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The way communities are designed has a great influence on how active we are. When communities are safe, well-maintained and have appealing scenery, children and families are more likely to be active. Unfortunately, many people—especially those at high risk for obesity—live in communities that lack parks and have high crime rates, dangerous traffic patterns and unsafe sidewalks.  Such communities discourage residents from walking, bicycling and playing outside. Increasingly, local governments are considering how community design will impact residents’ physical activity. Our research documents effective strategies for creating communities that support active living and promote health.

View The Role of Communities in Promoting Physical Activity infographic.

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A National Survey of Correlates of Local Health Department Engagement in Community Policy to Encourage Physical Activity

Date: 
02/23/2015
Description: 

Presentation at the 2015 Active Living Research Annual Conference.

Abstract: 

Background
Evidence has accumulated that particular environmental conditions and characteristics correlate with walking and bicycling, including participation in these behaviors for active transportation.  Public health authorities have recommended strategies in the realms of land use and urban design, transportation and recreation access for communities to become more walk- and bicycle-friendly. Model policies in these domains exist. Policy development is one of the core functions of public health, and evidence suggests that policy activity or development by local health departments (LHDs) correlates with policy adoption. However, there are critical practice gaps. Participation by local health officials in the built environment policy process, including policies related to land use and urban design, transportation and recreational access that promote physical activity, is limited.  Greater LHD involvement could increase the adoption and implementation of policies needed for national physical activity objectives and benchmarks to be met. LHD characteristics and activities have been shown to affect delivery of essential public health services, engagement in quality improvement efforts, partnership involvement, ties to other LHDs that could facilitate implementation of evidence-based programming, and public health performance. Better understanding of LHD characteristics associated with participation in built environment policy processes is an important first step to developing tailored interventions to increase policy implementation.

Objectives
We assessed correlates of local health department (LHD) participation in community-focused policy and advocacy activities to encourage physical activity in the past two years in a nationally representative sample of LHD directors.

Methods
Cross-sectional data from the National Association of County and City Health Officials’ 2013 National Profile of Local Health Departments were analyzed. 490 LHD directors completed both Core and Module 1 of the web-based survey (79% response rate). Policy participation was measured by a series of questions that first asked if the LHD had participated in obesity/chronic disease prevention policy and advocacy activities in the past two years. Those who responded yes were specifically asked about involvement in community level urban design and land use policies to encourage physical activity, active transportation options, and expanding access to recreational facilities. Correlates included structural characteristics (population size served, region, jurisdiction type, staffing), quality improvement efforts (completion of Community Health Assessment (CHA) and Community Health Improvement Plan (CHIP), Public Health Accreditation Board (PHAB) status, use of core competencies for public health workers, and use of Guide to Community Preventive Services), and collaboration (community land use partnership, cross-jurisdictional sharing of resources). Multivariable logistic regression models were used.

Results
Less than one-quarter of LHD directors reported that their department had been involved in policy and advocacy activities related to urban design and land use (25%), active transportation (16%) and recreational facility access (23%). In multivariable logistic regression models, LHDs with populations of 500,000 or more and consistent use of the Community Guide were associated with participation in each of the three policy types. Higher Full Time Equivalent (FTE) levels were associated with greater participation in policy to increase active transportation, with trends of an association with participation in policy for land use and urban design and expanding recreational access. LHDs with a community health improvement plan were more likely to participate in urban design and land use policy, whereas LHDs that were undecided about pursuing accreditation status were less likely to participate in recreational policy.  Participation in a community partnership related to land use was associated with urban design and land use and active transportation policy activity.

Conclusions
Population size served and staffing resources correlated with LHD participation in policy activities to increase community physical activity. Quality improvement efforts such as CHIP development, PHAB status and use of the Community Guide were associated with or show a trend toward policy participation. Collaboration in terms of partnering with the community on land use, but not resource sharing across LHDs, correlated with policy activity to increase physical activity at the community level.

Implications
Opportunities for interventions at the local level to boost policy implementation include assisting LHDs that serve smaller population sizes, integrating community physical activity strategies into LHD quality improvement efforts, and coaching LHDs on partnership-building with officials and the community in the areas of land use, transportation and recreation by increasing their capacity in these unfamiliar technical areas.

Support / Funding Source
This analysis is a product of a Prevention Research Center and was supported by Cooperative Agreement Number U48/DP001933 from the Centers for Disease Control and Prevention.

Authors: 
Karin Valentine Goins, MPH, University of Massachusetts Medical School
Location by State: 
Population: 
Study Type: 

Physical Activity-Related Policy and Environmental Strategies to Prevent Obesity in Rural Communities: A Systematic Review

Date: 
02/23/2015
Description: 

Presentation at the 2015 Active Living Research Annual Conference.

Abstract: 

Background
Research consistently supports greater health disparities for rural residents compared with urban residents, such as higher rates of chronic diseases, including obesity. Sixteen percent of Americans live in rural areas encompassing 72% of land in the U.S. Evidence supports the effectiveness of environmental and policy strategies to prevent obesity and promote health equity. In 2009, the CDC recommended 24 evidence-based strategies for communities to use in planning and monitoring obesity-related environmental and policy changes; the “Common Community Measures for Obesity Prevention” (COCOMO; Kettel Kahn, 2009). Twelve strategies focused on physical activity to “encourage physical activity or limit sedentary activity among children and youth” or “create safe communities that support physical activity”. However, evidence supporting environmental and policy strategies is largely derived from research conducted in urban and suburban settings.

Objectives
Objectives were to conduct a systematic literature review to describe physical activity-related policy and environmental strategies being implemented in rural communities and how COCOMO strategies have been applied.

