Background: Diverse environmental factors are associated with physical activity (PA) and healthy eating (HE) among youth. However, no study has created a comprehensive obesogenic environment index for children that can be applied at a large geographic scale. The purpose of this study was to describe the development of a childhood obesogenic environment index (COEI) at the county level across the United States. Methods: A comprehensive search of review articles (n = 20) and input from experts (n = 12) were used to identify community-level variables associated with youth PA, HE, or overweight/obesity for potential inclusion in the index. Based on strength of associations in the literature, expert ratings, expertise of team members, and data source availability, 10 key variables were identified – six related to HE (# per 1000 residents for grocery/superstores, farmers markets, fast food restaurants, full-service restaurants, and convenience stores; as well as percentage of births at baby (breastfeeding)-friendly facilities) and four related to PA (percentage of population living close to exercise opportunities, percentage of population < 1 mile from a school, a composite walkability index, and number of violent crimes per 1000 residents). Data for each variable for all counties in the U.S. (n = 3142) were collected from publicly available sources. For each variable, all counties were ranked and assigned percentiles ranging from 0 to 100. Positive environmental variables (e.g., grocery stores, exercise opportunities) were reverse scored such that higher values for all variables indicated a more obesogenic environment. Finally, for each county, a total obesogenic environment index score was generated by calculating the average percentile for all 10 variables. Results: The average COEI percentile ranged from 24.5–81.0 (M = 50.02,s.d. = 9.01) across US counties and was depicted spatially on a choropleth map. Obesogenic counties were more prevalent (F = 130.43,p < .0001) in the South region of the U.S. (M = 53.0,s.d. = 8.3) compared to the Northeast (M = 43.2,s.d. = 6.9), Midwest (M = 48.1,s.d. = 8.5), and West (M = 48.4,s.d. = 9.8). When examined by rurality, there were also significant differences (F = 175.86,p < .0001) between metropolitan (M = 46.5,s.d. = 8.4), micropolitan (M = 50.3,s.d. = 8.1), and rural counties (M = 52.9,s.d. = 8.8) across the U.S. Conclusion: The COEI can be applied to benchmark obesogenic environments and identify geographic disparities and intervention targets. Future research can examine associations with obesity and other health outcomes.

This study examined spatial patterns of obesogenic environments for US counties. We mapped the geographic dispersion of food and physical activity (PA) environments, assessed spatial clustering, and identified food and PA environment differences across U.S. regions and rurality categories. Substantial low food score clusters were located in the South and high score clusters in the Midwest and West. Low PA score clusters were located in the South and high score clusters in the Northeast and Midwest (p < .0001). For region, the South had significantly lower food and PA environment scores. For rurality, rural counties had significantly higher food environment scores and metropolitan counties had significantly higher PA environment scores (p < .0001). This study highlights geographic clustering and disparities in food and PA access nationwide. State and region-wide environmental inequalities may be targeted using structural interventions and policy initiatives to improve food and PA access.

Background: Research is needed that identifies environmental resource disparities and applies multiple rural definitions. Therefore, this study aims to examine urban‐rural differences in food and physical activity (PA) environment resource availability by applying several commonly used rural definitions. We also examine differences in resource availability within urban‐rural categories that are typically aggregated.Methods: Six food environment variables (access to grocery/superstores, farmers' markets, fast food, full‐service restaurants, convenience stores, and breastfeeding‐friendly facilities) and 4 PA environment variables (access to exercise opportunities and schools, walkability, and violent crimes) were included in the childhood obesogenic environment index (COEI). Total COEI, PA environment, and food environment index scores were generated by calculating the average percentile for related variables. US Department of Agriculture Urban Influence Codes, Office of Management and Budget codes, Rural‐Urban Continuum Codes, Census Bureau Population Estimates for percent rural, and Rural Urban Commuting Area Codes were used. One‐way ANOVA was used to detect urban‐rural differences.Results: The greatest urban‐rural disparities in COEI (F=310.2, P<.0001) and PA environment (F=562.5, P<.0001) were seen using RUCC codes. For food environments, the greatest urban‐rural disparities were seen using Census Bureau percent rural categories (food: F=24.9, P<.0001). Comparing remote rural categories, differences were seen for food environments (F=3.1, P=.0270) and PA environments (F=10.2, P<.0001). Comparing metro‐adjacent rural categories, differences were seen for PA environment (F=4.7, P=.0090).Conclusion: Findings inform future research on urban and rural environments by outlining major differences between urban‐rural classifications in identifying disparities in access to health‐promoting resources.

