Parks & Recreation

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Parks and recreation facilities provide opportunities for physical activity and can help people of all ages lead a more active lifestyle. People who live near parks are more likely to be active. However, some lower-income communities and communities of color tend to have less access to quality parks and recreation facilities. Our research documents the most effective ways to improve the design, quality and availability of parks and recreation resources. Making recreational facilities accessible in all communities is a critical strategy for increasing physical activity and preventing obesity.

Download our Parks and Recreation-related Resources Sheet for the best evidence available about a variety of park- and trail-based strategies for promoting physical activity.

View The Role of Parks and Recreation in Promoting Physical Activity infographic.

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Measuring Perceived Environments through Ecological Momentary Assessment: Correspondence with Objective GIS Indicators

Date: 
03/12/2014
Description: 

Presentation at the 2014 Active Living Research Annual Conference.

Abstract: 

Background and Purpose
Perceived neighborhood traffic and greenness are important variables that have been examined in prior research correlating the built environment with health-related behaviors such as walking and exercise. In general, vehicular traffic presents hazards to pedestrians that limit participation in physical activity and has been associated with poorer health in adults and children. Neighborhood greenness improves perceived aesthetics and access to natural shade encouraging individuals to engage in physical activity and subsequently better overall health in the population. Studies typically use retrospective survey methods to measure subjective perceptions of the neighborhood environment (e.g., Neighborhood Environment Walkability Survey [NEWS]). However, this approach may be prone to recall biases, condense perceptions of multiple micro settings into one overall neighborhood rating, and take into account parts of the neighborhood that are never or not regularly encountered. Ecological momentary assessment (EMA) provides an alternate way to measure the perceived environment by collecting real-time assessments of one’s immediate setting on mobile devices. However, an important first step to using EMA measures of the perceived environment is to examine the extent to which they correspond to objective measures of the built environment such as Geographic Information System (GIS) indicators

Objectives
The primary purpose of this study was to assess the convergent construct validity of EMA self-report of perceived traffic and greenness. To address this objective, the study analyzed EMA items measuring adults’ perceptions of nearby traffic, greenery, and shade. These items were compared respectively to objective measures of traffic (all vehicular collisions) and greenness (Normalized Difference Vegetation Index) near each participant’s place of residence.

Methods
The study sampled adults from the first two waves of data from an ongoing study investigating the effects of environmental and interpersonal factors on health behavior decision-making. The participants carried mobile devices (HTC Shadow) with a custom EMA application that prompted for surveys eight times a day for a period of four days per wave. Each wave was separated by six months. Participants were asked a random subset of questions from a larger survey in order to limit time spent taking the short assessment. One item rated the perceived level of traffic (“How much TRAFFIC is on the closest street to where you are right now?”) and two items rated the perceived level of greenery (“How many TREES AND PLANTS are there in the area where you are right now?” and “How much SHADE FROM THE SUN is there in the area where you are standing right now?). Using ArcGIS, a 3000 meter street network buffer was created around each subject’s place of residence to measure NDVI in 20XX and all collisions after the year 2006. Three multilevel regressions examined the validity of EMA-reported perceived traffic against GIS-derived vehicular collisions, EMA-reported perceived greenness variable against GIS-derived NDVI, and EMA-reported perceived shade variable against GIS-derived NDVI. Time (wave) was treated as a covariate in each equation.

Results
The final sample consisted of 43 individuals with a total of 165 observations after exclusions. Eighty-one percent of subjects were female and 35% of subjects reported being Hispanic, with ages ranging from 29 to 59. After adjusting for wave, the positive association between EMA-reported perceived greenness and NDVI was statistically significant, B=11.59, p=0.008. EMA-reported perceived traffic was positively associated with vehicular collisions after adjusting for wave, B=0.02, p=0.016. The positive association between EMA-reported perceived shade and NDVI was marginally significant after adjusting for wave, B=4.13, p=0.087.

Conclusions
Results from this study provide initial evidence of the construct validity of EMA-reported perceptions of neighborhood traffic and greenness. EMA-reported perceived greenness (i.e., trees or plants) corresponded with an objective measure of greenness (NDVI). Likewise, the EMA-reported variable for perceived traffic was validated against an object measure of traffic (i.e., vehicular collisions). The findings are consistent with prior research in children validating the EMA-reported variables against parent-reported retrospective traffic and aesthetics on the NEWS. However, there was only partial validation of the EMA-reported variable for shade against objective greenness. One explanation for this could be vagueness of the question. While shade from the sun often refers to trees and other natural shrubbery, there are many situations where shade could come from tents, tall buildings (such as during sunset or sunrise), or other manmade structures (e.g. parking garages). These findings offer support for the construct validity of EMA items measuring perceived greenness and traffic and suggest that the EMA-reported item measuring shade may not be necessary when attempting to measure greenness, although future studies could validate the shade measure against a real-time UV monitor.

Implications for Practice and Policy
By showing construct validity, these findings allow future practice and policy research to utilize EMA measures for perceived greenness and traffic, thereby eliminating major hurdles such as recall bias.  New studies will be able to show more conclusive findings on greenness and traffic by correlating perceived micro settings with health behaviors.

Support / Funding Source
American Cancer Society (118283-MRSGT-10-012-01-CPPB), the National Heart Lung and Blood Institute (R21HL108018 ), and the National Cancer Institute (R01CA123243).

Authors: 
Eldin Dzubur, MS, Yue Liao, MPH, Mary Ann Pentz, PhD, & Genevieve Dunton, PhD, MPH
Location by State: 
Study Type: 

Spatial Profiling: A Latent Profile Analysis of Obesogenic Activity Spaces and Adult BMI

Date: 
03/12/2014
Description: 

Presentation at the 2014 Active Living Research Annual Conference.

Abstract: 

Background and Purpose
In spite of progress on the issue, obesity remains among the most challenging health issues of our time.  Features of the built environment can contribute to obesity by increasing the real or opportunity costs of healthy food choices and physical activity.  The term “obesogenic environment” describes geographic areas that promote obesity across multiple domains—too much fast food, not enough fresh food, and not enough support for physical activity (1).  However, the effect of obesogenic environments on the actual weight status of those exposed to them has not been conclusively established.   Most research has focused on either single objective built environment indicators, subjective ratings of walkability, and/or conventional residential buffers to characterize neighborhood-level risk. In this study, we used multiple objective GIS measures to generate latent profiles of neighborhood risk, with neighborhood defined activity spaces rather than residences.

Objectives
Using a sample of 460 adults in Southern California, we sought to discover a typology of obesogenic risk in the built environment, and determine whether these categories of places predicted physical activity and weight status.