Methods
This project was conducted by a workgroup within the CDC-funded Physical Activity Policy and Research Network (PAPRN; http://paprn.wustl.edu).   A primary and secondary literature search was conducted in PubMed, PsychInfo, Web of Science, CINHAL, and PAIS databases for articles published between 2002 and 2013, in English, that reported findings from physical activity-related policy and/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 using search terms representing Native American communities and predominantly rural states. Methods mirror a sister review conducted by the CDC-funded Nutrition and Obesity Policy Research and Evaluation Network to help provide a more complete picture of obesity prevention in rural communities.     Inclusion and Exclusion Criteria:   At least two researchers reviewed titles, abstracts, and texts of articles for inclusion. To be included an article had to report findings from empirical formative, process, or outcome research with strategies aimed to change policy and/or environments to support physical activity in rural North American communities. No studies were excluded a priori based on study design or location. Publications were excluded if both rural and urban communities were included, but rural-specific findings were not reported; the primary focus was on instrument development or individual-level behavioral change; or if descriptive studies were not associated with an intervention.   Extraction Process:   Each article was extracted independently by two researchers who recorded results using a customized Qualtrics online survey software. Extraction results were compared and discrepancies were resolved by consensus. Study quality was examined using Cochrane and GRADE assessments of bias risk for randomized and non-randomized studies, respectively. Risk of bias was rated as low, high, or unclear for each category and overall summary scores for bias risk were calculated and categorized as low, medium, or high.

Results
Searches returned 9,879 articles, of which 2,002 were identified as relevant based on their title and abstract for further screening. Duplicates were removed, leaving 488 records for full-text screening; 443 of these did not meet inclusion criteria.  Of the remaining 45 articles representing 41 distinct studies, 11 additional articles were excluded during the extraction phase, thus 34 articles representing 30 distinct studies were extracted. Each physical activity-related COCOMO strategy was mentioned at least once within these 30 studies. The two most commonly applied COCOMO strategies were #14 “communities should increase opportunities for extracurricular physical activity” (n=10) and #18 “communities should enhance infrastructure supporting walking” (n=9).  In addition, the following non-COCOMO strategies were identified: increasing physical activity opportunities at school (e.g., classroom activity breaks, longer school recess); increasing physical activity equipment, access to equipment, or improving existing resources; promotion of physical activity resources (e.g., signs to promote hallway walking routes); access to public buildings after hours for walking; reducing screen time at home; worksite or school policies/practices; and increasing community green space.   Study settings included schools (n=19), community (n=13), worksites (n=5), churches (n=1), and family (n=1); four interventions targeted multiple settings. Four studies were randomized control trials. Bias risk assessments revealed the majority of studies had a high risk of bias (n=21), five had a medium risk, and four studies had a low risk. Over half (67%) of the interventions had at least one positive policy and/or environmental result.

Conclusions
COCOMO strategies provide an evidence-based approach to promoting physical activity and appear to be applicable in rural communities. However, relatively few studies have incorporated these to date. Most strategies are being applied at school and community settings, which might have the greatest reach within rural areas. Future studies should consider study designs and methods to reduce these high bias risks.

Implications
Further understanding of policy and environmental strategies for rural areas can help policy makers and community leaders with decisions for resource allocations and COCOMO-related efforts in their communities. However, further research is needed in rural communities to better understand which COCOMO strategies are most applicable and effective.

References
Kettel Kahn, L., Sobush, K., Keener, D., Goodman, K., Lowry, A., Kakietek, J., & Zaro, S. (2009). Recommended community strategies and measurements to prevent obesity in the United States. Morbidity and Mortality Weekly Report Recommendations and Reports, 58(RR07), 1-26.

Support / Funding Source
This study was funded by the CDC Cooperative Agreement number U48/DP001903, Prevention Research Centers Program, Special Interest Project 9-09, and Physical Activity Policy Research Network to Washington University (PI: Amy Eyler), Baylor University, Department of Health, Human Performance & Recreation.

Authors: 
M. Renée Umstattd Meyer, PhD, Baylor University
Location by State: 
Study Type: 

Evaluation of Healthy Kids, Healthy Communities

Date: 
02/25/2015
Description: 

Presentation at the 2015 Active Living Research Annual Conference.

Abstract: 

Background
The evaluation of the Robert Wood Johnson Foundation’s (RWJF) Healthy Kids, Healthy Communities (HKHC) national program is an example of an effort to apply systems science and mixed-methods evaluation approaches to comprehensive policy, systems, and environmental interventions. The HKHC national program (www.healthykidshealthycommunities.org) supported community-based efforts to implement policy, system, and environmental changes aimed to make communities healthier, particularly for higher-risk children and families (ethnic/racial minorities, lower-income populations, or those living in southern states), by increasing both active living and healthy eating. RWJF funded one year of evaluation planning (mid-March 2009 to mid-March 2010) and four subsequent years to support a mixed-methods evaluation of HKHC (April 2010 to March 2014), including all 49 communities across the United States and Puerto Rico. Communities selected to participate in this multi-year demonstration varied in population and geographic sizes (municipal to eight counties), sociodemographic composition (median annual household income, race/ethnicity, urban/suburban/rural), scale (county-wide to specific organizations or settings), scope of their proposed strategies (e.g., new or modified parks versus nutrition assistance in farmers’ markets), lead organizations (nonprofit, education, philanthropy, government), and age of the community partnerships. The evaluation did not focus on changes in individual behaviors and health outcomes.

