE) (n = 8) (eg, classroom activity breaks, longer school recess, reversing lunch and recess); increase amount of and access to PA equipment or improve existing equipment resources (n = 6); promote PA resources (n = 6) (eg, signs to promote walking routes or trails); provide access to public buildings after hours for PA purposes (n = 3); adopt worksite policies or practices (n = 3) (eg, allowing PA breaks during workday); reduce home screen time (n = 1); reduce school or preschool sedentary time (n = 1); and school district-wide adoption of a PA-supportive curriculum (n = 1). The mean number of COCOMO and non-COCOMO strategies applied per study was 3.1 (SD, 2.3).

Measurement of policy and environmental strategies

Measurement of policy or environmental changes was not consistent or standard across studies, and researchers often did not use COCOMO-suggested measurements (Table 3). For example, studies (n = 6) that “increased the amount of physical activity in PE programs in schools” (strategy no. 13) documented results by indicating use of a modified PE program, increased minutes in PE or increased time in PA during PE (29–34,43,53,57). Some studies used a similar non-COCOMO metric to measure change. For example, the 8 studies that “increased opportunities of extracurricular physical activity” (strategy no. 14) measured change by indicating the presence of increased opportunities for PA (28,39,42,43,45,47,53–55).

Intervention effects on policy, environment, behavioral, and health outcomes

Sixteen interventions had at least one positive environmental change or result, and 18 interventions reported a positive policy change or result (Table 3). Three studies focused solely on formative approaches without reporting policy or environmental results ( Table 4). Seven of the 8 nonformative studies that “required PE” (strategy no. 12) or “increased amount of physical activity in PE programs” (strategy no. 13) reported a positive PE policy or environmental change (Table 3). All 3 studies that adopted worksite policies promoting PA documented policy implementation, and 2 studies measured improvements in health status (28,38).

PA changes were reported in 14 studies with mixed results; 10 studies reported successfully implementing policy or environmental changes and positive changes in PA. However, PA measurement methods varied across studies, and only 4 studies reported significant positive changes in PA (40,49,51,52). COCOMO strategies were applied in 9 studies reporting increases in PA; 2 studies used pedometers (51,52), 1 study used a combination of self-report and accelerometry (31–34), and 7 studies used self-report or proxy self-report (28,36,38,40,49,50,56). These 9 studies reporting positive changes in PA used at least one of these 5 COCOMO strategies: no. 12, “require PE in schools” (31-34); no. 13, “increase amount of physical activity in PE programs” (31-34); no. 14, “increase opportunities for extracurricular physical activity” (28); no. 15, “reduce screen time in public venues” (36); no. 16, “improve access to outdoor recreation facilities” (51,52,56); and no. 18, “enhance infrastructure for walking” (38,40,49,51,52,56). All but one (56) of these 9 studies also implemented at least one non-COCOMO strategy (28,31–34,36,38,40,49,51,52,): increase amount of and access to PA equipment or improve existing equipment resources (36,38); promote PA resources (38,40,49); adopt worksite policies or practices (28,38); improve access to public buildings after hours for PA purposes (40); reduce sedentary time in school or preschool settings (36); and increase PA opportunities at school outside of PE (31–34,51,52). One of the 10 studies reporting positive PA results implemented only a non-COCOMO strategy: provide access to public buildings after hours for PA purposes (50). Four studies found no change in PA; one of these used both self-report and a subset of accelerometer data (54,55), and 3 studies used self-report or proxy self-report (47,53,57). Of these 4 studies, one used COCOMO strategy no. 12, “require PE in schools” (54,55), 2 studies used strategy no. 13, “increase amount of physical activity in PE programs” (53,57), 3 studies used strategy no. 14, “increase opportunities for extracurricular physical activity”(47,53–55), and one used strategy no. 15, “reduce screen time in public service venues” (53). All but one (57) of these studies also implemented at least one non-COCOMO strategy; one study increased amount of and access to PA equipment or improved existing equipment resources (53), 3 studies increased PA opportunities at school outside of PE (47,53–55), and one reduced home screen time (53).

Top

Discussion

We found 26 unique studies that implemented PA-related COCOMO or non-COCOMO strategies in rural communities. Given the variation in settings, methods, and results of the studies reviewed, we were unable to empirically assess effectiveness; however, these findings provide a synthesis of current practices and guidance on implementing policy and environmental strategies in rural communities.

Seven of the 12 PA-related COCOMO strategies (nos. 12–18) were applied in 2 or more nonformative studies, suggesting that these strategies are relevant in rural communities. All but 2 studies (29,47) that used these 7 strategies reported effectively implementing them in the target rural communities. Ten studies reported improvements in PA after implementation of policy or environmental changes. “Enhancing infrastructure supporting walking” (no. 18) was implemented in 6 of these 8 studies, with significant changes in 4 studies. However, because 5 of these 6 studies implemented more than one strategy, we cannot attribute the improvement in PA to this strategy alone. Three of the COCOMO strategies were not implemented in any of the reviewed studies, and 2 strategies were implemented in only one study each, suggesting these strategies might be less relevant for rural communities, as originally cautioned when the guidelines were released (21). These strategies relate to location of schools, improvement of public transportation, mixed-use zoning, enhanced personal safety, and traffic safety in areas where people could be physically active. Rural communities may not use these strategies because they lack the resources to implement these strategies or because of other constraints related to small and dispersed populations in comparison with urban and suburban communities. For example, many rural areas have limited or no public transportation systems (59,60) and may not have the tax base or concentration of users to make a public transportation system feasible (61). The studies reviewed implemented 8 non-COCOMO strategies. Although these strategies may not be germane to rural areas only, they have been implemented in rural areas; more research on their effectiveness in rural areas is warranted. Given the increase in policy and environmental approaches for improving PA after the publication of the COCOMO strategies in 2009, our review is beneficial to the field and indicates it may be an opportune time to update PA-related COCOMO strategies (21).

Several relevant studies were published after this review was conducted. A search of literature published from June 2013 through October 2015 found 6 additional articles representing 5 interventions that implemented PA-related policy or environmental strategies in rural areas. Four studies focused on school or preschool children or facilities (62–66); one focused on both the community-at-large and schools (67); none were RCTs; PA levels increased in 3 studies. PA was measured by using pedometers (65,66), direct observation (63), or through a coalition member’s report (67). Four interventions implemented COCOMO strategies: 2 interventions used no. 12, “require PE in schools” (64–66); 2 interventions used no. 13, “increase physical activity in PE” (64–66); 4 interventions used no. 14, “extracurricular physical activity” (62,64–67); one used no. 16, “improve access to outdoor recreation facilities” (67); and one used no. 18, “enhance infrastructure supporting walking” (67). All 5 interventions implemented at least one non-COCOMO strategy: 3 interventions increased amount of PA equipment (62,63,67); 3 interventions increased PA opportunities at school outside of PE (62,64–66); and 3 interventions adopted a district-wide PA-supportive curriculum (62,64–66).

Most policy or environmental strategies implemented in the studies reviewed focused on schools, whether the target population was students, school employees, or community members using the school facilities outside of school time. Use of schools as the focal point for obesity-related interventions aligns with Institute of Medicine recommendations (68). In rural areas where community resources and safe places to be active are limited (59,60,69–71), school facilities and resources (eg, gyms, fields, playgrounds) are often some of the few, if not the only community assets for PA (72). However, many rural areas are consolidating their school districts and building new schools on the outskirts of rural communities or on state highways rather than renovating existing schools or building new schools within municipal domains or current residential areas; this trend may create school grounds that are less accessible for the more populous areas of a rural county (60,71,73,74). Thus, when school-consolidation decisions are made, accessibility of school facilities for PA should be considered.