Methods
We used Wave 1 data from 460 adult respondents in two control groups from Healthy PLACES, a natural experiment which has the overarching goal of examining the effects of “smart growth” community design principles on obesity outcomes.  Study participants had at least one school-aged child who also participated in the study.  We used 7 measures of the built environment derived from a GIS: vegetation index, residential/commercial land use mix, fast food restaurants, parks, street connectivity, and traffic accidents involving pedestrians or cyclists.  To define neighborhood of likely exposure, we created stadium-shaped 1-mile buffers around the line connecting adults’ homes and the school at which their child is enrolled.  Since most adults spend a large amount of their time outside the immediate vicinity of home, but within a few miles of it (2), we took these buffers as a proxy for the local geography to which the study participants are likely to be exposed on a regular basis.  We also used self-reported data on age, gender, and educational attainment; anthropometric measures; and physical activity collected using an accelerometer over a 7-day study period.

Analysis proceeded in two phases.  Our first analytic step was to enter the set of 7 continuous area measures into a latent profile analysis.  A latent variable modeling approach can be used to identify unobserved subgroups among a set of continuous characteristics, in this case, characteristics of the built environment that have been linked to obesity.

In the second stage of analysis, we entered the categories of neighborhood identified in the first step as independent variables in a regression model.  Our outcomes in separate models were moderate-to-vigorous physical activity (MVPA) measured by accelerometer, body mass index (BMI), and waist circumference.  Final models were adjusted for age, gender, and educational level, an indicator of socioeconomic status.

Results
Latent profile analysis identified four distinct unobserved environmental profiles.  We expected neighborhoods classified as Profile 2 to be the most obesogenic, with low greenness, high proportion commercial land use, and the highest rate of pedestrian and bike accidents.  Neighborhoods fitting in Profile 3 seemed the least obesogenic, with high greenness, low proportion commercial, low pedestrian and bike accidents, and moderate street connectivity.  Bivariate analysis confirmed that participants in Profile 2 had the highest weight status and lowest MVPA; and those exposed to Profile 3 had the healthiest weight status and highest MVPA.  These differences were significant.

Membership in a Profile 3 neighborhood was significantly associated with lower BMI and nearly 20 minutes per day of additional MVPA, compared to membership in a Profile 2 context.  However, the results did not remain significant after adjusting for gender, age, and education.

Conclusions
Our primary aims were to find latent profiles of built environment obesity risk factors, and test whether these profiles were associated with increased risk for obesity.  Using LPA, we identified four distinct profiles of neighborhood obesogenic risk.  Furthermore, these context types were marginally predictive of obesity and physical activity among the adults who experienced them.  Our results suggest that latent profile analysis can uncover latent clusters of risk factors for obesity in the built environment.  Our results also suggest that the experience of built environment as a factor in obesity is complex and multidimensional.  Further research should focus on the interrelationships between many environmental exposure factors, and the possibility that they interact with one another.

Implications for Practice and Policy
As we work to change the obesity risk environment, we should consider that environments are multifactorial.  Strategic small changes in more than one dimension of the built environment may be able to shift the overall obesogenic profile of an area.  Also, we should be mindful of the fact that people do not spend all of their time at home; they experience a range of places and may self-select environments based on their own priorities.

References

  1. Saelens BE, Sallis JF, Frank LD, Couch SC, Zhou C, Colburn T, et al. Obesogenic neighborhood environments, child and parent obesity: the Neighborhood Impact on Kids study. Am J Prev Med 2012;42(5):e57-64.
  2. Jones M, Pebley AR. Redefining Neighborhoods Using Common Destinations: Social Characteristics of Activity Spaces and Home Census Tracts Compared. In press, Demography 2013.

 

Support / Funding Source
This research was supported by the National Cancer Institute (Grant T32CA009492-280).

Authors: 
Malia Jones, MPH, PhD, Jimi Huh, PhD, Donna Spruijt-Metz, MFA, PhD, Genevieve Dunton, MPH, PhD, & Mary Ann Pentz, PhD
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Accountable Care Organizations, Physicians, and Private-Public Partnerships for Active Design

Date: 
03/12/2014
Description: 

Presentation at the 2014 Active Living Research Annual Conference.

Abstract: 

Background and Purpose
How can the Affordable Care Act benefit the neighborhood built environment? Accountable care organizations (ACO’s), a relatively new model of healthcare delivery, may be a critical component to the multidisciplinary partnerships necessary to build healthy communities. The model, which rewards doctors and hospitals for health maintenance rather than health care provision, is a logical outgrowth of health reform measures designed to improve patient outcomes and reduce costs. In the wake of the new law, as health care systems reinvent themselves to maintain viability and profitability, ACOs will continue to proliferate across the nation, presenting a timely opportunity for organizations looking to move active living research into built realities.

As defined by a task force of the American Academy of Family Physicians, an ACO is “a primary care-based collaboration of health care professionals and health care facilities that accept joint responsibility and accountability for the quality and cost of care provided to a defined patient population.” They are a relatively new phenomenon; currently ACOs now number more than 400, but cover four million Medicare enrollees and millions more people with private insurance.

Because ACO profits will be tied to keeping their patient population healthy, and recruiting health-minded patients to select their ACO, these healthcare organizations are expected to play increasingly active roles in promoting community health by aligning with public health, local government community development departments and community-based organizations(CBO. Armed with new growing empirical evidence on the relationship between the built environment and preventative health behavior, ACO’s can potentially help fund and direct neighborhood health programs such as tree planting initiatives, retrofitting parks with walking paths, or sponsoring farmers’ markets. ACO’s can also influence community and regional health by providing grant match dollars needed for transportation projects to improve transit access, close sidewalk gaps and advance complete streets. Supporting this type of neighborhood, community and regional development can further improve health, supports the work of physicians in encouraging consumers to increase physical activity, and reduces the need for costly medical care.

Description
This research collaboration which includes professionals and researchers at Sutter Eden Medical Center, Kaiser Permanente, and Design 4 Active Sacramento (D4AS), a community-based organization and advisory council in Sacramento, California, discusses the nature and growth of ACO’s in the wake of the Affordable Care Act, and its potential for active living initiatives. Using our current work in Sacramento as a case study, we outline how we have already established partnerships between public health, local government, and community based organizations to fund and implement interventions in the built environment.

Lessons Learned
D4AS has already begun the process of implementing active design guidelines and programs such as improved access to transit, complete streets initiatives, sidewalk gaps closures, and a Safe Routes to School initiative. We discuss how they have leveraged these guidelines and programs into existing infrastructure and new development projects by strategically reaching out to other agencies and organizations and focusing on the monetary benefits of active design, from attaching “price tags” to and quantifying benefits of these programs for outside investment, to finding and structuring federal grant match programs.