Description
Eight complementary evaluation methods addressed four primary aims seeking to: 1) coordinate data collection for the evaluation through the web-based project management system and provide training and technical assistance for use of this system; 2) guide data collection and analysis through use of the Assessment & Evaluation Toolkit; 3) conduct a quantitative cross-site impact evaluation among a subset of community partnership sites; and 4) conduct a qualitative cross-site process and impact evaluation among all 49 community partnership sites. The evaluation consisted of the following key components: HKHC Community Dashboard: This web-based project management system (www.hkhcdashboard.org) coordinated data collection for the evaluation. It was designed to encourage the formation of a collective learning network among community partnerships, Project Officers, and Evaluation Officers. This website included functions such as social networking, progress reporting, and access to the assessment and evaluation toolkit to maintain a steady flow of users over time and increase peer engagement across communities. Individual and Group Interviews: Evaluators collaborated with community partnerships to conduct individual and group interviews with staff, partners, and community representatives before, during, and after site visits. Interview protocols focused on organizational and community factors influencing processes and means used to develop, implement, and enforce policies. In addition, evaluators tracked costs and funding associated with the design, development, implementation, and enforcement of cross-site strategies. Group Model Building: The evaluation team and partners from the Social System Design Lab at Washington University in St. Louis co-designed a group model building process to develop behavior-over-time-graphs and graphical system dynamics models (causal loop diagrams) with community partnerships. These exercises provide deeper and shared insights among representatives from the community partnerships into the drivers of obesity dynamics, better understanding of local systems at play, more rigorous critique of assumptions underlying the systems, and greater “buy in” to high-leverage prevention policy recommendations. Enhanced Evaluation: The evaluation team created tools, protocols, and trainings for environmental audits and direct observations associated with cross-site strategies to be conducted by community partnerships. Participation in these methods was voluntary, yet 31 of 49 community partnerships engaged in these activities. Supplemental Methods: Evaluators also collected and analyzed data from an online partnership and community capacity survey, photos, community partnerships’ annual narrative and financial reports, and surveillance systems (e.g., U.S. census). A synopsis of cross-site findings with community examples will be presented.

Lessons Learned
Several themes emerged, including: the value of systems approaches, the need for capacity building for evaluation, the value of focusing on upstream and downstream outcomes, and the importance of practical approaches for dissemination. Constraints included: a lack of standards in the field for indicators and measures of many of these factors, difficulty in attributing effects or impacts to specific strategies, and challenges with analyzing, interpreting, and applying what is learned, particularly with respect to complex systems science methods.

Conclusions
Community-based initiatives such as HKHC provide promising approaches for addressing childhood obesity. This presentation illustrates how mixed-methods evaluation approaches can provide practice-relevant evidence that has the potential to improve population health. The mixed-methods evaluation of HKHC advances evaluation science related to community-based efforts for addressing childhood obesity in complex community settings.

Next Steps
This evaluation will inform research and practice related to the design, implementation, and evaluation of policy, system, and environmental interventions; key partners to engage in the process to change community environments; and possible causal relationships among social determinants as well as factors associated with partnership and community capacity that influence healthy eating and active living policies and environments, and health and health behaviors.

References
Evaluation of Healthy Kids, Healthy Communities Supplement  to be published in March/April 2015.

Support / Funding Source
Support for this evaluation was provided by a grant from the Robert Wood Johnson Foundation (#67099).

Authors: 
Laura Brennan, PhD, MPH, Transtria LLC
Location by State: 

A Longitudinal Study: The Impact of a Signalized Crosswalk on Crossing Behaviors in a Low-Income Minority Neighborhood

Date: 
02/24/2015
Description: 

Presentation at the 2015 Active Living Research Annual Conference.

Abstract: 

Background
There is a paucity of research exploring the behaviors of low-income community residents in context of their neighborhoods (Gordon-Larsen et al., 2006; Zhu & Lee, 2008). These underserved communities often are comprised of an outdated built environment with high-speed, high-volume streets resulting in limited access to parks and active transportation. Studies show that key neighborhood features, including high-speed traffic and general walkability, directly influence physical activity (Kaczynski et al., 2014; Handy et al., 2008). We have previously shown that the completion of a signalized crosswalk and median linking low-income housing with a public park showed positive effects on active living behaviors (Schultz et al., 2014). Additional data collection in 2014 provided an opportunity to examine the longevity of these behavioral changes associated with the crosswalk installation.

Objectives
This study aims to explore if previously observed built environmental influences on street crossing behaviors and traffic speed reductions have been sustained in a low-income minority neighborhood with significant barriers to physical activity opportunities.

Methods
Data collection occurred at one Intervention site (Providence Road) and one Control site (College Avenue) in Columbia, MO. The Control site was selected by examining relevant characteristics of the neighborhood (e.g., size, income level), and the corresponding street (e.g., number of lanes, typical traffic volumes/speeds, pedestrian crossing facilities). Street crossing behaviors were collected using direct observation and assessed the mode of transportation, designation of the crossing (e.g., Designation Zone: Designated Crossing [at intersections/crosswalks] or Non-Designated Crossing [e.g., other crossing point]), as well as race/ethnicity, gender, and age within 5-6 predetermined zones at both sites. Magnetic traffic detectors were also embedded in both the Intervention and Control streets during the data collection to capture traffic volume and speed. Data collection ran concurrently, at both sites, for a total of 21 observational shifts over the same two-week period in June 2012 (pre-intervention), June 2013 (post-intervention) and June 2014 (follow up). Crossing behaviors were recorded during three hour-long shifts (7:30am, 12:30pm, and 3:30pm), while traffic data were collected continuously for 150 hours during the first week. Traffic sensors were unavailable at the Control Site in 2014. Descriptive statistics were calculated for all variables. Analysis of Covariance (ANCOVA) models assessed changes in crossing behaviors at each site from 2012 to 2014, controlling for temperature. Changes in traffic speed (above the speed limit/below the speed limit) and volume at each site from 2012 to 2014 were analyzed using Pearson’s Chi Square.