Recommended COCOMO measurements were not used in the studies reviewed, suggesting that COCOMO measurement approaches might need to be adapted for rural areas. For example, for strategy no. 18, “enhance infrastructure supporting walking,” the suggested COCOMO measurement is miles of paved sidewalks relative to total street miles. In the studies reviewed, enhancing infrastructure for walking included building walking trails and paving sidewalks; thus, miles of paved sidewalks would not capture all possible supportive infrastructure changes. The scale of some suggested COCOMO measurements are too large to be pertinent to smaller communities. For example, the suggested COCOMO measure for no. 13, “increase the amount of physical activity in PE programs in schools,” is whether the largest school district in the local jurisdiction has a policy that requires K through 12 students to be physically active for at least 50% of PE time. Small school districts, common in rural areas, may not be able to provide the level of detail necessary to determine success using this metric, and rural communities may have only one or 2 school districts; an appropriate rural metric could be the percentage of schools in a district that require K through 12 students to be physically active for at least 50% of PE time. Creating standard valid, reliable, specific, appropriate, and feasible metrics for policy and environmental strategy measurement for rural communities would help these communities better assess the success of policy and environmental strategies and help build an evidence base in rural communities.

Measurement of PA outcomes in the studies reviewed was rare and lacked consistency and methodological strength, limiting interpretation. When PA change was reported, most studies used a form of self-report. Few studies used objective measurement, and those that did measure PA objectively only did so in a subset of their sample, with half using pedometers. Accelerometers are a valid, reliable, and practical measure of PA and are used nationally and internationally (75). Rural evaluations need to consistently measure PA across studies using accelerometers to allow for better understanding of intervention effectiveness and comparison across the urban–rural continuum. Because of the decreasing costs of accelerometers and the ability to borrow units or purchase used units, recent rural community-based approaches have used accelerometers to measure PA and suggested strategies to improve feasibility, accuracy, and consistency (eg, text/email “wear” reminders, data collection methods, scoring methods) for using them in rural communities (75–77).

Lack of detail on study methods and variation in study design, measurement of outcomes, and context limited our ability to compare results of strategies across studies and examine effectiveness. Most studies were biased across assessment categories, indicating overall weakness in research design. Only 14 studies measured change in PA in response to policy or environmental strategies, and measurement approaches greatly varied. Future studies should incorporate elements of strong study design, such as clearly defined inclusion or exclusion criteria, protocols for data collection and use of measurement tools, measurement of potential confounders, reporting of sample size and estimated reach, and objective measurement of change in PA behavior.

Despite the challenges discussed and the challenges inherent in the subjective methods of systematic reviews, this review and its companion (24), provide a synthesis of the data on the use of COCOMO strategies in rural communities. The main findings of both reviews include the importance of making schools the focal point of nutrition- and PA-related interventions and building on existing community resources. Additionally, several nutrition- and PA-related COCOMO strategies, such as improvement of public transportation or geographic availability of supermarkets, may not be applicable to rural communities. We recommend inclusion of non-COCOMO PA-related strategies and refinement of current COCOMO recommended measurements. Improvements for current COCOMO nutrition-related strategies have been suggested (24). Both reviews recommend the use of stronger study design and measurement of policy, environment, and behavior in future studies (24). We echo a conclusion that additional guidance on implementation of policy or environmental strategies in rural communities could be found in research not published in scientific literature (eg, websites, gray literature) (24). Although we used many strategies to identify studies conducted in rural settings, the definition of “rural” varies (71), and studies that were not explicitly identified as rural by their authors might not have been included. Although the variability in rural communities (by geography, population density, topography, resources, and other factors) should be considered in obesity prevention approaches (71), these reviews described strategies that were successfully implemented in multiple rural communities.

COCOMO strategies and recommended measurements provide an evidence-based approach to address obesity and measure the success of intervention strategies. Most PA-related strategies appeared to be applicable in rural communities; however, measurements recommended by COCOMO were not always appropriate. Several non-COCOMO strategies were effectively implemented in rural communities. Generating a database of recommended strategies and measurements relevant to rural communities should be considered. Further research, using robust study designs and measures, is needed to better ascertain the success and effectiveness of implementing policy and environmental strategies in rural communities. This information could aid policy makers and community leaders in decision making on resource allocation and obesity-prevention efforts in their rural communities.

Top

Acknowledgments

This project was conducted by a workgroup in the CDC-funded Physical Activity Policy and Research Network (PAPRN; http://paprn.wustl.edu), a nationwide network of 21 academic centers (http://paprn.wustl.edu/about-us/Pages/Members1205-3695.aspx). Eleven PAPRN centers were involved in this study. This study was funded by the CDC cooperative agreement no. U48/DP001903, Prevention Research Centers Program, Special Interest Project 9-2009, and Physical Activity Policy Research Network to Washington University (principal investigator [PI], Amy Eyler), Baylor University, Department of Health, Human Performance & Recreation (PI, M. Renée Umstattd Meyer). The content is solely the responsibility of the authors and does not necessarily represent the official views of CDC. We also thank Dr Jordan Carlson (Children’s Mercy Hospital, Center for Children’s Healthy Lifestyles & Nutrition), Dr Jamie Chriqui (University of Illinois at Chicago, Institute for Health Research and Policy) and Dr. Thomas Schmid (CDC, Physical Activity and Health Branch, Division of Nutrition, Physical Activity, and Obesity [DNPAO]) for their assistance in the extraction process and Dr Thomas Schmid and Dr Laura Kettel Khan (CDC, DNPAO) for providing guidance in manuscript preparation.

Top

Author Information

Corresponding Author: M. Renée Umstattd Meyer, PhD, MCHES, Baylor University, Robbins College of Health and Human Sciences, Department of Health, Human Performance and Recreation, One Bear Place No. 97313, Waco, TX 76798. Telephone: 254-710-4029. Email: Renee_Umstattd@Baylor.edu.

Author Affiliations: Cynthia K. Perry, School of Nursing Oregon Health & Science University, Portland, Oregon; Jasmin C. Sumrall, Shana M. Walsh, Stephanie C. Clendennen, Robbins College of Health and Human Sciences, Baylor University, Waco, Texas, Megan S. Patterson, Baylor University, Waco, Texas; Steven P. Hooker, Arizona State University, Phoenix, Arizona; Kelly R. Evenson, Sydney Jones, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina; Karin V. Goins, University of Massachusetts Medical School, Worcester, Massachusetts; Katie M. Heinrich, Kansas State University, Manhattan, Kansas; Nancy O’Hara Tompkins, West Virginia Prevention Research Center, West Virginia University, Charleston, West Virginia; Amy A. Eyler, Rachel Tabak, Cheryl Valko, Prevention Research Center, Brown School at Washington University in St. Louis, St. Louis, Missouri.