Conclusions and Implications
By examining both the challenges and potential in healthcare provider partnerships and quantifying costs and benefits of active design implementation, we aim to lead a practical discussion on beginning to translate the vast research on active living into realized projects in a new era of healthcare healthcare delivery.

Next Steps
We outline our current and future efforts in integrating healthcare providers, with a specific focus on ACO’s, in the wake of the Affordable Care Act.

References

  1. Lowery, A. (2013, April 24). A Health Provider Strives to Keep Hospital Beds Empty. New York Times, p. A1.
  2. Bovbjerg, R. R., Ormond, B. A., & Waidmann, T. A. (2011). What Directions for Public Health under the Affordable Care Act? Urban Institute Health Policy Center.
  3. Rittenhouse DR, Shortell SM, Fischer ES. Primary care and accountable care – two essential elements of delivery-system reform. N Engl J Med 2009; 361(24): 2301-2303.
  4. Shortell, S. M. (2013). Bridging the Divide Between Health and Health. JAMA, 309(11), 1121-1122.

 

Support / Funding Source
The Design 4 Active Sacramento team was one of 20 teams nationwide chosen this year by the US Centers for Disease Control to participate in the National Leadership Academy for the Public’s Health.

Authors: 
Sara Carr, MArch, MLA, Edie Zusman, MD, FACS, FAANS, MBA, & Judy Robinson
Location by State: 
Population: 

Learning from the Economists: Using an Elasticity Analysis to Assess Changes in Screen-time in Response to a Temporary Pop-up Park in California

Date: 
03/12/2014
Description: 

Presentation at the 2014 Active Living Research Annual Conference.

Abstract: 

Background and Purpose
Screen-time is a common proxy measure for sedentary behavior, which is a risk factor for certain chronic diseases independent of moderate-to-vigorous physical activity (1,2). Park use and park exposure have been positively associated with leisure-time physical activity (3), but few studies have documented the relationship between park availability and screen-time (4).  Cross-elasticity is a common measure in economic research to report the change in demand of an item given an increase in price of another item. In active living research elasticity analysis can been used to understand changes in time-allocation patterns in relation to built environmental changes (5). Natural experiments have been identified as a research priority to understand the causal relationship between built environment features (like park availability) and changes in health behaviors. During the summer of 2013, we took advantage of a natural intervention involving a temporary modification of the urban landscape of downtown Los Altos, California (28,976 inhabitants), where a main street block was closed due to construction and transformed into a temporary park. During this time, we conducted a study to examine the relationship of park exposure with time allocated to screen time and other activities.

Objectives
To estimate the type and prevalence of activities that temporary park-use displaced (defined as alternate activities); and to conduct a cross-elasticity analysis to estimate the number of minutes of alternate activities (screen-time, time spent at any different park, time spent in downtown Los Altos, time spent outdoors) gained or lost due to the presence of the temporary street park.

Methods
Researchers from the Stanford Prevention Research Center designed and conducted an intercept survey for users of the temporary street park of downtown Los Altos. Surveys were administered during a four day period, including two week days and two weekend days. Survey respondents reported the amount of time that they intended to spend at the park that day, as well as the type of activity they would regularly be doing during that time if the temporary street park were not available (i.e., alternate activities).  Alternate activities were coded as the following binary variables: screen-time vs. non screen-time, spending time in a park vs. spending time elsewhere, spending time in downtown Los Altos vs. spending time elsewhere, and spending time outdoors vs. spending time indoors. Prevalence was estimated per category.  To obtain cross-elasticity estimates, we ran multivariate linear regression models using minutes of screen-time, minutes of total park use, minutes of outdoor time, and minutes spent in downtown Los Altos as the dependent variables; park availability was used as the independent (exposure) variable. All models controlled for effects of sex and age. The regression coefficients for the effect of park availability represented the number of minutes gained or lost per alternate activity due to park presence.

Results
A total of 147 park-users were surveyed. Of the sample, 62.5% were female, 5.5% were children, 6.8% were adolescents, 66.7% were adults and 20.4% were seniors.  Among survey respondents, 15.0% reported that if the temporary park was not available they would be spending that time in front of a screen. Meanwhile, 64.6% would regularly be at an indoor location at time of survey, 40.8% would be spending time in downtown Los Altos regardless of temporary park availability, and 15.7% would be spending time at another park. The elasticity analysis revealed that among survey respondents, the presence of the temporary park was associated with 77.4 fewer minutes of screen-time, 72 additional minutes of time spent at a park, 88.6 additional minutes of time spent in downtown Los Altos, and 76.1 additional minutes of outdoor-time.

Conclusions
Our results indicate that the presence of a temporary 1-block street park located in the heart of the downtown shopping district of Los Altos, California was associated with positive changes in time-allocation patterns, including a significant reduction in screen-time and a significant increase in time spent in a park. Larger studies are needed to estimate the elasticities of time-allocation in relation to the availability of public spaces, such as parks, to verify our findings. Future studies should also take place in locations where environmental changes are intended to be long-term/permanent.

Implications for Practice and Policy
This study provides valuable evidence supporting the creation of parks and open spaces in high land-use mix urban areas. Our findings (more time spent in downtown Los Altos due to park availability) also suggest that the presence of public recreation spaces may have benefits beyond health behaviors, and may also contribute towards the revitalization of downtown shopping districts in small cities. These potential benefits should be further explored, as they may be more likely to influence the decisions of stakeholders for the creation of parks than health-related benefits.

References

  1. Hamilton, Marc T., et al. "Too little exercise and too much sitting: inactivity physiology and the need for new recommendations on sedentary behavior." Current cardiovascular risk reports 2.4 (2008): 292-298.
  2. Katzmarzyk, Peter T., et al. "Sitting time and mortality from all causes, cardiovascular disease, and cancer." Med Sci Sports Exerc 41.5 (2009): 998-1005.
  3. Cohen, Deborah A., et al. "Contribution of public parks to physical activity." American Journal of Public Health 97.3 (2007): 509-514.
  4. Epstein, Leonard H., et al. "Reducing Sedentary Behavior The Relationship Between Park Area and the Physical Activity of Youth." Psychological science 17.8 (2006): 654-659.
  5. Olds, Tim, et al. "The Elasticity of Time Associations Between Physical Activity and Use of Time in Adolescents." Health Education & Behavior 39.6 (2012): 732-736.
Authors: 
Deborah Salvo, PhD, Jylana Sheats, PhD, Jorge Banda, PhD, Sandra Winter, PhD, Martell Hesketh, Nkeiruka Umeh, & Abby King, PhD
Location by State: 

A Multi-level Analysis Showing Associations between School Neighborhood and Child Body Mass Index

Date: 
03/11/2014
Description: 

Presentation at the 2014 Active Living Research Annual Conference.