Results
Total pedestrian crossings at the Intervention site did not significantly change from 2012(n=1,408) to 2013(n=1,352) or 2014(n=1,380; p=0.561), but there was a significant year*designation zone interaction(p=0.018). Pairwise comparisons of the Designated Crossings indicated an overall increase between Years 2012(M=1.050) and 2014(M=1.248; p=0.012) and Years 2012(M=1.050) and 2013(M=1.233; p=0.033), but not between Years 2013(M=1.233) and 2014(M=1.248; p=0.995). Pairwise comparisons of the Non-Designated Crossings indicated no change overall between Years 2012 and 2014(p=0.533), Years 2012 and 2013(p=0.917), or Years 2013 and 2014(p=0.894). There was also a significant year*designation zone*race interaction (p<0.001).

Conclusions
This study suggests that street crossing infrastructure improvements can help support lasting changes in pedestrian behavior. These data may help inform decisions regarding future street-crossing interventions and could be used to guide policies promoting physical activity in similar communities where high-speed arterials are barriers to parks and active living.

Implications
By demonstrating increased pedestrian safety and traffic calming longitudinally, this study adds support to the feasibility of advocacy efforts to promote transportation practices that favor safe pedestrian accessibility over vehicular traffic. These successful outcomes could be used to support advocacy efforts seeking to modify the built environment to increase physical activity in underserved neighborhoods.

References

  1. Gordon-Larsen, P., Nelson, M. C., Page, P., & Popkin, B. M. (2006). Inequality in the built environment underlies key health disparities in physical activity and obesity. Pediatrics, 117(2), 417-424. doi: 10.1542/peds.2005-0058.
  2. Handy, S. L., Cao, X., & Mokhtarian, P. L. (2008). The causal influence of neighborhood design on physical activity within the neighborhood: evidence from Northern California. American journal of health promotion, 22(5), 350-358.
  3. Kaczynski, A., Mohammad, J. K., Wilhelm Stanis, S. A., Bergstrom, R., & Sugiyama, T. (2014). Association of street connectivity and road traffic speed with park usage and park-based physical activity American journal of health promotion, 28(3), 197-203. doi: 10.4278/ajhp.120711-QUAN-339.
  4. Schultz, C., Wilhelm Stanis, S.A., Sayers, S., & Thomas, I. (March, 2014). Oral presentation for the 2014 Active Living Research Annual Conference. San Diego, CA.
  5. Zhu, X., & Lee, C. (2008). Walkability and safety around elementary schools economic and ethnic disparities. Am J Prev Med, 34(4), 282-290. doi: 10.1016/j.amepre.2008.01.024.

 

Support / Funding Source
University of Missouri Research Board Grant

Authors: 
Courtney Schultz, MS, North Carolina State University
Location by State: 

Adolescent Physical Activity: Role of School Support, Role Models and Social Participation in Racial and Income Disparities

Date: 
02/24/2015
Description: 

Presentation at the 2015 Active Living Research Annual Conference.

Abstract: 

Background
Lack of physical activity is associated with obesity in youth. African-American, Latino, and low-income adolescents have higher rates of obesity and are less physically active than their white or more affluent counterparts. Previous research suggests that having a role model is associated with greater levels of physical activity among youth. Similarly, research has suggested that greater social participation, or civic engagement, is associated with physical activity. There is also evidence that social support in community settings can promote physical activity. However, few studies have focused on social support in the school setting. In addition, little is known about whether the effects of role models, civic engagement, and support at school are beneficial across racial/ethnic and income groups.

Objectives
This study used a population-based dataset to examine the extent to which role models, civic engagement and support at school promote physical activity among groups at risk for inactivity and obesity, specifically low-income youth and youth of color.

Methods
Data were from the 2011-12 California Health Interview Survey (CHIS), a random-digit dial (RDD) telephone survey of households drawn from every county in California. Analyses included responses from 2,799 adolescents ages 12-17. A validated self-report question was used to assess the number of days adolescents were physically active for 60 minutes or more. Regression analyses were used to examine the association of civic engagement, support at school, and role models with physical activity; stratified analyses examined variations by income and race/ethnicity. Analyses included the following factors: age, gender, race/ethnicity (white, Latino, Asian, African American and American Indian), household income, participation in clubs outside school besides sports, volunteer work in past year, feeling supported at school, and type of role model (family member, athlete, entertainer, teacher, friend, no role model).

Results
In California, adolescents were physically active for at least 60 minutes on an average of only 3.6 days in the last week. Adolescents from low-income households were active on fewer days than those from higher income households.

Conclusions
Civic engagement, feeling supported at school, and having a role model are associated with adolescent physical activity, and these factors also vary by race and income. Results from stratified regression analyses suggest that some of these factors may help promote physical activity among Latino, African-American, and low-income youth, groups at increased risk for physical inactivity and obesity.

Implications
Strengthening social support at school among low-income, Latino, and African-American youth may help promote physical activity in these groups. Information regarding variations by race and income in associations of role models, school support, and civic engagement with physical activity can inform programs and policies designed to reduce disparities in physical activity.

References
Yancey AK, Grant D, Kurosky S, Kravitz-Wirtz N, Mistry R. Role modeling, risk, and resilience in California adolescents. Journal of Adolescent Health. 2011;48(1):36-43.   Lindström M, Hanson BS, Östergren P-O. Socioeconomic differences in leisure-time physical activity: the role of social participation and social capital in shaping health related behaviour. Social Science & Medicine. 2001;52(3):441-451.   Kahn EB, Ramsey LT, Brownson RC, et al. The effectiveness of interventions to increase physical activity: A systematic review. American Journal of Preventive Medicine. 2002;22(4, Supplement 1):73-107.   Hohepa M, Scragg R, Schofield G, Kolt GS, Schaaf D. Social support for youth physical activity: Importance of siblings, parents, friends and school support across a segmented school day. Int J Behav Nutr Phys Act. 2007;4:54.