Top

References

  1. Bennett KJ, Probst JC, Pumkam C. Obesity among working age adults: the role of county-level persistent poverty in rural disparities. Health Place 2011;17(5):1174–81. CrossRefexternal icon PubMedexternal icon
  2. Eberhardt MS, Pamuk ER. The importance of place of residence: examining health in rural and nonrural areas. Am J Public Health 2004;94(10):1682–6. CrossRefexternal icon PubMedexternal icon
  3. Lutfiyya MN, Lipsky MS, Wisdom-Behounek J, Inpanbutr-Martinkus M. Is rural residency a risk factor for overweight and obesity for U.S. children? Obesity (Silver Spring) 2007;15(9):2348–56. CrossRefexternal icon PubMedexternal icon
  4. Cooper R, Cutler J, Desvigne-Nickens P, Fortmann SP, Friedman L, Havlik R, et al. Trends and disparities in coronary heart disease, stroke, and other cardiovascular diseases in the United States: findings of the national conference on cardiovascular disease prevention. Circulation 2000;102(25):3137–47. CrossRefexternal icon PubMedexternal icon
  5. Ramsey PW, Glenn LL. Obesity and health status in rural, urban, and suburban southern women. South Med J 2002;95(7):666–71. CrossRefexternal icon PubMedexternal icon
  6. Befort CA, Nazir N, Perri MG. Prevalence of obesity among adults from rural and urban areas of the United States: findings from NHANES (2005–2008). J Rural Health 2012;28(4):392–7. CrossRefexternal icon PubMedexternal icon
  7. Trivedi T, Liu J, Probst J, Merchant A, Jhones S, Martin AB. Obesity and obesity-related behaviors among rural and urban adults in the USA. Rural Remote Health 2015;15(4):3267. PubMedexternal icon
  8. Go AS, Mozaffarian D, Roger VL, Benjamin EJ, Berry JD, Blaha MJ, et al. Heart disease and stroke statistics 2014 update: a report from the American Heart Association. Circulation 2014;129(3):e28–e292.
  9. Reis JP, Bowles HR, Ainsworth BE, Dubose KD, Smith S, Laditka JN. Nonoccupational physical activity by degree of urbanization and U.S. geographic region. Med Sci Sports Exerc 2004;36(12):2093–8. CrossRefexternal icon PubMedexternal icon
  10. Parks SE, Housemann RA, Brownson RC. Differential correlates of physical activity in urban and rural adults of various socioeconomic backgrounds in the United States. J Epidemiol Community Health 2003;57(1):29–35. CrossRefexternal icon PubMedexternal icon
  11. Martin SL, Kirkner GJ, Mayo K, Matthews CE, Durstine JL, Hebert JR. Urban, rural, and regional variations in physical activity. J Rural Health 2005;21(3):239–44. CrossRefexternal icon PubMedexternal icon
  12. Economic Research Service. Rural America at a glance: 2014 edition. Washington (DC): US Department of Agriculture; 2014.
  13. Economic Research Service. State fact sheets: US summary. Washington (DC): US Department of Agriculture; 2013.
  14. Glasser M, Holt N, Hall K, Mueller B, Norem J, Pickering J, et al. Meeting the needs of rural populations through interdisciplinary partnerships. Fam Community Health 2003;26(3):230–45. CrossRefexternal icon PubMedexternal icon
  15. Glasgow N, Blakely RM. Older nonmetropolitan residents’ evaluations of their transportation arrangements. J Appl Gerontol 2000;19(1):95–116. CrossRefexternal icon
  16. Oleson JJ, Breheny PJ, Pendergast JF, Ryan S, Litchfield R. Impact of travel distance on WISEWOMAN Intervention attendance for a rural population. Prev Med 2008;47(5):565–9. CrossRefexternal icon PubMedexternal icon
  17. Rosenthal TC, Fox C. Access to health care for the rural elderly. JAMA 2000;284(16):2034–6. CrossRefexternal icon PubMedexternal icon
  18. US Census Bureau. 2010 Demographic profile data; 2010. http://www.census.gov/2010census/news/press-kits/demographic-profiles.html. Accessed December 3, 2015.
  19. Marmot M, Friel S, Bell R, Houweling TAJ, Taylor S; Commission on Social Determinants of Health. Closing the gap in a generation: health equity through action on the social determinants of health. Lancet 2008;372(9650):1661–9. CrossRefexternal icon PubMedexternal icon
  20. Guide to Community Preventive Services. Increasing physical activity: environmental and policy approaches. Community Guide Branch, Division of Public Health Information Dissemination, Center for Surveillance, Epidemiology and Laboratory Services, Office of Public Health Scientific Services, Centers for Disease Control and Prevention. http://www.thecommunityguide.org/pa/environmental-policy/index.html. Accessed December 3, 2015.
  21. Khan LK, Sobush K, Keener D, Goodman K, Lowry A, Kakietek J, et al. ; Centers for Disease Control and Prevention. Recommended community strategies and measurements to prevent obesity in the United States. MMWR Recomm Rep 2009;58(RR-7, RR07):1–26. PubMedexternal icon
  22. Economic Research Service. Rural definitions. http://www.ers.usda.gov/data-products/rural-definitions.aspx. Accessed December 3, 2015.
  23. Rural Assistance Center. Frontier counties of the United States, 2010. https://www.raconline.org/racmaps/mapfiles/frontier.png. Accessed December 3, 2015.
  24. Calancie L, Leeman J, Jilcott Pitts SB, Khan LK, Fleischhacker S, Evenson KR, et al. Nutrition-related policy and environmental strategies to prevent obesity in rural communities: a systematic review of the literature, 2002–2013. Prev Chronic Dis 2015;12(140540):E57. PubMedexternal icon
  25. Higgins JPT, Altman DG. Assessing risk of bias in included studies. In: Collaboration TC, editor. Cochrane handbook for systematic reviews of interventions. West Sussex, England: John Wiley and Sons Ltd; 2011.
  26. Higgins JPT, Altman DG, Gøtzsche PC, Jüni P, Moher D, Oxman AD, et al. ; Cochrane Bias Methods Group; Cochrane Statistical Methods Group. The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials. BMJ 2011;343:d5928. CrossRefexternal icon PubMedexternal icon
  27. Guyatt GH, Oxman AD, Vist G, Kunz R, Brozek J, Alonso-Coello P, et al. GRADE guidelines: 4. Rating the quality of evidence — study limitations (risk of bias). J Clin Epidemiol 2011;64(4):407–15. CrossRefexternal icon PubMedexternal icon
  28. Bachar JJ, Lefler LJ, Reed L, McCoy T, Bailey R, Bell R. Cherokee Choices: a diabetes prevention program for American Indians. Prev Chronic Dis 2006;3(3):A103. PubMedexternal icon
  29. Belansky ES, Cutforth N, Delong E, Ross C, Scarbro S, Gilbert L, et al. Early impact of the federally mandated Local Wellness Policy on physical activity in rural, low-income elementary schools in Colorado. J Public Health Policy 2009;30(Suppl 1):S141–60. CrossRefexternal icon PubMedexternal icon
  30. Belansky ES, Cutforth N, Chavez R, Crane LA, Waters E, Marshall JA. Adapted intervention mapping: a strategic planning process for increasing physical activity and healthy eating opportunities in schools via environment and policy change. J Sch Health 2013;83(3):194–205. CrossRefexternal icon PubMedexternal icon
  31. Caballero B, Clay T, Davis SM, Ethelbah B, Rock BH, Lohman T, et al. ; Pathways Study Research Group. Pathways: a school-based, randomized controlled trial for the prevention of obesity in American Indian schoolchildren. Am J Clin Nutr 2003;78(5):1030–8. PubMedexternal icon
  32. Davis SM, Clay T, Smyth M, Gittelsohn J, Arviso V, Flint-Wagner H, et al. Pathways curriculum and family interventions to promote healthful eating and physical activity in American Indian schoolchildren. Prev Med 2003;37(6 Pt 2):S24–34. CrossRefexternal icon PubMedexternal icon
  33. Going S, Thompson J, Cano S, Stewart D, Stone E, Harnack L, et al. The effects of the Pathways Obesity Prevention Program on physical activity in American Indian children. Prev Med 2003;37(6 Pt 2):S62–9. CrossRefexternal icon PubMedexternal icon
  34. Steckler A, Ethelbah B, Martin CJ, Stewart D, Pardilla M, Gittelsohn J, et al. Pathways process evaluation results: a school-based prevention trial to promote healthful diet and physical activity in American Indian third, fourth, and fifth grade students. Prev Med 2003;37(6 Pt 2):S80–90. CrossRefexternal icon PubMedexternal icon
  35. Devine CM, Maley M, Farrell TJ, Warren B, Sadigov S, Carroll J. Process evaluation of an environmental walking and healthy eating pilot in small rural worksites. Eval Program Plann 2012;35(1):88–96. CrossRefexternal icon PubMedexternal icon
  36. Drummond RL, Staten LK, Sanford MR, Davidson CL, Magda Ciocazan M, Khor KN, et al. A pebble in the pond: the ripple effect of an obesity prevention intervention targeting the child care environment. Health Promot Pract 2009;10(2 Suppl):156S–67S. CrossRefexternal icon PubMedexternal icon
  37. DyckFehderau D, Holt NL, Ball GD, Willows ND; Alexander First Nation Community. Feasibility study of asset mapping with children: identifying how the community environment shapes activity and food choices in Alexander First Nation. Rural Remote Health 2013;13(2):2289. PubMedexternal icon
  38. Farag NH, Moore WE, Thompson DM, Kobza CE, Abbott K, Eichner JE. Evaluation of a community-based participatory physical activity promotion project: effect on cardiovascular disease risk profiles of school employees. BMC Public Health 2010;10(1):313. CrossRefexternal icon PubMedexternal icon
  39. Filbert E, Chesser A, Hawley SR, St Romain T. Community-based participatory research in developing an obesity intervention in a rural county. J Community Health Nurs 2009;26(1):35–43. CrossRefexternal icon PubMedexternal icon
