Understanding spatial variation of physical inactivity across the continental United States

Lee, Kyung Hee; Dvorak, Robert G; Schuett, Michael A.; Riper, Carena J. van

doi:10.1016/j.landurbplan.2017.09.020

Cited by 19 publications

(20 citation statements)

References 59 publications

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“…This result corresponds to that of previous studies, in that people with higher SES have a lower obesity rate [18]. SES showed a statistically significant relationship with walkability and urban leisure amenities, which also aligns with previous findings, maintaining the presence of spatial disparity in the provision of a healthier living environment [21][22][23]79,80]. The SES is negatively associated with walkability (coeff.= −0.947), meaning that, on average the people of higher SES live in less walkable neighborhoods.…”

Section: Analytical Resultssupporting

confidence: 91%

“…This observation might imply a limitation of the variable specification. The provision of open space does not mean people actually use such amenities [22]. The simple presence of amenities may not lead residents to "visit and use" [77].…”

Section: Analytical Resultsmentioning

confidence: 99%

“…This difference is magnified by their differing living environments [18,19]. First, low-income communities have relatively few public amenities-such as playgrounds, sidewalks, and recreational facilities-that potentially encourage physical activities, in comparison to places where tax money is abundant [20][21][22][23]. Second, low-income neighborhoods have lower accessibility to healthy food resources and higher accessibility to fast food, to which the residents routinely resort, resulting in poor dietary habits [24,25].…”

Section: Of 20mentioning

confidence: 99%

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Do the Walkability and Urban Leisure Amenities of Neighborhoods Affect the Body Mass Index of Individuals? Based on a Case Study in Seoul, South Korea

Choi

Yoon

2020

IJERPH

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This study investigates the impact of neighborhood-built environments on obesity in interrelationship with socioeconomic status (SES)—controlling for dietary patterns and physical activities of residents—using structural equation modeling (SEM). A total of 577 samples who are between 19 and 64 years old and reside in Seoul are extracted from Korea National Health and Nutrition Examination Survey (KNHNES), 2015. Neighborhood environments are represented as the two latent constructs—walkability and leisure amenities—composited with indicators such as density of intersections, density of mixed-use area, and the area of open spaces and are aggregated by jurisdictional unit in Seoul. We found that greater walkability in a neighborhood explained a lower body mass index (BMI) among residents, whereas more urban leisure amenities in a neighborhood explained a higher BMI. The finding suggests that a walking-friendly environment is more effective than active recreational amenities in inducing people to engage in daily physical activities to the level that reduces obesity rate. SES exerted a negative impact on BMI of a greater magnitude than the impact of either of the environmental living conditions, reinforcing the importance of general wealth and education level in leading to a healthy lifestyle. Our research contributes to growing evidence of a relationship between obesity and the built environment in the context of Asian countries where the prevalence of obesity is becoming a serious issue and requires immediate attention.

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Section: Analytical Resultssupporting

confidence: 91%

Section: Analytical Resultsmentioning

confidence: 99%

Section: Of 20mentioning

confidence: 99%

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Do the Walkability and Urban Leisure Amenities of Neighborhoods Affect the Body Mass Index of Individuals? Based on a Case Study in Seoul, South Korea

Choi

Yoon

2020

IJERPH

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“…Besides food environments, living in closer proximity to sidewalks, parks, and gyms are more likely to have access to exercise and physical activity 68 – 70 . Physical inactivity is not solely associated with community characteristics 71 . Physical inactivity has been associated with T2D prevalence, independent of obesity 72 and related to high health care expenditures 73 .…”

Section: Discussionmentioning

confidence: 99%

Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA

Quiñones

Goyal

Ahmed

2021

Sci Rep

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Type 2 diabetes mellitus (T2D) prevalence in the United States varies substantially across spatial and temporal scales, attributable to variations of socioeconomic and lifestyle risk factors. Understanding these variations in risk factors contributions to T2D would be of great benefit to intervention and treatment approaches to reduce or prevent T2D. Geographically-weighted random forest (GW-RF), a tree-based non-parametric machine learning model, may help explore and visualize the relationships between T2D and risk factors at the county-level. GW-RF outputs are compared to global (RF and OLS) and local (GW-OLS) models between the years of 2013–2017 using low education, poverty, obesity, physical inactivity, access to exercise, and food environment as inputs. Our results indicate that a non-parametric GW-RF model shows a high potential for explaining spatial heterogeneity of, and predicting, T2D prevalence over traditional local and global models when inputting six major risk factors. Some of these predictions, however, are marginal. These findings of spatial heterogeneity using GW-RF demonstrate the need to consider local factors in prevention approaches. Spatial analysis of T2D and associated risk factor prevalence offers useful information for targeting the geographic area for prevention and disease interventions.

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“…The purpose of global Moran’s I analysis was to test the level of spatial autocorrelation in the whole study area associated with poor mental health days. The Moran’s I value ranged from −1 (perfect dispersion) to 1 (perfect clustering) and a zero value indicated a perfect randomness [ 49 , 50 ]. Additionally, local Moran’s I was completed to visualize whether poor mental health days were spatially dependent across the 3109 counties.…”

Section: Methodsmentioning

confidence: 99%

Mental Health and Recreation Opportunities

Lee

2020

IJERPH

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The environment has direct and indirect effects on mental health. Previous studies acknowledge that the poor design of communities and social environments leads to increased psychological distress, but methodological issues make it difficult to draw clear conclusions. Recent public health, leisure and recreation studies have tried to determine the relationship between recreation opportunities and mental health. However, previous studies have heavily focused on individual contexts rather than national or regional levels; this is a major limitation. It is difficult to reflect the characteristics of community environments effectively with such limited studies, because social environments and infrastructure should be analyzed using a spatial perspective that goes beyond an individual’s behavioral patterns. Other limitations include lack of socioeconomic context and appropriate data to represent the characteristics of a local community and its environment. To date, very few studies have tested the spatial relationships between mental health and recreation opportunities on a national level, while controlling for a variety of competing explanations (e.g., the social determinants of mental health). To address these gaps, this study used multi-level spatial data combined with various sources to: (1) identify variables that contribute to spatial disparities of mental health; (2) examine how selected variables influence spatial mental health disparities using a generalized linear model (GLM); (3) specify the spatial variation of the relationships between recreation opportunities and mental health in the continental U.S. using geographically weighted regression (GWR). The findings suggest that multiple factors associated with poor mental health days, particularly walkable access to local parks, showed the strongest explanatory power in both the GLM and GWR models. In addition, negative relationships were found with educational attainment, racial/ethnic dynamics, and lower levels of urbanization, while positive relationships were found with poverty rate and unemployment in the GLM. Finally, the GWR model detected differences in the strength and direction of associations for 3109 counties. These results may address the gaps in previous studies that focused on individual-level scales and did not include a spatial context.

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Understanding spatial variation of physical inactivity across the continental United States

Cited by 19 publications

References 59 publications

Do the Walkability and Urban Leisure Amenities of Neighborhoods Affect the Body Mass Index of Individuals? Based on a Case Study in Seoul, South Korea

Do the Walkability and Urban Leisure Amenities of Neighborhoods Affect the Body Mass Index of Individuals? Based on a Case Study in Seoul, South Korea

Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA

Mental Health and Recreation Opportunities

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