An animated version of this presentation can be viewed here.

Abstract: 

Background and Purpose
Environmental effects on child health, including obesity, are well established. Studies examining associations between schools and child health largely have focused on the immediate school environment (e.g., cafeteria, schoolyard). Accordingly, Harrison and Jones, call for conceptualizing school environments more broadly. In particular, they propose a multi-tiered model that includes the infrastructure of schools themselves, but also surrounding neighborhoods, since even children not residing in those areas nonetheless regularly traverse them.  Most importantly, if the areas surrounding schools have significant influence on student health, they may represent anchors around which to more efficiently deploy resources for environmental improvements. Additionally, inconclusive findings from previous studies, most examining food environments, warrant additional study of school neighborhoods.

With a large sample (n=12,118) of racial/ethnically diverse elementary school children, this study used hierarchical linear modeling to estimate the associations between objective assessments of low-income urban school neighborhoods and measured BMI expressed as BMI percentile. Of critical importance, this study accounted for individual-level factors such as race, gender, and age, to more robustly estimate the effects of park or fast-food density, population change, and other community-level health indicators.

Objectives
To examine associations between environmental aspects of neighborhoods surrounding schools and childhood body mass index percentile (BMIp) using a sophisticated hierarchical design to improve the validity of the results.

Methods
Health data were collected from elementary students as part of a non-profit program offering health screenings, education, and referrals.  Data at the community/neighborhood level was collected from various databases including the U.S. Census and the Walkscore website.  The student data used in this analysis was collected in the 2008-2009 academic year and contained 46 different schools falling in 25 unique zip codes. The distribution across grade levels was relatively even, ranging from 2,123 students in the kindergarten cohort (17.5%) to 1,894 in the fifth grade cohort (15.6%).  The schools were heavily minority (41.76% black, 33.28% Hispanic, 21.89% white, and 3.07% other), with 49.07% female and 50.93% male.

Our data have a two-level hierarchical structure where individuals are nested within school/neighborhood. Specifying the model in this way is important because ignoring the clustering effect can lead to false positives in hypothesis testing, something that calls into question some previous work in this area. We used HLM, which takes into consideration the intraclass correlation between individuals within the same cluster and adjusts for its effect accordingly. Therefore, it produces more appropriate significance tests while simultaneously examining the effects of variables at both individual and group levels.

Results
While race, age, and sex remained predictive, the presence of parks and fitness facilities were associated with additional reductions in BMI percentile.  Similarly, the number of fast food restaurants predicts higher BMI percentile, as do declining populations, which likely signal urban decay of some sort.  More complex relationships manifest among some other community-level variables in the models.  While many call for increased access to grocery stores, particularly in efforts to assist “food deserts,” our analysis shows that access does not necessarily promote health, at least among children.  That is, the positive relationship between grocery stores and BMI percentile among children illustrates the need for changing not only access to them, but also likely the types of foods they offer and ultimately the food choices of the consumers who use them. The latter would presumably focus on parents. Similarly, convenience stores often are regarded as having a preponderance of unhealthy food choices.  That they manifest in our analysis as health promoting likely has less to do with the convenience stores themselves, and more to do with the density of retail and shopping in areas, which have been show to promote physical activity (mainly walking) resources. This is additionally evidenced by the effect of population size itself, where higher population densities may correspond to greater numbers of destinations within neighborhoods.

Differential estimates of BMI percentile among children based on the results of Model 2 are particularly illuminating.  Using the values from our data, the predicted BMI percentile at age 10 by race/ethnicity and sex shows a 15- point drop in BMI percentile for “obesogenic” vs. “non-obesogenic” neighborhoods.

Conclusions
This paper demonstrates that aspects of the environment in the neighborhoods surrounding schools indeed are associated with childhood BMI percentile, pointing to the fact that they should be regarded as significant zones of health influence for children.

Implications for Practice and Policy
Where redevelopment efforts have previously focused on classically defined neighborhood boundaries, our study suggests that neighborhood redevelopment efforts by HUD and other non-profits (e.g. Local Initiative Support Corporation) should consider targeting the radial areas around schools, rather than traditionally defined neighborhoods.  This is particularly important because improved child health manifests healthier adults later on.  While traditional neighborhood boundaries will capture a cross-section of the public, the number of children affected by improvements to school neighborhoods ultimately may pay greater health dividends, and the full range of corollary benefits, as they age.

References

  1. Ball K, Timperio A, Crawford D. Understanding environmental influences on nutrition and physical activity behaviors: where should we look and what should we count? Int J Beh Nutri Phy Act. 2006;3:33.
  2. Dunton GF, Kaplan J, Wolch J, Jerrett M, Reynolds KD. Physical environmental correlates of childhood obesity: a systematic review. Obes Rev. 2009;10:393-402.
  3. Rahman T, Cushing RA, Jackson RJ. Contributions of built environment to childhood obesity. Mt Sinai J Med. 2011;78:49-57.
  4. Burdette HL & Whitaker RC. Neighborhood play-grounds, fast food restaurants, and crime: relationships to overweight in low-income preschool children. Prev Med. 2004;38:57-63.
  5. Kipke M, Iverson E, Moore D, et al. Food and Park Environments: Neighborhood-level Risks for Childhood Obesity in East Lost Angeles. J Adol Health. 2007;40:325-333.
  6. Feng J, Glass TA, Curriero FC, Stewart WF, Schwartz, BS. The built environment and obesity: a systematic review of the epidemiologic evidence. Health and Place. 2010;16:175-190.
  7. Ding D, Sallis J, Kerr J, Lee S, Rosenberg D. Neighboorhood Environment & Physical Activity Among Youth. Am J Prev Med. 2011;41:422-455.
  8. Jones NR, Jones A, van Sluijs EMF, Panter J, Harrison F, Griffin SJ. School environments and physical activity: the development and testing of an audit tool. Health and Place. 2010;16:776–783.
  9. Harrison F, Jones AP. A framework for understanding school based physical environmental influences on childhood obesity. Health Place. 2012;18:639-48.
  10. Austin SB, Melly SJ, Sanchez BN, Patel A, Buka S, Gortmaker SL. Clustering of fast-food restaurants around schools: a novel application of spatial statistics to the study of food environments. Am J Public Health. 2005;95:1575-1581.
  11. Zenk SN, Powell LM. United States secondary schools and food outlets. Health and Place. 2008;14:336-346.
  12. Seliske LM, Pickett W, Boyce WF, Janssen I. Association between the food retail environment surrounding schools and overweight in Canadian youth. Pub Health Nutr. 2009;12:1384–1391.
Authors: 
Jason Wasserman, PhD, Richard Suminski, MPH, PhD, Juan Xi, PhD, Carlene Mayfield, MPH, Alan Glaros, PhD, & Richard Magie, DO
Location by State: 
Study Type: 

Examining Local Land Use Policies that May Affect Active Living among School Students

Date: 
03/11/2014
Description: 

Presentation at the 2014 Active Living Research Annual Conference.