  40. Friesen CA. Operation Wellness: a university/community collaboration to enhance adult wellness. Fam Consum Sci Res J 2010;39(2):152–60. CrossRefexternal icon
  41. Gantner LA, Olson CM. Evaluation of public health professionals’ capacity to implement environmental changes supportive of healthy weight. Eval Program Plann 2012;35(3):407–16. CrossRefexternal icon PubMedexternal icon
  42. Gombosi RL, Olasin RM, Bittle JL. Tioga County Fit for Life: a primary obesity prevention project. Clin Pediatr (Phila) 2007;46(7):592–600. CrossRefexternal icon PubMedexternal icon
  43. Humbert ML, Chad KE. Building an active living school: dream or reality? Avante 2003;9(1):9–24.
  44. Laing SS, Hannon PA, Talburt A, Kimpe S, Williams B, Harris JR. Increasing evidence-based workplace health promotion best practices in small and low-wage companies, Mason County, Washington, 2009. Prev Chronic Dis 2012;9:E83. PubMedexternal icon
  45. Martin SL, Maines D, Martin MW, MacDonald PB, Polacsek M, Wigand D, et al. Healthy Maine Partnerships: policy and environmental changes. Prev Chronic Dis 2009;6(2):A63. PubMedexternal icon
  46. Ndirangu M, Perkins H, Yadrick K, West JR, Bogle ML, Avis-Williams A, et al. Conducting needs assessment using the comprehensive participatory planning and evaluation model to develop nutrition and physical activity interventions in a rural community in the Mississippi delta. Prog Community Health Partnersh 2007;1(1):41–8. CrossRefexternal icon PubMedexternal icon
  47. Pate RR, Saunders RP, Ward DS, Felton G, Trost SG, Dowda M. Evaluation of a community-based intervention to promote physical activity in youth: lessons from Active Winners. Am J Health Promot 2003;17(3):171–82. CrossRefexternal icon PubMedexternal icon
  48. Jilcott Pitts SB, Smith TW, Thayer LM, Drobka S, Miller C, Keyserling TC, et al. Addressing rural health disparities through policy change in the stroke belt. J Public Health Manag Pract 2013;19(6):503–10. CrossRefexternal icon PubMedexternal icon
  49. Reger-Nash B, Bauman A, Cooper L, Chey T, Simon KJ, Brann M, et al. WV Walks: replication with expanded reach. J Phys Act Health 2008;5(1):19–27. PubMedexternal icon
  50. Riley-Jacome M, Gallant MP, Fisher BD, Gotcsik FS, Strogatz DS. Enhancing community capacity to support physical activity: the development of a community-based indoor-outdoor walking program. J Prim Prev 2010;31(1-2):85–95. CrossRefexternal icon PubMedexternal icon
  51. Schetzina KE, Dalton WTI 3d, Lowe EF, Azzazy N, VonWerssowetz KM, Givens C, et al. A coordinated school health approach to obesity prevention among Appalachian youth: the Winning with Wellness Pilot Project. Fam Community Health 2009;32(3):271–85. CrossRefexternal icon PubMedexternal icon
  52. Schetzina KE, Dalton WTI 3d, Pfortmiller DT, Robinson HF, Lowe EF, Stern HP. The Winning with Wellness Pilot Project: rural Appalachian elementary student physical activity and eating behaviors and program implementation 4 years later. Fam Community Health 2011;34(2):154–62. CrossRefexternal icon PubMedexternal icon
  53. Story M, Hannan PJ, Fulkerson JA, Rock BH, Smyth M, Arcan C, et al. Bright Start: description and main outcomes from a group-randomized obesity prevention trial in American Indian children. Obesity (Silver Spring) 2012;20(11):2241–9. CrossRefexternal icon PubMedexternal icon
  54. Tomlin D, Naylor PJ, McKay H, Zorzi A, Mitchell M, Panagiotopoulos C. The impact of Action Schools! BC on the health of Aboriginal children and youth living in rural and remote communities in British Columbia. Int J Circumpolar Health 2012;71(0):17999. CrossRefexternal icon PubMedexternal icon
  55. Naylor PJ, Scott J, Drummond J, Bridgewater L, McKay HA, Panagiotopoulos C. Implementing a whole school physical activity and healthy eating model in rural and remote First Nations schools: a process evaluation of Action Schools! BC. Rural Remote Health 2010;10(2):1296. PubMedexternal icon
  56. Wiggs I, Brownson RC, Baker EA. If you build it, they will come: lessons from developing walking trails in rural Missouri. Health Promot Pract 2008;9(4):387–94. CrossRefexternal icon PubMedexternal icon
  57. Williamson DA, Champagne CM, Harsha DW, Han H, Martin CK, Newton RL Jr, et al. Effect of an environmental school-based obesity prevention program on changes in body fat and body weight: a randomized trial. Obesity (Silver Spring) 2012;20(8):1653–61. CrossRefexternal icon PubMedexternal icon
  58. Brownson RC, Housemann RA, Brown DR, Jackson-Thompson J, King AC, Malone BR, et al. Promoting physical activity in rural communities: walking trail access, use, and effects. Am J Prev Med 2000;18(3):235–41. CrossRefexternal icon PubMedexternal icon
  59. Yousefian A, Ziller E, Swartz J, Hartley D. Active living for rural youth: addressing physical inactivity in rural communities. J Public Health Manag Pract 2009;15(3):223–31. CrossRefexternal icon PubMedexternal icon
  60. Hansen AY, Umstattd Meyer MR, Lenardson JD, Hartley D. Built environments and active living in rural and remote areas: a review of the literature. Curr Obes Rep 2015;4(4):484–93. CrossRefexternal icon PubMedexternal icon
  61. Barnidge EK, Radvanyi C, Duggan K, Motton F, Wiggs I, Baker EA, et al. Understanding and addressing barriers to implementation of environmental and policy interventions to support physical activity and healthy eating in rural communities. J Rural Health 2013;29(1):97–105. CrossRefexternal icon PubMedexternal icon
  62. Gollub EA, Kennedy BM, Bourgeois BF, Broyles ST, Katzmarzyk PT. Engaging communities to develop and sustain comprehensive wellness policies: Louisiana’s schools putting prevention to work. Prev Chronic Dis 2014;11:E34. CrossRefexternal icon PubMedexternal icon
  63. Cosco NG, Moore RC, Smith WR. Childcare outdoor renovation as a built environment health promotion strategy: evaluating the preventing obesity by design intervention. Am J Health Promot 2014;28(3 Suppl):S27–32. CrossRefexternal icon PubMedexternal icon
  64. Heelan KA, Bartee RT, Nihiser A, Sherry B. Healthier school environment leads to decreases in childhood obesity: the Kearney Nebraska story. Child Obes 2015;11(5):600–7. PubMedexternal icon
  65. King KM, Ling J. Results of a 3-year, nutrition and physical activity intervention for children in rural, low-socioeconomic status elementary schools. Health Educ Res 2015;30(4):647–59. CrossRefexternal icon PubMedexternal icon
  66. Ling J, King KM, Speck BJ, Kim S, Wu D. Preliminary assessment of a school-based healthy lifestyle intervention among rural elementary school children. J Sch Health 2014;84(4):247–55. CrossRefexternal icon PubMedexternal icon
  67. Barnidge EK, Baker EA, Estlund A, Motton F, Hipp PR, Brownson RC. A participatory regional partnership approach to promote nutrition and physical activity through environmental and policy change in rural Missouri. Prev Chronic Dis 2015;12:E92. CrossRefexternal icon PubMedexternal icon
  68. Institute of Medicine. Accelerating progress in obesity prevention: solving the weight of the nation. Washington (DC): Institute of Medicine; 2012.
  69. Hennessy E, Kraak VI, Hyatt RR, Bloom J, Fenton M, Wagoner C, et al. Active living for rural children: community perspectives using PhotoVOICE. Am J Prev Med 2010;39(6):537–45. CrossRefexternal icon PubMedexternal icon
  70. Robinson JC, Carson TL, Johnson ER, Hardy CM, Shikany JM, Green E, et al. Assessing environmental support for better health: active living opportunity audits in rural communities in the southern United States. Prev Med 2014;66:28–33. CrossRefexternal icon PubMedexternal icon
  71. Umstattd Meyer MR, Moore JB, Abildso C, Edwards MB, Gamble A, Baskin ML. Rural active living: a call to action. J Public Health Manag Pract 2015 Aug 31 [epub ahead of print] CrossRefexternal icon PubMedexternal icon
  72. Young DR, Spengler JO, Frost N, Evenson KR, Vincent JM, Whitsel L. Promoting physical activity through the shared use of school recreational spaces: a policy statement from the American Heart Association. Am J Public Health 2014;104(9):1583–8. CrossRefexternal icon PubMedexternal icon
  73. Elliott PW. School consolidation and notions of progress: why community actors almost always lose the fight to keep local schools and how they can turn the tables — a review of the literature. In Education 2012;18(1):50–66.
  74. ChangeLab-Solutions. Smart school siting: how school locations can make students healthier and communities stronger. Oakland (CA): National Policy and Legal Analysis Network to Prevent Childhood Obesity; 2012.
  75. Cain KL, Sallis JF, Conway TL, Van Dyck D, Calhoon L. Using accelerometers in youth physical activity studies: a review of methods. J Phys Act Health 2013;10(3):437–50. PubMedexternal icon
  76. Moore JB, Beets MW, Morris SF, Kolbe MB. Comparison of objectively measured physical activity levels of rural, suburban, and urban youth. Am J Prev Med 2014;46(3):289–92. CrossRefexternal icon PubMedexternal icon
  77. Morris SF, Kolbe MB, Moore JB. Lessons learned from a collaborative field-based collection of physical activity data using accelerometers. J Public Health Manag Pract 2014;20(2):251–8. CrossRefexternal icon PubMedexternal icon

