Obesity relationships with community design, physical activity, and time spent in cars.
ABSTRACT Obesity is a major health problem in the United States and around the world. To date, relationships between obesity and aspects of the built environment have not been evaluated empirically at the individual level.
To evaluate the relationship between the built environment around each participant's place of residence and self-reported travel patterns (walking and time in a car), body mass index (BMI), and obesity for specific gender and ethnicity classifications.
Body Mass Index, minutes spent in a car, kilometers walked, age, income, educational attainment, and gender were derived through a travel survey of 10,878 participants in the Atlanta, Georgia region. Objective measures of land use mix, net residential density, and street connectivity were developed within a 1-kilometer network distance of each participant's place of residence. A cross-sectional design was used to associate urban form measures with obesity, BMI, and transportation-related activity when adjusting for sociodemographic covariates. Discrete analyses were conducted across gender and ethnicity. The data were collected between 2000 and 2002 and analysis was conducted in 2004.
Land-use mix had the strongest association with obesity (BMI >/= 30 kg/m(2)), with each quartile increase being associated with a 12.2% reduction in the likelihood of obesity across gender and ethnicity. Each additional hour spent in a car per day was associated with a 6% increase in the likelihood of obesity. Conversely, each additional kilometer walked per day was associated with a 4.8% reduction in the likelihood of obesity. As a continuous measure, BMI was significantly associated with urban form for white cohorts. Relationships among urban form, walk distance, and time in a car were stronger among white than black cohorts.
Measures of the built environment and travel patterns are important predictors of obesity across gender and ethnicity, yet relationships among the built environment, travel patterns, and weight may vary across gender and ethnicity. Strategies to increase land-use mix and distance walked while reducing time in a car can be effective as health interventions.
Full-textDOI: · Available from: Tom L Schmid, Jul 30, 2014
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ABSTRACT: Collective efficacy, i.e., perception of mutual trust and willingness to help each other, is a measure of neighborhood social capital and has been associated with positive health outcomes including lower rates of assaults, homicide, premature mortality, and asthma. Collective efficacy is frequently considered a "cause", but we hypothesized that environmental features might be the foundation for or the etiology of personal reports of neighborhood collective efficacy. We analyzed data from the Los Angeles Family and Neighborhood Study (LAFANS) together with geographical data from Los Angeles County to determine which social and environmental features were associated with personal reports of collective efficacy, including presence of parks, alcohol outlets, elementary schools and fast food outlets. We used multi-level modeling controlling for age, education, annual family income, sex, marital status, employment and race/ethnicity at the individual level. At the tract level, we controlled for tract-level disadvantage, the number of off-sale alcohol outlets per roadway mile, the number of parks and the number of fast food outlets within the tract and within 1/2 mile of the tract's boundaries. We found that parks were independently and positively associated with collective efficacy; alcohol outlets were negatively associated with collective efficacy only when tract-level disadvantage was not included in the model. Fast food outlets and elementary schools were not linearly related to collective efficacy. Certain environmental features may set the stage for neighborhood social interactions, thus serving as a foundation for underlying health and well-being. Altering these environmental features may have greater than expected impact on health.Health & Place 07/2008; 14(2):198-208. DOI:10.1016/j.healthplace.2007.06.001 · 2.44 Impact Factor
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