Support / Funding Source
This work was supported by a grant from The California Endowment.

Authors: 
Susan Babey, PhD, University of California, Los Angeles
Location by State: 
Study Type: 

SPARK Parks: Monitoring the Implementation and Impact of Schoolyards-turned-Community Parks

Date: 
02/24/2015
Description: 

Presentation at the 2015 Active Living Research Annual Conference.

Abstract: 

Background
The importance of physical activity to individual health is widely recognized, and empirical research shows that close-to-home access to parks and other recreational amenities can encourage higher levels of physical activity.[i] However, many Americans do not have parks close to home. Within the largest 60 U.S. cities, 31.7% of residents (over 16 million people) do not have access to a park within a 10-minute walk of their home[ii]; “high-need” neighborhoods (those with low-income, high-minority, and dense populations of children) tend to be particularly short of park space.[iii] Increasingly, schools and joint-use agreements (JUAs) are being used to provide essential recreational spaces and studied for their obesity prevention potential.[iv] The SPARK School Park Program, created in 1983 as a way to increase park space and access in Harris County, Texas, works to develop public schoolyards into community parks. Over 130 schoolyard-to-park conversions (“SPARK Parks”) currently exist within the county, and provide much needed park space to local residents. 340,000 people in Harris County live within a half-mile of a SPARK Park, and 129,917 people in Houston (6% of the total population) only have access to public park space within a 10-minute walk because a SPARK Park exists nearby. While access, design, and quality/condition of the built environment are understood to influence physical activity, there is a gap in the knowledge regarding what specific park features, characteristics, and policies most impact use and health.[v]

Description
Recently, The Trust for Public Land, a national non-profit land conservation and parks organization, partnered with the SPARK School Park Program to evaluate the use of SPARK Parks and to monitor the implementation of joint-use agreements. Direct observations using SOPARC: the System for Observing Play and Recreation in Communities are being conducted at all completed SPARK Parks and ten control parks.[vi] Evaluations measure use (number of people, age, and activity levels) and accessibility, and are taking place during time periods when the SPARK Parks are available for public use (during non-school hours and on weekends). An assessment tool, based upon the Community Park Audit Tool, CPAT, is also being used in all of these parks.[vii] In addition, a survey of park users is being conducted to gather additional information about the use of these parks, barriers to use, design preferences, and other perceived benefits or impacts of parks. This information about park access, features/characteristics, conditions, and use, will help evaluate the success of these joint-use agreements, and lessons learned will be developed in collaboration with SPARK and other stakeholders.

Lessons Learned
The data collected will be used to find strategies to maximize the impacts of current parks, and develop and provide recreation practitioners with evidence-based recommendations for creating active and engaging schoolyard parks. Data collection is currently underway, and will be completed in October 2014. This data will be used in the following, specific ways: (1) Evaluate the role (in terms of park access and park use) of SPARK School Parks and associated JUAs within the county's parks and open space system; (2) Assess how park features and characteristics contribute to park use and activity (with a focus on moderate and vigorous levels); and (3) Study SPARK service areas and explore the impact of potential new SPARK Parks.  Information about the current SPARK implementation process and use of these SPARK Parks, as well as new lessons learned and recommendations to improve the implementation of JUAs and schoolyard-to-park conversions, will be the focus of this presentation.

Conclusions
It is a unique opportunity to be able to monitor the implementation of joint-use policies and evaluate the impact of parks among such a large number of completed projects. Determining how existing schoolyard-to-park conversion programs successfully implement joint-use agreements and renovations is important for both maximizing the impact of existing programs, informing new programs, and providing information to researchers and practitioners alike. The data collected will also help inform park design and the creation of better-used, effective, and impact-maximizing park spaces.

Next Steps
The Trust for Public Land is currently documenting the need for new parks and identifying the most park-deficient neighborhoods in Harris County through our ParkScore methods. This on-the-ground measurement of park access and use could identify underserved areas and support the development of new SPARK Parks, help inform decisions regarding investments or reinvestment in park projects, and help to engage public agencies, elected officials, and nonprofit partners in decisions regarding the priorities and funding for improved park access and related policy implementation.