Abstract: 

Background and Purpose
Policy makers and researchers have been examining ways to solve the youth obesity epidemic. One area of interest has focused on the adoption of local policies related to the built environment to promote physical activity.  The Task Force on Community Preventative Services recommends using community and street-scale design and land use policies to promote physical activity.1 Through its zoning and land development laws, a local government can regulate the location of park and recreation facilities, open space, trails, and other facilities that promote physical activity; regulate land use patterns (e.g. mixed use districts); and specify structural requirements such as sidewalks or bike lanes.

Objectives
This presentation will examine the extent to which youth reside in communities with local land development policies that address infrastructure-related features or improvements that would facilitate active living.

Methods
Data were compiled in 2011 from zoning ordinances and related policies obtained from 378 local governments (county, municipal, town/township) surrounding 154 secondary school catchments where a national sample of secondary school students were enrolled as part of the Bridging the Gap Community Obesity Measures Project (BTG-COMP).  Hard and electronic copies of the zoning codes and related policies for each jurisdiction were obtained and were independently reviewed and evaluated by trained policy analysts using the BTG-COMP Built Environment Local Zoning/Policy Audit Tool that seeks to assess the extent to which policies addressed active living. The Tool specifically captures the extent to which policies address any marker that would promote walking and biking (e.g., sidewalks), crosswalks, bike lanes, bike parking, trails/paths, mixed use, bike/pedestrian or street connectivity, active recreation (e.g. playgrounds, athletic fields, recreation facilities, etc.), passive recreation (e.g. open space, parks, etc.), and Complete Streets/Context Sensitive Design (CSD) policies.  The Tool also captures the strength and if applicable the type of use (permitted, conditional, accessory) related to each marker.

Each marker was weighted by the proportion of the youth (0-17) population in a given catchment area that resided in each jurisdiction sampled as a proxy for neighborhood school enrollment zones. Youth population estimates were derived from the American Community Survey 2007-2011 5-year file by multiplying each jurisdiction’s youth population density by the area in square miles that the jurisdiction overlapped its catchment. The proportion of the catchment youth population in a given jurisdiction was the resulting weight value. These measures were then aggregated to the catchment level. Thus, the weighted policy markers reflect the estimated proportion of the catchment’s youth population living in an area that addressed the active living markers of interest (e.g., bike lanes) as well as the strength and type of use variables. The catchment-level variables ranged from 0-1, with 0 meaning that no youth in the catchment were exposed to a given a policy provision and a score of 1 indicating that all of the youth in the catchment were exposed to a given policy. Data for this presentation were based on summary statistics examining the extent to which each of the active living policy markers were addressed and whether they were required and/or permitted uses.

Results
Most youth lived in catchments with zoning and land use policies that addressed passive recreation (89%), walking or biking infrastructure improvements (88%), active recreation (87%), mixed use (75%), or trails/paths/greenways (70%) (see Figure 1).  However, youth were least likely to reside in catchments with zoning and land use policies that addressed bike lanes (30%) or an officially adopted Complete Streets or CSD policy (12%). Required provisions or permitted use-type provisions also varied as youth were more likely to reside in catchments with policies containing provisions that required or allowed walking/biking-related infrastructure improvements (78%), passive recreation (85%), or active recreation (83%), and were less likely to require or allow policies related to crosswalks (18%), bike lanes (8%), or Complete Streets or CSD (7%).

Conclusions
Although many local governments include active-living oriented provisions in their land use policies, data from this study suggests that some provisions are more prevalent than others.  Policies were more likely to address items related to walking and biking, mixed use, and active and passive recreation than they were to address bike lanes or to contain Complete Streets or CSD provisions.

Implications for Practice and Policy
Local governments should review their existing land use policies and modify them to address infrastructure improvements or regulate land use patterns that could be to facilitate active living. Local governments should specifically require structural improvements, such as sidewalks or open space, to ensure active living opportunities.

References

Heath GW, Brownson RC, Kruger J, et al. The effectiveness of urban design and land use and transportation policies and practices to increase physical activity: a systematic review. J Phys Act Health. 2006;3(Suppl 1):S55-S76.

Support / Funding Source
Robert Wood Johnson Foundation Bridging the Gap Research Program.

Authors: 
Emily Thrun, MUPP, Jamie Chriqui, PhD, MHS, Christopher Quinn, MS, Sandy Slater, PhD, Dianne Barker, MHS, & Frank Chaloupka, PhD
Location by State: 
Study Type: 

The Impact of a Signalized Crosswalk on Crossing Behaviors in a Low-Income Minority Neighborhood

Date: 
03/11/2014
Description: 

Presentation at the 2014 Active Living Research Annual Conference.

Abstract: 

Background and Purpose
Communities with predominantly low-income and minority populations are effected by the highest levels of sedentary behavior and obesity (Day, 2006). These underserved communities often have limited access to parks and active transportation resultant of high-speed, high-volume streets and an outdated built environment. While studies suggest that sidewalks, crosswalks, and traffic calming measures can increase pedestrian safety (Pucher & Dijkstra, 2003) few studies have evaluated pedestrian crossing behaviors as a result of infrastructure changes. In 2012-2013, the completion of a signalized crosswalk and landscaped median linking low-income housing with a public park provided a natural experiment to examine the effect of an infrastructure project upon active living behaviors.

Objectives
The purpose of this study was to examine the effect of changes to the built environment to determine whether street crossing infrastructure modifications change pedestrian crossing behaviors or traffic patterns in a low-income and predominately racial/ethnic minority community.

Methods
Data collection occurred at one Intervention site (Providence Road) and one Control site (College Avenue) in Columbia, MO. We selected the Control site 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, legality of the crossing (e.g., at intersections/crosswalks or not), as well as race/ethnicity, gender, and age within 5-6 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 seven days (Monday-Sunday) over the same two-week period in June 2012 (pre-intervention) and June 2013 (post-intervention), crossing behaviors were recorded for three hours each day (7:30am, 12:30pm, and 3:30pm) while traffic data were collected continuously for 168 hours during the first week.