Park use has numerous health benefits. However, little research has investigated how the combination of park facilities, amenities, and conditions are related to park visitation. This study examined the association between a novel composite park quality metric and the use of specific parks, including variations by demographics. Data were collected in 128 census block groups across four diverse cities in the USA. Adults (n = 262) used an online, map-based survey to indicate which parks within one half-mile they had used within the past 30 days. All parks (n = 263) were audited using the Community Park Audit Tool, and a composite quality metric was calculated by standardizing and averaging six key components: access, facilities, amenities, aesthetic features, quality concerns, and neighborhood concerns. A total of 2429 participant-park pairs were analyzed. The average park quality score was 40.4/100 (s.d. = 30.2). For the full sample, a greater park quality score was significantly related to park use (OR = 1.02, 95% CI = 1.01–1.03) such that for each one unit increase in a park’s quality score, there was a 2% increase in the likelihood of the park being used. There was also a significant interaction by gender, with park quality associated with park use among females (OR = 1.03, 95% CI = 1.02–1.05) but not males (OR = 1.00, 95% CI = 0.99–1.02). Enhancing overall park quality may increase the likelihood of a park being used. Future research can explore the utility of this comprehensive quality metric for predicting other health behaviors and outcomes and how interventions to enhance park quality augment park use and health over time.

Parks are critical components of healthy communities. This study explored neighborhood socioeconomic and racial/ethnic inequalities in park access and quality in a large U.S. southeastern metropolitan region. A total of 241 block groups were examined, including 77 parks. For each block group, we obtained multiple sociodemographic indicators, including unemployment rate, education level, renter-occupied housing, poverty rate, and racial/ethnic minority composition. All parks were mapped using geographical information systems and audited via the Community Park Audit Tool to evaluate their features and quality. We analyzed seven diverse elements of park quality (transportation access, facility availability, facility quality, amenity availability, park aesthetics, park quality concerns, and neighborhood quality concerns), as well as an overall park quality score by calculating the mean for all parks within each block group. The mean percent of residents below 125% of the poverty level and the percentage of renter-occupied housing units were significantly higher among block groups with any parks in comparison to block groups with no parks. In addition, there were significant positive associations between park transportation access scores and both the percentage of residents with less than high school education and the percent identifying as non-Hispanic white. Moreover, there was a significant negative association between park amenity availability and the block group’s unemployed population. Further, a significant negative association between park aesthetics and the population with a lower than high school education percentage was observed. Revealed differences in park availability, park acreage, and park quality dimensions emphasized the need for targeted policy, programmatic, and infrastructure interventions to improve park access and quality and address health disparities.

The purpose of this scoping review was to identify objectively-measured environmental factors that impact physical activity (PA), healthy eating (HE), and/or breastfeeding practices among rural communities in the United States. A secondary aim was to assess measurement approaches for assessing rural environments and resource access. Searches, informed by a scientific librarian, occurred from February to July 2020. Grey literature was identified using Google, Google Scholar, Google government pages, public health, federal nutrition assistance program, Cooperative Extension Services, and related webpages. Medline, PubMed, Web of Science, and Agricola academic databases were selected to identify peer-reviewed research. ProQuest Global was used to identify dissertation/thesis research. Inclusion criteria included: (1) focus on PA, HE, and/or breastfeeding; (2) environmental approaches assessed using objective measures; (3) specific to U.S. rural populations/contexts; and (4) English language. PA environment results (n=49) focused on initiatives in parks and recreational settings, streets or sidewalks, schools, trails and greenways, workplaces, and churches. HE environment results (n=84) focused on initiatives in retail environments, schools/childcare settings, food assistance programs, local food producers, and hospital or healthcare settings. Breastfeeding environment results (n=11) focused on healthcare settings, workplace settings, and food retail settings. To measure PA, HE, and breastfeeding environments, audits, scans, and administrative data were mostly used. Results from this scoping review were used to create recommendations for environmental changes that may improve PA, HE, and breastfeeding in under-resourced, rural communities. Researchers and practitioners can also use findings from this study to understand best practices for objectively measuring environmental resources in rural contexts.