References

  1. Mowen A, Kaczynski AT, Cohen DA. The Potential of Parks and Recreation in Addressing Physical Activity and Fitness. President’s Council on Physical Fitness and Sports. Research Digest. 2008; 9(1). www.presidentschallenge.org/informed/digest/docs/march2008digest.pdf.
  2. Kaczynski AT, Henderson KA. Environmental correlates of physical activity: A review of evidence about Parks and Recreation. Leisure Sciences. 2007; 29(4):315-354.
  3. The Trust for Public Land. Data from ParkScore® index. The Trust for Public Land; 2014. http://parkscore.tpl.org/.
  4. Sherer PM. The Benefits of Parks: Why America Needs More City Parks and Open Space. San Francisco, CA: The Trust for Public Land; 2006. www.tpl.org/health-benefits-parks.
  5. Bocarro J, Kanters M, Edwards M, Suau L, Floyd M. Shared Use of School Facilities: A Systematic Observation of Facility Use and Physical Activity. [Presentation at the 2014 Active Living Research Conference].
  6. Kanters M, Bocarro J, Carlton T, Moore R, Floyd. After-school Shared Use of Public Facilities for Physical Activity in North Carolina. [Presentation at the 2014 Active Living Research Conference].
  7. Slater S, Chriqui J, Chaloupka F, Johnston L. The Pros and Cons of the Influence of Joint Use Agreements and Adolescent Physical Activity and Sedentary Behaviors. [Presentation at the 2014 Active Living Research Conference].
  8. Cohen D, Marsh T, Williamson S, et al. Parks and physical activity: why are some parks used more than others? Prev Med. 2010; 50(Suppl 1):S9–S12.
  9. Dunton GF, Kaplan J, Wolch J, et al. Physical environmental correlates of childhood obesity: a systematic review. Obes Rev. 2009; 10(4):393–402.
  10. Bedimo-Rung AL, Mowen AJ, Cohen DA. The Significance of Parks to Physical Activity and Public Health: A Conceptual Model. Am J Prev Med. 2005; 28(2 Suppl 2):159 –168.
  11. McKenzie TL, Cohen DA. 2006. SOPARC (System for Observing Play and Recreation in Communities) Description and Procedures Manual. http://activelivingresearch.org/sites/default/files/SOPARC_Protocols.pdf.
  12. Cohen DA, Setodji C, Evenson KR, Ward P, Lapham S, Hillier A, McKenzie TL. 2011. How much observation is enough? Refining the administration of SOPARC. J Phys Act Health; 8(8): 1117-23. http://www.ncbi.nlm.nih.gov/pubmed/22039130.
  13. Kaczynski AT, Wilhelm Stanis SA, GM Besenyi. 2012. Community Park Audit Tool (CPAT). http://activelivingresearch.org/sites/default/files/CPAT_AuditTool_v3.pdf.

 

Support / Funding Source
Funding for the SPARK School Park evaluation is provided by The Houston Endowment.

Authors: 
Bianca Shulaker, MPL, The Trust for Public Land & Kathleen Ownby, SPARK School Park Program
Location by State: 
Population: 

Factors Influencing Choice of Commuting Mode

Date: 
02/24/2015
Description: 

Presentation at the 2015 Active Living Research Annual Conference.

Abstract: 

Background
Walking and cycling are recommended forms of moderate-to-vigorous physical activity (MVPA) that can serve as means of travel to substitute for short car trips. Walking and cycling to work (active commuting) have the potential to be incorporated into commuters’ daily routine and might therefore be more easily adopted and maintained than other forms of physical activity. In addition, active commuting is specifically associated with reduced cardiovascular risk, physical fitness, and weight control in adults. The proportion of walking and cycling to work in the US (5%) is extremely low compared to many European countries, such as Denmark (31%), Germany (32%), the Netherlands (47%), and Switzerland (50%). The use of public transit usually involves walking or cycling to and from bus or train stations and has shown the potential to contribute to the commuter’s overall physical activity level. Despite that, public transit and multi-modal transit have been studied less as a mode choice compared to active commuting. In order to develop effective interventions to promote alternative commuting modes (other than car driving), an understanding of the factors associated with this particular behavior is required.

Objectives
Using data from a large sample of working adults in four Missouri metropolitan areas, this analysis examines the combined impact of self-reported home and worksite neighborhood environmental factors and worksite supports and policies on employees’ commuting modes.

Methods
The participants were from the Supports at Home and Work for Maintaining Energy Balance (SHOW-ME) study, a cross-sectional study designed to understand environmental and worksite policy influences on employees’ obesity status. Between 2012 and 2013, participants residing in four Missouri metropolitan areas were interviewed via phone and provided informations on socio-demographic characteristics. A subset of questions from the Physical Activity Neighborhood Environment Survey (PANES) was used to measure built environment features in the home neighborhood environment. Ten PANES questions were adapted to ask similar questions about the worksite neighborhood environment. Worksite supports and policies were determined using eighteen questions asking whether specific policies or features supporting physical activity were available at the worksite and if the participants ever used them. Commuting mode were self-reported and categorized into car driving, public transit, and active commuting (or multi-modal). Commuting distance was calculated using geographic information system. Multivariate logistic regressions were used to examine the correlates of using public transit and active commuting (or multi-modal) respectively, adjusting for selected significant covariates such as age, sex, BMI, education, marital status, number of children in the household, household income and household car ownership,. All analyses were performed using Stata version 12.0 (STATA Corp., College Station, Texas, USA).

Results
The majority of 1,338 included participants (69.3% women) reported commuting by driving (88.9%); while only 4.9% used public transit and 6.2% used active modes. In final adjusted models, living within 10-15 minutes walking distance from a transit stop is associated with higher likelihood of using public transit (3.78, CI 95%: 1.00-14.9) compared to those home neighborhoods without transit stops within walking distance. Employees who reported ever having used worksite incentives to use public transit had a higher likelihood of using public transit modes (23.9, CI 95%: 10.4 – 54.8) compared to those whose worksites provide no such incentive. For multi-modal or active commuting mode, living 10 miles or further from work is associated with less likelihood (0.12, CI 95%: 0.05 – 0.29) of using any active mode to commute compared to commuters who drive. While having free or low cost recreation facilities around the worksite is associated with higher likelihood (1.85, CI 95%: 1.03 – 3.32) of using active commuting mode. In addition, reporting having ever used the bike facility to lock bikes at the worksite is associated with higher likelihood (9.17, CI 95%: 3.84 – 21.8) of using active commuting mode.

Conclusions
Both environmental factors and worksite supports and policies are associated with the use of public transit, active commuting or multi-modal transportation. These findings add to the body of research evidence on the promotion of alternative commuting mode other than car driving, in order to promote physical activity in the employed population at large. Using longitudinal design, future studies should explore the potential of alternative commuting mode interventions, including policies and supports that involve worksites effort.