Descriptive statistics were calculated for all variables. Independent samples t-tests assessed overall changes in pedestrian crossings and traffic volume at each site from 2012 to 2013. Changes in legal/illegal crossings and traffic speed (above the speed limit/below the speed limit) at each site from 2012 to 2013 were analyzed using Pearson’s Chi Square.

Results
Total pedestrian crossings at the Intervention site (Providence Road) increased from 1,464 in 2012 to 1,658 in 2013 (p<0.001). Between 2012 and 2013, the number of legal crossings at the Intervention site increased from 553 (38%) to 795 (48%) (p<0.001). In both years, the majority of observations were pedestrians (1,099 [75%], 2012; 1,316 [79%], 2013) followed by bicyclists (332 [23%], 2012; 310 [19%], 2013). Amongst children and teens, legal crossings rose from 45(25%) to 94(61%) and from 90(23%) to 169(41%), respectively between 2012 and 2013 (both: p<0.001). In addition, total traffic volume at the Intervention site fell slightly from 148,857 vehicles in 2012 to 148,508 in 2013 (p=0.01). Motor vehicles that were traveling above the speed limit of 35 mph decreased from 67,922(46%) in 2012 to 51,339(35%) in 2013 (p<0.001).

There was no change in the number of total pedestrian crossings at the Control site (College Avenue) from 2012 (4,385) to 2013 (4,485) (p=0.90). Legal crossings increased at the Control site, but only by 2% (2,341 [53%] in 2012 to 2,507 [55%] in 2013) (p=0.01).  Similar to the Intervention site, pedestrians were most commonly observed (3712 [85%], 2012; 3890 [87%], 2013), followed by bicyclists (640[15%], 2012; 549[12%], 2013). Amongst children, the small number of legal crossings did not significantly change (10 [77%], 2012; 18 [95%], 2013) (p=0.135) but for teens changed from 497(39%) to 162(55%) (p<0.001), respectively between 2012 and 2013. As with the Intervention site, total traffic volume at the Control site fell from 132,428 in 2012 to 124,635 in 2013 (p<0.001). However, motor vehicles that were traveling above the speed limit of 35 mph increased from 64,310 (49%) in 2012 to 73,552 (59%) in 2013 (p<0.001).

Conclusions
The replacement of an unsafe pedestrian bridge with an at-grade, signalized pedestrian crosswalk and landscaped median significantly impacted both pedestrian crossing behaviors and vehicular traffic behaviors. Specifically, the installation of the pedestrian crosswalk yielded reduced proportions of illegal crossings (especially among children), and reduced the percentage of vehicles speeding on the highway through the neighborhood at the Intervention site while the percentage of vehicles speeding at the Control site increased. This study suggests that street crossing infrastructure changes do change behavior, which will help inform future street crossing interventions and may be used to guide policies promoting physical activity in similar communities where high-speed arterials are barriers to parks and active living.

Implications for Practice and Policy
By demonstrating increased pedestrian safety and traffic calming, this study adds support to the feasibility of advocacy efforts to reverse transportation practices that favor automobiles at the expense of pedestrian accessibility. 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. Day, K. (2006). Active living and social justice: Planning for physical activity in low-income, Black, and Latino communities. Journal of the American Planning Association, 72(1), 88-99.
  2. Pucher, J., & Dijkstra, L. (2003). Promoting safe walking and cycling to improve public health; Lessons from the Netherlands and Germany. American journal of public health, 93(9), 1509-1516.

 

Support / Funding Source
University of Missouri Research Board Grant.

Authors: 
Courtney Schultz, BS, Sonja Wilhelm Stanis, PhD, Ian Thomas, PhD, & Stephen Sayers, PhD
Location by State: 

Correlates of Walking for Transportation and Public Transportation Use among St. Louis Adults

Date: 
03/11/2014
Description: 

Presentation at the 2014 Active Living Research Annual Conference.

Abstract: 

Background and Purpose
Walking for transportation, which can include walking that takes place at the beginning or end of a trip taken by public transportation, can provide individuals with the opportunity to meet recommended levels of physical activity. Previous studies have demonstrated that individuals who walk to and from public transportation stops engage in more daily physical activity than those who do not.[1-5] More evidence is needed, however, to better understand the relationship between walking for transportation and public transportation use and more specifically, the mechanisms through which this relationship occurs.[5] A growing body of evidence has also suggested that perceptions of built environment characteristics can influence walking for transportation.[6-8] Despite this evidence, little is known about how these perceived environmental factors influence public transportation use.

Objectives
The aims of this study were to: (1) further assess the relationship between individual factors, public transportation use, and walking for transportation, specifically in a low-income community of color; and (2) examine the association among individual and perceived environmental factors and public transportation use.

Methods
This cross-sectional study was conducted in 2012. We used questionnaire data from 772 adults living in St. Louis, Missouri. We used the International Physical Activity Questionnaire long form to assess walking for transportation and public transportation use. The abbreviated Neighborhood Environment Walkability Scale was used to examine perceptions of the environment. Two different models were tested using multinomial logistic regression with walking for transportation and public transportation use as the outcome variables. Model 1 examined the association between individual factors and public transportation use with walking for transportation. Model 2 examined the association between individual and perceived environmental factors with public transportation use.

Results
Most participants were women and adults less than 50 years old. The majority of the sample was employed outside of the home and 27% had an annual income less than $10,000.

Multinomial logistic regression analyses revealed that the odds of walking for transportation for 1-149 minutes/previous week and =150 minutes/previous week (OR=2.11, CI=1.31-3.40 and OR=2.08, CI=1.27-3.42, respectively) were higher for individuals who reported using public transportation 1-4 days in the previous week in comparison to individuals who did not use public transportation. Similarly, the use of public transportation for five or more days in the previous week was positively related to walking for transportation. Compared to individuals who did not use public transportation, individuals who used public transportation for five or more days in the previous week were 3.47 times more likely to walk for transportation for 1-149 minutes/previous week and 8.61 times more likely to walk for transportation for more than 150 minutes/previous week (CI=1.47-8.19 and CI=3.87-19.20, respectively).

Model 2 revealed that the odds of using public transportation more than once a week (1-4 days/previous week) was greater among individuals between 50-59 years old (OR=1.98, CI=1.06-3.70) in comparison to individuals between 18-29 years old. However, adults over 60 years old were less likely to use public transportation five or more days in the previous week (OR=.34, CI=.14-.86) compared to individuals between 18-29 years old. Employed individuals were less likely than unemployed individuals to use public transportation more than once a week (1-4 days/previous week: OR=.56, CI=.35-.92).