Top

Tables

Return to your place in the textTable 1. Physical Activity–Related Strategies and Recommended Measurement Approaches From “Community Strategies and Measurements to Prevent Obesity in the United States”a
Strategy No. Strategy and Recommended Measurement Approach
Category: Encourage physical activity or limit sedentary activity among children and youth
12 Require physical education in schools
Largest school district has a policy requiring number of PE minutes per week meeting physical activity recommendations
13 Increase the amount of physical activity in physical education programs in schools
Largest school district has a policy that requires kindergarten–12 students to be active for at least 50% of time spent in PE classes
14 Increase opportunities for extracurricular physical activity
Percentage of schools in largest school district that allow use of athletic facilities by the public during nonschool hours
15 Reduce screen time in public service venues
Licensed childcare facilities required to limit screen viewing time to =2 hours per day for children aged =2 years
Category: Create safe communities that support physical activity
16 Improve access to outdoor recreational facilities
Percentage of residential parcels located within ½ mile of =1 outdoor public recreation facility
17 Enhance infrastructure supporting bicycling
Total miles of designated shared-use paths and bicycle lanes relative to total street miles (exclude limited access highways)
18 Enhance infrastructure supporting walking
Total miles of paved sidewalks relative to total street miles
19 Support locating schools within easy walking distance of residential areas
Largest school district has policy: new schools or fix existing schools in easy walking/bicycling distance of residential areas
20 Improve access to public transportation
Percentage of residential and commercial parcels within ¼ mile of =1 bus stop or ½ mile of =1 train stop
21 Zone for mixed-use development
Percentage of acres zoned for mixed use (residential with =1 public use)
22 Enhance personal safety in areas where people are or could be physically active
Number of vacant or abandoned buildings relative to total number of buildings
23 Enhance traffic safety in areas where people are or could be physically active
Local government policy for street design and operations with safe access for all users (include =1 complete streets element)

Abbreviation: PE, physical education.
a Kettel Khan et al (21).