Implications
While an improvement to the built environment may require long-term effort, worksite supports and policies such as incentives and safe bike storage could be implemented in the short-term with minimum effort. The prevalence of active commuting in the US as well as our study sample is noticeably lower than many European countries. Thus there is a potential to implement and evaluate the cost-effectiveness of worksite supports and policies in promoting alternative commuting mode other than car driving, as well as the longitudinal impact on wider health outcomes and productivity associated with active commuting.

Support / Funding Source
The SHOW-ME study is supported by the Transdisciplinary Research on Energetics and Cancer (TREC) Center at Washington University in St. Louis. The TREC Center is funded by the National Cancer Institute at NIH (U54 CA155496), Washington University and the Alvin J. Siteman Cancer Center. We acknowledge all the participants in this study. We thank to Dr. Jung Ae Lee for providing statistical consultant for this work.

Authors: 
Lin Yang, PhD, Washington University in St. Louis
Location by State: 
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Worksite Policies and Supports for Physical Activity

Date: 
02/24/2015
Description: 

Presentation at the 2015 Active Living Research Annual Conference.

Abstract: 

Background
The etiology of obesity is believed to be multi-factorial, including genetic, metabolic, behavioral, psycho-social, and environmental influences. Individual behaviors that directly affect energy balance include diet and physical activity (PA), which are influenced by larger psycho-social, environmental, organizational, and policy factors. If the factors responsible for physical inactivity and obesity at multiple levels can be better understood, we can identify more appropriate targets for dissemination and implementation. Because many employed adults spend at least half of their waking hours at work, worksites are excellent venues for health promotion. Due to rising costs of healthcare associated with obesity-related illness and disability, there is interest among employers in offering programs or benefits to assist employees in making healthful decisions. A particularly promising type of worksite health promotion strategy involves environmental and policy changes that may assist employees in making healthful choices at work (e.g., easy access to stairways, on-site exercise facilities, time or breaks for PA during the work day).

Objectives
The overall goal of this project is to understand how environments and policies where employed adults work are associated with energy balance. Here we examine whether specific types of worksite supports for PA are predictive of total and domain-specific PA.

Methods
Participants were from the Supports at Home and Work for Maintaining Energy Balance (SHOW-ME) study, a cross-sectional study to understand environmental and worksite policy influences on employees’ obesity status. Census tracts in four Missouri metropolitan areas (St. Louis, Kansas City, Springfield, and Columbia) were used for sampling. Between 2012 and 2013, 2,015 participants were recruited who met each of the following criteria: between the age of 21 and 65 years; employed outside of the home at one primary location; employed for 20 or more hours per week at one site with at least five employees; not pregnant; and no physical limitation to prevent walking or bicycling in the past week. Recruited participants completed a telephone-based survey. The survey instrument was developed using existing self-reported and environmental assessment instruments and input from a Questionnaire Advisory Panel. Worksite supports for PA included 18 unique items (e.g., ‘Does your workplace offer…’ ‘Incentives to use public transit, such as free or reduced transit pass,’ ‘Flexible time for PA during the work day’) as well as specific usage questions for 14 of the 18 items (e.g., ‘Have you used X in the past two months?’). PA and PA sub-domains (travel, work, and leisure) were measured using the International Physical Activity Questionnaire long form.  Analyses include unadjusted and adjusted odds of meeting domain-specific and total PA CDC recommendations (150 minutes per week), provided access and use of the 32 worksite support questions. Analyses adjusted for race, gender, age, income, employer size, self-reported health, obesity, and hours worked per week. Limited stratified results have been completed. Cumulative results (e.g., access and use to incentives to bike/walk to work AND access to a shower) are forthcoming.

Results
Access to five of 18 worksite supports for PA were significantly associated with increased odds of meeting the CDC’s recommended 150 minutes of moderate and vigorous PA. These were access to bike storage, flextime for PA, PA breaks during meetings, incentives to bike/walk to work, and maps or signs of worksite walking routes. Nine of 14 use of PA supports were associated with significant odds of meeting PA recommendations, including use of bike storage, shower at work, and outdoor exercise facilities. Of specific interest was the worksite supports associated with travel-domain PA. In unadjusted analyses, access to bike storage was associated with a 1.31 (95% CI: 1.03, 1.65) increase in odds of obtaining 150 minutes of PA during travel alone. Using bike storage was associated with a 4.40 (2.75, 7.02) increase in odds of meeting 150 minutes of PA. After adjustment the odds of obtaining 150 minutes of travel PA were only reduced to 4.32 (2.48, 7.52). For age groups there was a stepped progression in odds of meeting 150 minutes of travel PA. Odds ratios for meeting 150 minutes of travel PA were 4.06, 4.68, and 8.14, respectively for employees under the age of 45, 45-54, and older than 55 years. Each result was significant at p<0.05.

Conclusions
Access to and use of specific worksite policies and supports for PA increase the likelihood of employees meeting the CDC’s recommendation of 150 minutes of PA per week. The use of supports had greater associations with PA than mere access to supports, suggesting future research and intervention efforts should be primed to move from awareness of supports for PA to regular use of the supports.

Implications
Worksite wellness plans are on the rise across the US with worksites eager for evidence-based supports for increasing PA and reducing sedentary time. Our team is working to package results such that worksites can be informed of likely benefits associated with 18 unique supports as well as costs of specific supports.

Support / Funding Source
U54 CA155496-01 (Colditz, Center PI; Hipp, Project PI) NCI/NIH.  ‘A multilevel approach to energy balance and cancer across the life course: worksite policies and neighborhood influences on obesity and cancer risk.’