Participants who reported high traffic speed and high crime in their neighborhood were less likely to use public transportation. More specifically, individuals who reported that traffic exceeded the posted speed limits in their neighborhood were less likely to use public transportation for 1-4 days in the previous week (OR=.54, CI=.36-.81) compared to those who did not report high traffic speed in their neighborhood. Similarly, individuals who perceived high crime in their neighborhood had lower odds of using public transportation for more than five days in the previous week (OR=.50, CI=.28-.87) compared to those who did not report high crime.

Conclusions
Using a diverse sample of adults where many participants were unemployed and used public transportation as their primary mode of transport, we found that individuals that use public transportation more frequently are more likely to meet physical activity recommendations by walking for transportation. Our study results are consistent with earlier research demonstrating that regular public transportation use is associated with increased physical activity and that walking for transportation appears to occur in combination with public transportation.[1-5] Of the perceived environmental factors assessed, our study results indicated that high traffic speed and high neighborhood crime were negatively associated with public transportation use. To our knowledge, no studies to date have investigated the relationship between perceived built environment attributes and public transportation use.

Implications for Practice and Policy
Programs, policies, and infrastructure changes to improve the perception and actual safety from traffic and crime may be an important investment to increase public transportation use in similar urban communities, and thereby increase levels of walking.

References

  1. Besser LM, Dannenberg AL, 2005. Walking to public transit: steps to help meet physical activity recommendations. Am J Prev Med 29: 273-280.
  2. Frank LD, Greenwald MJ, Winkelman S, Chapman J, Kavage S, 2010. Carbonless footprints: promoting health and climate stabilization through active transportation. Prev Med 50: S99-105.
  3. Freeland AL, Banerjee SN, Dannenberg AL, Wendel AM, 2013. Walking associated with public transit: moving toward increased physical activity in the United States. Am J Public Health 103: 536-542.
  4. Lachapelle U, Frank L, Saelens BE, Sallis JF, Conway TL, 2011. Commuting by public transit and physical activity: where you live, where you work, and how you get there. J Phys Act Health 8: S72-82.
  5. Wener RE, Evans GW, 2007. A morning stroll: levels of physical activity in car and mass transit commuting. Environ Behav 39: 62-74.
  6. Bauman AE, Reis RS, Sallis JF, Wells JC, Loos RJF, Martin BW, 2012. Correlates of physical activity: why are some people physically active and others not? Lancet 380: 258-271.
  7. Duncan MJ, Spence JC, Mummery WK, 2005. Perceived environment and physical activity: a meta-analysis of selected environmental characteristics. Int J Behav Nutr Phys Act 2: 11.
  8. Van Dyck D, Cerin E, Conway TL, De Bourdeaudhuij I, Owen N, Kerr J, Cadon G, Frank LD, Saelens BE, Sallis JF, 2012. Perceived neighborhood environmental attributes associated with adults’ transport-related walking and cycling: Findings from the USA, Australia, and Belgium. Int J Behav Nutr Phys Act 9: 70.

 

Support / Funding Source
This study was supported by the International Center for Advanced Renewable Energy and Sustainability at Washington University in St. Louis, Missouri (1660-94758A) and the John Hopkins Global Center on Childhood Obesity (2001656847).

Authors: 
Marissa Zwald, MPH, Aaron Hipp, PhD, Marui Corseuil, MPH, & Elizabeth Dodson, PhD, MPH
Location by State: 
Study Type: 

The Surveillance and Management Toolkit Positions Parks and Recreation as a Public Health Provider

Date: 
03/11/2014
Description: 

Presentation at the 2014 Active Living Research Annual Conference.

Abstract: 

Background and Purpose
The Healthy Communities Research Group (HCRG) was created by Dr. David M. Compton while at Indiana University, to help position parks, recreation, and related community agencies as public health providers.  The purpose is to develop and test the Surveillance and Management Toolkit - a step by step systematic assessment that allows communities to determine the key factors, indicators, and  actions necessary to help reduce obesity and increase physical activity. The testing is currently focused on ages 10 - 14 in the community but the Toolkit can be used to identify factors for all ages. Funding currently comes from GP RED, a 501(c)(3) public charity that provides research, education, and development for health, recreation, and land management agencies, with community funding support for hard costs, along with alliances and support from East Carolina University, and direction from the HCRG Director, Teresa Penbrooke, currently a PhD student at North Carolina State University.

Description
Since 2009, the GP RED Healthy Communities Research Group (HCRG) has been working to develop and test the Healthy Communities Surveillance and Management Toolkit. The project targets the community aspects that influence obesity and active living, specifically targeting ages 10 to 14, and helps to position parks and recreation agencies and partners as key public health providers. The initial “alpha project” with Indiana University in Bloomington, Indiana in 2010 was successful, and the methods are now being integrated into a training process and toolkit and applied to additional “beta” site communities for further refinement, testing, and implementation in the future. South Bend, IN, is just completing Year Three of the project, and Liberty, MO is finishing Year One.  Other communities are in funding stages. This presentation will cover methodology, outcomes, and evaluation of the Toolkit and its applications.

The Surveillance and Management Toolkit helps parks, recreation, and related departments and agencies assess, analyze, document, and evaluate five systematic elements related to the re-positioning of parks and recreation as a primary preventative community public health provider:

  1. Convening Community Stakeholders and Champions – Residents? Partners? Providers?
  2. Creating a Warrant for Agency Action – Why? Who? What is the Impact?
  3. Policies, Laws, and Procedures – What is influencing obesity and/or active living?
  4. Fiscal Resources and Distribution – What funds? How should they be allocated?
  5. Inventory of Assets and Affordances – Programs? Parks? Facilities? Food?

 

From an inventory and quantitative and qualitative analysis of these elements, the project moves to creation of a systems portfolio, strategic concepts for improvement, and future modeling for the purposes of articulation, prioritization, management, and surveillance of outcomes over time. The process utilizes a specially designed Multi-Attribute Utilities Theory (MAUT) process to quantitatively determine evidence-based indicators, and as a tool for discerning consensus on healthy contributors in any community. Using current best practices for management, along with Composite-Values Method (CVM) for Level of Service Analysis, the process compiles a complete inventory of GIS-based relevant assets and affordances. This information was then integrated into the five-element analysis, including policy, fiscal, environmental, and Stella® implementation modeling, with evaluation and outcomes determined for each year.

Lessons Learned
The Surveillance and Management Toolkit has been created over a three-year development and testing phase. The Toolkit and process has been modified through implementation of three different communities, and still undergoing additional testing. Adjustments have been made for all inventory templates to ensure   practitioner and agency management ease of use, along with modifications to the MAUT process and Stella Modeling process to ensure accuracy and relevant results.