 

Return to your place in the textTable 2. Location, Setting, COCOMO Strategies and Non-COCOMO Strategies Used, and Study Evaluation Focus in Review of Studies on Physical Activity–Related Policy and Environmental Strategies to Prevent Obesity in Rural Communities, 2002–2013
Study Location Setting COCOMO Strategy Non-COCOMO Strategy Evaluation Focus
Bachar et al (28) Western North Carolina, EBCI American Indian Reservation School (students), community, worksite, church No. 14 Adopt worksite policies or practices. Outcome
Belansky et al (29) Colorado School (students) Nos. 12, 13 Increase PA opportunities at school outside of PE. Outcome
Belansky et al (30) South central Colorado School (students) Nos. 13, 18 Increase PA opportunities at school outside of PE; increase amount of and access to PA equipment or improve existing equipment resources. Process, outcome
Caballero et al (31), Davis et al (32), Going et al (33), Steckler et al (34) Schools serving American Indian communities in Arizona, New Mexico, South Dakota School (students) Nos. 12, 13 Increase PA opportunities at school outside of PE. Process, outcome
Devine et al (35) Upstate New York Worksite None Promote PA resources. Process
Drummond et al (36) Yuma County, Arizona Childcare setting No. 15 Increase amount of and access to PA equipment or improve existing equipment resources; reduce sedentary time in school or preschool setting. Process, outcome
DyckFehderau et al (37) Alberta, Canada, main reserve land of the Alexander First Nation Community Nos. 16–18 Increase amount of and access to PA equipment or improve existing equipment resources. Formative
Farag et al (38) Southwestern Oklahoma School (employees) No. 18 Increase amount of and access to PA equipment or improve existing equipment resources; promote PA resources; adopt worksite policies or practices. Process, outcome
Filbert et al (39) Jefferson County, Kansas School (students) Nos. 12, 14, 18 None Formative, outcome
Friesen (40) Wells County, Indiana School (facility), community No. 18 Provide access to public buildings after hours; promote PA resources. Outcome
Gantner and Olson (41) Upstate New York Community None Promote PA resources. Process
Gombosi et al (42) Tioga County, Pennsylvania School (students), community, worksite, home No. 14 Adopt PA-supportive curriculum in school district. Outcome
Humbert and Chad (43) Saskatchewan Province, Canada School (students) Nos. 12–14 None Process
Laing et al (44) Mason County, Washington Worksite None Adopt worksite policies or practices. Process, outcome
Martin et al (45) Maine School (students), school (facility), community, worksite Nos. 14, 17, 18 Increase amount of and access to PA equipment or improve existing equipment resources. Process, outcome
Ndirangu et al (46) Lower Mississippi Delta Region, Arkansas, Louisiana, Mississippi Community Nos. 16, 18, 22, 23 Provide access to public buildings after hours; promote PA resources. Formative
Pate et al (47) South Carolina School (students) No. 14 Increase PA opportunities at school outside of PE. Process, outcome
Jilcott Pitts et al (48) Lenoir County, North Carolina Community Nos. 12–23 None Formative
Reger-Nash et al (49) North central West Virginia Community No. 18 Promote PA resources. Process, outcome
Riley-Jacome et al (50) Columbia and Greene counties, New York School (facility) None Provide access to public buildings after hours. Formative, process, outcome
Schetzina et al (51) Northeastern Appalachian Tennessee School (students) Nos. 16, 18 Increase PA opportunities at school outside of PE. Formative, process, outcome
Schetzina et al (52) Northeastern Appalachian Tennessee School (students) Nos. 16, 18 Increase PA opportunities at school outside of PE. Process, outcome
Story et al (53) Pine Ridge Reservation, South Dakota School (students), home Nos. 13–15 Increase PA opportunities at school outside of PE; increase amount of and access to PA equipment or improve existing equipment resources; reduce screen time at home. Formative, outcome
Tomlin et al (54), Naylor et al (55) Northwestern British Columbia, Canada School (students) Nos. 12, 14 Increase PA opportunities at school outside of PE. Process, outcome
Wiggs et al (56) Southeast Missouri (Ozarks) Community Nos. 16, 18 None Process, outcome
Williamson et al (57) Louisiana School (students) No. 13 None Outcome

Abbreviations: COCOMO, Common Community Measures for Obesity Prevention (21); EBCI, Eastern Bank of Cherokee Indians; PA, physical activity; PE, physical education.

 