Authors: 
J. Aaron Hipp, PhD, Washington University in St. Louis
Location by State: 
Population: 
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Promoting Science in the Public Health Policy Process: Tools and Resources for Researchers, Practitioners, and Advocates

Date: 
02/22/2015
Description: 

Workshop at the 2015 Active Living Research Annual Conference.

Abstract: 

The need for policies and environments that promote population-wide increases in physical activity is urgent, given that less than 50% of U.S. adults meet the recommendations for moderate and/or vigorous physical activity. There are various ways to engage in the policy process ranging from educating policymakers about existing research, to promoting evidence-based policymaking, to ensuring that policies are implemented well. Such insights are critical for realizing greater use of available evidence in active living policy decisions. This workshop highlighted evidence about how science is used to inform policy decisions and implementation practices, and complemented the existing literature with their own practice-based experiences. Participants learned how researchers and practitioners can more effectively engage in the different stages of the policy process to promote active living research. Participants practiced what they learn during small group and role-play exercises. This interactive workshop involved didactic instruction and provided participants with concrete strategies and skills needed to advance evidence-based and evidence-informed active living interventions throughout the policy process.

Authors: 
Keshia M. Pollack, PhD, MPH & Shannon Frattaroli, PhD, MPH, Johns Hopkins Bloomberg School of Public Health
Location by State: 
Population: 
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Translating Research into Policy: New York City's Executive Order on Active Design

Date: 
02/23/2015
Description: 

Presentation at the 2015 Active Living Research Annual Conference.

Abstract: 

Background
The US Task Force on Community Preventive Services has concluded that there is sufficient and strong evidence that environmental and policy change interventions increase physical activity.(1) In 2010, 12 NYC government agencies along with academics, professional associations, private sector design professionals, and community organizations collaborated to develop the Active Design Guidelines (ADG), evidence-based and best-practice strategies for increasing opportunities for physical activity in the design of buildings, neighborhoods, and streets.(2) Over 25,000 copies of the ADG have been distributed to architects, urban planners, developers, and other built environment and health professionals globally. Using grant funding obtained by the Department of Health and Mental Hygiene (DOHMH), government agencies collaborated with the local chapters of professional associations such as the American Institute of Architects, the American Planning Association, and the US Green Building Council to develop trainings for the design community, and with community partners to encourage incorporation of ADG strategies into building and urban design projects.  NYC government designs, builds, renovates, and maintains neighborhoods, streets, parks, and buildings that are used by millions of people. As such, local government can act as an agent of change by incorporating ADG strategies routinely into its own practices.

Description
In 2012, NYC’s Obesity Task Force, comprised of representatives from 11 City agencies and the Mayor’s Office, recommended that the City increase opportunities for active living by establishing a policy to require review of all City projects for incorporation of ADG strategies. Many City agencies had already begun voluntarily incorporating ADG strategies into projects, demonstrating the feasibility of more broadly implementing a policy to incorporate these concepts into City practice. In June 2013, Executive Order (EO) No. 359, Incorporating Active Design Principles in City Construction, was signed into effect by Mayor Bloomberg.(3) Under the EO, City agencies are instructed to review all City capital projects, including construction and major renovations, to identify opportunities for incorporating active design strategies per the ADG and the City’s Street Design Manual. The EO requires that information be provided on the ADG and encourages the use of active design strategies in relevant City guidelines, standards, and handbooks used for the design and construction of the City’s built projects. Projects undergoing LEED green building certification are also directed to incorporate, wherever applicable, the Pilot Credit “Design for Active Occupants,” which offers a menu of building features to promote stair use and active recreation. The EO also requires agencies to assess opportunities to promote stair use in City buildings, including designating a stairway for public access and installing signage encouraging stair use. Finally, the EO requires the Department of Design and Construction (DDC), in consultation with DOHMH, to coordinate trainings on the ADG for staff of City agencies involved in design and construction.  City construction and renovation projects for offices, public buildings, and streets are now reviewed for inclusion of active living-promoting strategies during the design process. Requests for proposals, like those issued by the NYC Department of Housing Preservation and Development, will also include reference to the ADG where practicable. To continue trainings for relevant City agency staff, DDC contracted with the Center for Active Design, a nonprofit organization that promotes active design.

Lessons Learned
Garnering input and buy-in from impacted City agencies was important to the success of the policy. The EO was developed and vetted by all City agencies that play key roles in building, renovating, and maintaining City buildings and streets. To educate City agencies about the EO, a kick-off meeting was hosted by the Deputy Mayors of Health and Human Services and of Operations, demonstrating commitment from Mayor’s leadership. Trainings for both City agency staff and design professionals are necessary to increase understanding of the policy and build demand for buildings that incorporate active design elements. Partnerships with relevant non-profit and professional organizations are valuable for promoting active design strategies and training the design community.

Conclusions
In NYC, executive authority was used to encourage the integration of evidence-based active design strategies into public policy, potentially impacting a wide variety of settings and promoting use of these strategies in the City’s built environment for decades to come. This is one example of how health-promoting strategies can be routinely integrated throughout city practice. Other jurisdictions should consider the use of executive action to increase systematic incorporation of evidence-based active living elements into their built environment.

Next Steps
DOHMH and DDC are collaborating to track incorporation of ADG strategies in the design of City office buildings and public facilities. Future policy efforts may be informed by these data.

References

  1. Guide to Community Preventive Services. Increasing physical activity: environmental and policy approaches. Last updated: September 27, 2013.
  2. Lee KK. Developing and implementing the Active Design Guidelines in New York City. Health and Place. 2012;18(1):5-7.
  3. Executive Order No. 359. Incorporating Active Design Principles in City Construction. June 27, 2013.
Authors: 
Megan Lent, MPH, New York City Department of Health and Mental Hygiene
Location by State: 
Population: 
Study Type: 

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