Conclusions and Implications
A step by step standardized but flexible systematic assessment process has been needed by public agencies and their community partners to help ascertain accurate factors and indicators to help reduce obesity and increase active living, and to position parks and recreation as key public health providers. This Surveillance and Management Toolkit has been successfully utilized and is being tested, and is now ready for peer review and refinement for broad community implementation to address these issues.

Next Steps
The intention is to identify up to 10 Beta site communities of various demographic profiles around the U.S., continue testing and validation, and then to publish the Surveillance and Management Toolkit for broad-based practitioner and community implementation and application.

References

  1. Centers for Disease Control and Prevention (2010). Obesity: Halting the Epidemic by making health easier. Retrieved at http://www.cdc.gov/chronicdisease.
  2. Compton, D.M., Muehlenbein, M., Penbrooke, T.L. (2010) Healthy Communities: Repositioning public park and recreation agencies as catalysts for healthy people. Unpublished workbook, Indiana University.
  3. Crompton, J.L.(2010). Measuring the Economic Impact of park and recreation services. National Recreation and Park Association Research Series, Arlington, VA
  4. International City/County Management Association (2005). Active Living and Social Equity – Creating Healthy Communities for all Residents, Washington DC.

 

Support / Funding Source
Support provided by GP RED, East Carolina University, Indiana University, and North Carolina State University.

Authors: 
Teresa Penbrooke, MAOM
Location by State: 

Developing the Active Living Plan for a Healthier San Antonio: Lessons Learned

Date: 
03/11/2014
Description: 

Presentation at the 2014 Active Living Research Annual Conference.

Abstract: 

Background and Purpose
Despite the evidence that regular physical activity (PA) is essential for good health, many Americans do not meet the PA guidelines. In San Antonio, Bexar County, Texas, only 1 in 4 adults and 1 in 3 youth meet the PA guidelines. Strategies to facilitate and support physical activity opportunities for people of all ages must be identified and implemented at the local level. The local health department (LHD) of San Antonio initiated a multi-sector collaborative effort to increase PA among residents by establishing the Active Living Council of San Antonio (ALCSA) to create a 3-5-year master plan and policy recommendations to encourage active living in the community. The current study describes the 2-year process of forming a multi-sector community coalition and writing a plan to promote active living in San Antonio.

Description
An ALCSA Steering Committee (SC) composed of LHD staff and community organization representatives convened to organize and launch the ALCSA. After learning about active living and PA-promoting initiatives at the national, state, and local levels, the SC set preliminary ALCSA goals: 1) Provide a forum to address active living issues; 2) Promote coordination among various sectors that impact active living; 3) Foster local PA and active living projects; 4) Promote improved access to places and programs for PA; and 5) Promote policies related to increasing PA and active living. The SC determined ALCSA’s membership will represent multiple sectors and activities should reflect current evidence and national guidelines. The SC created ALCSA membership categories to mirror the 8 sectors of the National Physical Activity Plan (NPAP) and added 2 general membership categories to ensure broad community representation. The 20-member council includes 2 volunteer members for each membership category. Following a coordinated outreach effort to recruit applicants, the SC selected ALCSA members and outlined initial council activities, concluding SC responsibilities. ALCSA wrote vision and mission statements, adopted a governance framework, elected officers, and devoted much time to internal capacity-building about PA and health, relying on evidence-based resources to guide discussions about PA-promoting strategies. Drawing on diverse expertise among members and participating in a variety of educational opportunities, members became well-informed active living advocates. ALCSA conducted outreach to other coalitions and organizations to identify opportunities to align and support local initiatives. Seeking broad community input about local needs and priorities related to active living, members engaged their sector constituents in a variety of ways, such as presentations to professional networks and distribution of a sector-specific online survey. Master plan development was a multi-step, collaborative process. Given the NPAP’s PA focus, multi-sector approach, and use of evidence-based strategies to advance active living, the NPAP emerged as the key resource for guiding development of ALCSA’s master plan. ALCSA adopted the NPAP’s overall structure and selected strategies which reflected San Antonio’s needs and priorities and could make an impact in a 3-5-year period. Members embarked upon a plan-writing process to articulate priorities, guide allocation of resources, establish measures of success, and generate a sense of urgency about the importance of PA to the overall health of the community. A sub-committee (writing team) led the plan-writing effort and engaged all members throughout the process. The plan includes overarching and sector-specific strategies. The writing team outlined overarching strategies, whereas sector partners took the lead on sector strategies. The writing team provided drafts for members to review and incorporated feedback received, an iterative process which took place over 7 months. LHD staff and other local leaders as well as national experts reviewed final drafts over the subsequent 5 months. This process produced the Active Living Plan for a Healthier San Antonio, a plan that reflects national recommendations and fits San Antonio’s local context.

Lessons Learned
Participatory planning required a large commitment of volunteer time in addition to LHD staff time. Though information sharing about development and implementation of local PA initiatives would have been helpful in guiding ALCSA’s efforts, we did not find examples in the literature detailing experiences of other local multi-sector active living coalitions. Despite broad conceptual support for collaborative multi-sector community initiatives to promote active living, practices supportive of equitable partnerships are challenging and demand shared leadership, transparency, clearly-defined expectations, and extensive communication.

Conclusions and Implications
ALCSA adapted the U.S. National Physical Activity Plan (NPAP) to a local context. The Active Living Plan for a Healthier San Antonio is a 3-5-year roadmap for transforming San Antonio into a community that promotes and supports active living among its residents.

Next Steps
The plan received endorsements of San Antonio’s Mayor Julian Castro and the local Mayor’s Fitness Council, which recently incorporated ALCSA as a standing committee to advance implementation of the plan. Education and outreach efforts with decision makers and sector constituents about the plan’s strategies to promote active living are underway and an evaluation plan is being developed.

References

  1. U.S. Department of Health and Human Services. 2008 Physical Activity Guidelines for Americans. (2008). Retrieved September 4, 2013.
  2. U.S. National Physical Activity Plan. (2010). Retrieved September 4, 2013.
  3. Yan FA. San Antonio Bexar County Physical Activity and Inactivity Report Year 2010. (2011). Retrieved September 4, 2013.

 

Support / Funding Source
ALCSA is one of the initiatives of San Antonio’s Communities Putting Prevention to Work grant supported by funding from the Centers for Disease Control and Prevention (#1U58DP002453-01).

Authors: 
Laura Esparza, MS, Katherine Velasquez, RN, PhD, & Annette Zaharoff, MD
Location by State: 
Population: 
Study Type: 

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