Return to your place in the textTable 3. Description of Research Design and Results of Process or Outcome Evaluations After Implementation of Physical Activity Interventions, Review of Studies on Physical Activity–Related Policy and Environmental Strategies to Prevent Obesity in Rural Communities, 2002–2013
Study/Design and Bias Riska Reach,b Sample Sizec and Setting Factors Influencing Intervention Implementationd Policy or Environmental Change Implemented Changes Effected in Target Population
Bachar et al, Preventing Chronic Disease, 2006 (28)
Pre–post; no comparison group; high bias risk (score, 0) Reach: 1 tribal elementary school (up to 600 students enrolled); 1 tribal workplace; 5 churches. Sample: N = 86 employees; N = 140 students. — Employees given time off to exercise; increase in opportunities PA for students; student awardees given swim party instead of pizza party. Psychosocial School participants increased awareness of necessity to be physically active (teacher interview).
Behavior Increase in PA of worksite participants (self-reported in client histories and interviews; tool not specified).
Health status 71% of worksite participants lost weight (objectively measured) and decreased their BMI (objectively measured); some participants self-reported (in interviews) improvements in chronic illness (eg, decreased or eliminated diabetes medications, high blood pressure medications, or both).
Belansky et al, Journal of Public Health Policy, 2009 (29)
Pre–post follow-up; no comparison group; high bias risk (score, 1) Reach: 40 school districts. Sample: N = 45 elementary schools (mean number of enrolled students = 204). Barriers: competing pressures, lack of resources devoted to local wellness policy, principals’ unfamiliarity with local wellness policy, lack of a champion, lack of accountability mechanisms. Facilitators of success: committee of diverse individuals inside and outside of the school to develop policies. Increase in mean minutes of PE (PE teacher’s self-report in survey); decrease in time for recess (principal’s self-report in survey); no increase in number of principals requiring teachers to allow students to participate in PE or recess when incivilities occur (principal’s self-report in survey); most principals were not familiar with local wellness policy; most local wellness policies had weak wording. Psychosocial —
Behavior —
Health status —
Belansky et al, Journal of School Health, 2013 (30)
Pair randomized; medium bias risk (score, 2) Reach: 13 elementary schools. Sample: 10 elementary schools (mean number of enrolled students = 203). Barriers: lack of teacher and staff buy-in, principal turnover. Facilitators of success: involve staff at all levels, funding, communication within organization, timely feedback. 2 schools increased PE class time (eg, smaller classes, comprehensive curriculum); 4 schools made changes to recess (eg, organized activities during recess); 4 schools made changes to the playground (eg, balls, markings for 4-square, walking track). Psychosocial —
Behavior —
Health status —
Caballero et al, American Journal of Clinical Nutrition, 2003 (31); Davis et al, Preventive Medicine, 2003 (32); Going et al, Preventive Medicine, 2003 (33); Steckler et al, Preventive Medicine, 2003 (34)
RCT (school level); high bias risk (score, 1) Reach: 41 schools in 7 American Indian communities (2,059 students). Sample: baseline N = 1,704 children; post N = 1,409 children. — By year 3, all schools offered PE 3 times per week, and 56% of schools offered PE 5 days per week. Average of 1.6 activity breaks per school day. Psychosocial Increase in PA self-efficacy (self-report in survey).
Behavior No significant differences in PA change between groups for subset using 1 day of accelerometer data (n = 278), although nonsignificant increases in PA were found for intervention group (accelerometer, TriTrac-R3D); significantly higher self-reported PA at post-test for intervention schools (self-report: 24-hr PA recall survey).
Health status No significant differences between intervention and control groups for all anthropometric variables (objectively measured: BMI; % body fat using bioelectrical impedance; skinfold thickness).
Devine et al, Evaluation and Program Planning, 2012 (35)
Pre–post; mixed methods process evaluation; no comparison group; high bias risk (score, 0) Sample: N = 226 women at 5 worksites Facilitators to adherence: accountability to coworkers and public displays of walking achievements. Reach at each worksite ranged from 19%–96%. Worksite walking program, maps of walking trails at worksite. Psychosocial Increase in awareness of walking (self-reported in focus groups).
Behavior —
Health status —
Drummond et al, Health Promotion Practice, 2009 (36)
Pre–post; no comparison group; high bias risk (score, 1) Reach: 30 child care settings, serving 1,876 children. Sample: N = 17 of the 30 child care centers. Facilitators: program was accredited as continuing education for childcare providers, accreditation provided incentive for home-based childcare centers. Increase in number of centers that had portable play equipment, had indoor play space for running, did not restrict PA as a punishment, and implemented PA best practices; increase in percentage of centers providing all children with daily PA time (active play time =60 min, =2 outdoor active play times, based on staff self-report). Psychosocial —
Behavior Increase in level of staff member PA (informal self-report from site coordinator).
Health status —
Farag et al, BMC Public Health, 2010 (38)
Pre–post; no comparison group; high bias risk (score, 0) Reach: 1 school district with 5 schools. Sample: baseline N = 202 employees; post N = 187. — Worksite wellness program implemented. Employees could use planning period to exercise; treadmills added in schools; hallways marked with mileage. Psychosocial —
Behavior Nonsignificant increase in PA: increase in mean MET minutes per week (self-reported in survey: IPAQ-short).
Health status Significant decrease in total, HDL and LDL cholesterol levels (objectively measured) and decrease in systolic blood pressure (objectively measured).
Filbert et al, Journal of Community Health Nursing, 2009 (39)
2-Phase study. Phase 1: retrospective observation. Phase 2: implementation school health program. High bias risk (score, 0) Reach: 5 school districts. Sample: Phase 1: N = 174 (78 girls and 96 boys). — Built walking trail for student and community use, maintained daily PE, implemented employee wellness program. Psychosocial —
Behavior —
Health status —
Friesen, Family and Consumer Sciences Research Journal, 2010 (40)
Annual cross-sectional assessment for 4 years; pre–post; no comparison group; high bias risk (score, 0) Reach: 1 community, 3 school districts (10 schools), 52 worksites. Sample: N = 1,666 (annual community survey); adult PA program participants (N = 226); worksite wellness participants (N = 333). — School wellness policies developed and implemented; school facilities opened to community in all 10 schools; centralized walking path built on county fairgrounds. Psychosocial Adult PA program participants showed improvement in readiness to engage in PA for 30 min 5 days per week (self-reported in survey).
Behavior Community survey participants showed significant increase in days per week of PA (self-reported in survey, tool not specified); adult PA program participants showed increase in percentage engaged in 30 min per day of PA 3 to 7 days per week after 1 semester (self-reported in survey, tool not specified).
Health status 139 Worksite wellness participants lost on average 3 pounds after 1 semester and 63 participants lost on average 5 pounds after 2 semesters (objectively measured).
Gantner and Olson, Evaluation and Program Planning, 2012 (41)
Cross-sectional (initial year and year 2); qualitative; high bias risk (score, 0) Reach: 8 counties. Sample: N = 20 community partners; N = 31 survey participants at baseline, N = 20 survey participants at year 2; N = 21 interviews. Identified barriers: lack of organizational support for policy change, lack of political power to make change, need to develop skills and knowledge, frustration with long-term timeframe to make change, lack of consistent funding over long-term Creation of distribution of map of PA opportunities in county. Psychosocial Increase in awareness of how to advocate for policy and environment change (self-report by partnership members in interviews).
Behavior —
Health status —
Gombosi et al, Clinical Pediatrics, 2007 (42)
Nonrandomized age-matched cohorts; high bias risk (score, 0) Reach: 6,000 students in preschool through high school with an annual cohort of approximately 4,800 K–8 school children; 7 worksites, 15,000 employees; and 1,000 residents of 8 communities. — Worksite wellness program, health curriculum implemented in schools; community PA events implemented. Psychosocial —
Behavior —
Health status Increase in prevalence of overweight and obesity (measurement method not specified).
Humbert and Chad, Avante, 2003 (43)
Two-year longitudinal; qualitative; high bias risk (score, 1) Reach: 5 schools: one K–3 school (75 students) and 5 K–6 schools (range, 100–300 students per school). Sample: 50 teachers and 5 administrators. Facilitators and recommendations: Quality PE most critical component; need widespread support from school and community, adequate time for implementation, teacher effort and team work. Schools offered daily PE; increase in opportunities for PA during school. Psychosocial Increase in awareness of importance of PA among administration and teachers.
Behavior —
Health status —
Laing et al, Preventing Chronic Disease, 2012 (44)
Pre–post; no comparison group; medium bias risk (score, 2) Reach: 23 worksites (average 42 workers per worksite). Sample: 23 employers. Factors influencing worksite participation: upper management support and concern for health needs of employees. Easy to implement and broad in scope. Significant increase in best practices implemented; increase in number of employers offering PA programming; increase in implementation of PA policies. Psychosocial Increase in awareness of opportunities for PA.
Behavior —
Health status —
Martin et al, Preventing Chronic Disease, 2009 (45)
Retrospective evaluation; high bias risk (score, 0) Sample: N = 31 Community agency–school partnerships — 1,683 Environmental changes supporting PA were accomplished, including new walking or biking trails, employee wellness committees, community access to PA equipment. Psychosocial —
Behavior —
Health status —
Pate et al, American Journal of Health Promotion, 2003 (47)
Nonrandomized; 2-groups’; pre–post; low bias risk (score, 3) Sample: N = 434 students in grade 5 Community ownership of program not achieved; only after-school summer programming implemented as planned; transportation had impact on attendance; only 5% of children attended at least 50% of sessions; staff training took longer than expected; staff did not understand concept of self-efficacy or emphasis on noncompetitive programming Increase in opportunities for PA. Psychosocial No change in beliefs about PA intention, PA consequences, social influences on PA, or PA self-efficacy (self-reported in surveys).
Behavior No change in moderate-to-vigorous PA levels (self-reported using a previous day PA recall survey for after-school PA time).
Health status —
Reger-Nash et al, Journal of Physical Activity & Health, 2008 (49)
Nonrandomized; 2-group; pre–post; low bias risk (score, 3) Reach: 5,400 residents. Sample: baseline survey: N = 1,233 adults in intervention community and N= 633 in comparison community. Follow-up survey: N = 887 in intervention and N = 446 in comparison Successful media campaign: 1,143 television reports, 167 radio reports, 104 print media reports, and 17 campaign-related photos in newspapers. Increase in funding for trail maintenance and sidewalk construction: increase in opportunities for PA in community (walking league). Psychosocial —
Behavior Significant increase in community members being sufficiently active (self-reported in survey: BRFSS).
Health status —
Riley-Jacome et al, Journal of Primary Prevention, 2010 (50)
Post only; no comparison group; high bias risk (score, 0) Reach: 3 school districts. Sample: N = 40 survey responses; N = 55 completed walking logs. Existing school insurance policies were sufficient for community walking program; no additional school staff time required. Barriers: distance to school buildings, conflicts with school related activities, lack of person to administer program. Increase in opportunity for PA; opened up schools for community walking program. Psychosocial Increase in social support (self-reported in focus groups).
Behavior Increase in level of PA (self-reported in survey: 1 item asking participants to recall change in PA).
Health status —
Schetzina et al, Family & Community Health, 2009 (51)
Pre–post; no comparison group; high bias risk (score, 0) Reach: 1 elementary school. Sample: N = 114 students in grade 3 or grade 4 and 40 teachers. Some teachers reported pedometers cumbersome to use; 87% of teachers reported program acceptability. Indoor and outdoor walking trails established; instituted “move it moments” (5 min of PA); all teachers reported using “move it moments”; most teachers reported most or all students wore pedometers. Psychosocial Teachers perceived “move it moments” improved student behavior during class.
Behavior Significant increase in steps per day (pedometer).
Health status No change in BMI z scores in first 7 months of the program; students with healthy weight or at risk for overweight (85th–<95th percentile) were more likely to decrease BMI z score; students who were overweight were more likely to show no change in BMI z score than were healthy-weight or at-risk-for-overweight students.
Schetzina et al, Family & Community Health, 2011 (52)
4-Year follow-up; pre–post; high bias risk (score, 0) Reach: 1 elementary school. Sample: N = 66 students in grade 4 and 23 teachers. — Indoor and outdoor walking trails established; 86% of teachers reported using pedometers in class; 91% of teachers reported using “move it moments” (5 min of PA) daily in last month; 14% of teachers reported using indoor walking trails weekly as part of program (self-reported in survey). Psychosocial No significant change in descriptive family norms or descriptive or injunctive friend norms (self-reported in survey)
Behavior Significant increase in steps at the school-level (pedometers).
Health status —
Story et al, Obesity, 2012 (53)
RCT; pre–post (school level); high bias risk (score, 0) Reach: 14 schools. Sample: N = 232 boys and 222 girls in kindergarten or 1st grade. — Classroom action breaks; outside class walks; modified PE class; family activities. Psychosocial —
Behavior No significant changes in PA in schools (teacher self-report of school time spent in PA).
Health status 10% Decrease in prevalence of overweight (based on objective measurements of height and weight).
Tomlin et al, International Journal of Circumpolar Health, 2012 (54); Naylor et al, Rural and Remote Health, 2010 (55)
Pre-post; no comparison group; low bias risk (score, 3) Reach: 3 schools. Sample: N = 148 students. Barriers: lack of time and school resources, high staff turnover, evaluation requirements, student behavior, low levels of staff knowledge about healthy living. Facilitators: training, resources, and ease of implementation. Action bins distributed to classrooms for use with 15 min action break each day; increase in PA opportunities. Psychosocial —
Behavior No significant change in moderate-to-vigorous PA for self-report or subset (n = 30) using =3 days of accelerometer data (self-reported in survey: PAQ; subset accelerometers: Actigraph GT1M).
Health status No change in BMI (based on objective measurements of height and weight); increase in aerobic fitness (20-meter shuttle run).
Wiggs et al, Health Promotion Practice, 2008 (56)
Case study; retrospective evaluation (post only); qualitative; high bias risk (score, 0) Reach: Several counties. Issues to consider in trail development: plan for maintenance trail, funding sources, location, size, objectives, recognition of funding sources on trail. Liability concerns were not an issue. Construction of 30 walking trails in multiple counties, with most in residential park areas. Psychosocial —
Behavior Increase in the walking time of most walkers since using trail (interviews), increase in percentage of trail users’ PA since using the trail (self-reported in random-digit–dialed telephone survey: 1 item asking participants to recall change in PA since using trails) (58)
Health status —
Williamson et al, Obesity, 2012 (57)
Longitudinal (pre–post, month 18, month 28); cluster RCT (school systems were clustered); 2 intervention groups and 1 control group; medium bias risk (score, 2) Reach: 4, 857 students in grades 4–6. Sample: students in grades 4–6; N = 713 in primary intervention, N = 760 in primary and secondary intervention; and N = 587 in control. — Modified PE program Psychosocial —
Behavior No changes in PA or sedentary behavior (self-reported in survey: SAPAC).
Health status No changes in percentage body fat or BMI between intervention groups (based on objective measurements of height and weight); decrease in percentage body fat among boys, a slower increase in percentage body fat among girls in environmental-change group than in control, and significantly smaller increases in BMI for white girls between environmental-change group and control group at month 28 (based on objective measurements of height, weight, and body fat).

Abbreviations: —, data not reported; BMI, body mass index; BRFSS, Behavioral Risk Factor Surveillance System; HDL, high-density lipoprotein, IPAQ–Short, International Physical Activity Questionnaire–Short Form; LDL, low-density lipoprotein; PA, physical activity; PE, physical education; RCT, randomized control trial; MET, metabolic equivalent, PAQ, Physical Activity Questionnaire for Children and Adolescents; SAPAC, Self-Administered Physical Activity Checklist.
a Bias risk was determined using Cochrane Collaboration’s assessment tool for RCTs and GRADE guidelines for non-RCTs (25–27). The Cochrane tool assesses risk of bias across 6 categories: sequence generation, allocation concealment, blinding, incomplete outcome data, selective outcome reporting, and other sources of bias (25,26); GRADE guidelines assess risk of bias across 4 categories: appropriate eligibility criteria, measurement of exposure and outcome, control of confounding, and incomplete follow-up (27). Risk of bias was rated as low (score of 1), high (score of 0), or unclear (score of 0) for each Cochrane or GRADE category based on study type (25); overall summary scores for bias risk were calculated and categorized as low, medium, or high (RCTs, low risk = 5 or 6, medium risk = 2–4, and high risk = 0 or 1; non-RCTs: low risk = 3 or 4, medium risk = 2, and high risk = 0 or 1).
b When reported, we listed reach, which is the number of community members potentially affected by an intervention.
c When reported, we listed the sample size of participants who completed evaluation measures for each study.
d When reported, we listed the factors influencing intervention implementation.

 

Return to your place in the textTable 4. Description of Results of Formative Evaluations, Review of Studies on Physical Activity–Related Policy and Environmental Strategies to Prevent Obesity in Rural Communities, 2002–2013
Study Design Sample Size and Setting (If Reported) Policy and Environment Change Ideas
DyckFehderau et al (37) Asset mapping; high bias risk (score, 0) 2 high school students; 7 students in grade 6 Suggested improvements in park and recreation facilities.
Ndirangu et al (46) Needs assessment; high bias risk (score, 0) 21 community members; 9 university researchers Suggestions on nutrition and PA in school curriculum; fines or policy for loose dogs; improvement in parks and recreation facilities, walking trails, and street lighting; marketing through television advertisement depicting local community members exercising.
Jilcott Pitts et al (48) Mixed methods ranking of COCOMO strategies; medium bias risk (score, 2) 336 community members Most winnable: increasing opportunities for extracurricular PA. Winnable: enhancing infrastructure supporting bicycling and walking. Least winnable: zoning for mixed-use zoning. Government regulations or mandates were not favorably perceived. Rural landscape was a barrier to walkability and locating schools near neighborhoods. Community support for policy change was high for all 7 COCOMO strategies, highest for “communities should improve sidewalks to support walking” and “communities should improve access to outdoor exercise and recreation places.”

Abbreviations: COCOMO, Common Community Measures for Obesity Prevention; PA, physical activity.

Top

View Page In:pdf icon

The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the authors’ affiliated institutions.

Page last reviewed: January 7, 2016
Content source: National Center for Chronic Disease Prevention and Health Promotion