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.
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"It is increasingly recognised that synergistic policies in sectors outside of health, including that of transportation, may have significant potential to improve physical activity rates and hence the health status of populations (Pratt et al., 2012). Ecological evidence suggests that countries with higher rates of active transport have lower rates of obesity (Bassett et al., 2008) and that a positive association may exist between motor vehicle usage and body weight (Sugiyama et al., 2013; Ding et al., 2014; Frank et al., 2004; Jacobson et al., 2011). Although establishing the health effects of active transport policies and interventions is challenging, a recent systematic review of trials and cohort studies found consistent support for the health benefits of active transport over longer periods and distances (Saunders et al., 2013). "
[Show abstract][Hide abstract] ABSTRACT: Physical inactivity is one of the leading causes for the growing prevalence of non-communicable diseases worldwide and there is a need for more evidence on the effectiveness and cost-effectiveness of interventions that aim to increase physical activity at the population level. This study aimed to update a systematic review published in 2008 by searching peer-reviewed and unpublished literature of economic evaluations of transport interventions that incorporate the health related effects of physical activity. Our analysis of methods for the inclusion of physical activity related health effects into transport appraisal over time demonstrates that methodological progress has been made. Thirty-six studies were included, reflecting an increasing recognition of the importance of incorporating these health effects into transport appraisal. However, significant methodological challenges in the incorporation of wider health benefits into transport appraisal still exist. The inclusion of physical activity related health effects is currently limited by paucity of evidence on morbidity effects and of more rigorous evidence on the effectiveness of interventions. Significant scope exists for better quality and more transparent reporting. A more consistent approach to the inclusion of benefits and disbenefits would reinforce the synergies between the health, environmental, transport and other sectors. From a transport sector perspective the inclusion of physical activity related health benefits positively impacts cost effectiveness, with the potential to contribute to a more efficient allocation of scarce resources based on a more comprehensive range of merits. From a public health perspective the inclusion of physical activity related health benefits may result in the funding of more interventions that promote active transport, with the potential to improve population levels of physical activity and to reduce prevalence of physical activity related diseases.
"This is particularly the case for those at higher risk of functional decline and major mobility disability for whom obesity may be especially deleterious[19,20]. Therefore, recognizing that obesity is a product of person by environmental factors[12,21], the main objective of this investigation was to study the joint relations of sociodemographic and perceived built environment factors with the likelihood of being obese in an older adult population at risk for mobility disability. A substantial body of evidence is now available supporting the observation that perceived and objective environmental measures assess two distinct dimensions of the built environment222324, and that neighborhood perceptions may be more closely related to actual behaviors[25,26]. "
[Show abstract][Hide abstract] ABSTRACT: Background:
Obesity is an increasingly prevalent condition among older adults, yet relatively little is known about how built environment variables may be associated with obesity in older age groups. This is particularly the case for more vulnerable older adults already showing functional limitations associated with subsequent disability.
The Lifestyle Interventions and Independence for Elders (LIFE) trial dataset (n = 1600) was used to explore the associations between perceived built environment variables and baseline obesity levels. Age-stratified recursive partitioning methods were applied to identify distinct subgroups with varying obesity prevalence.
Among participants aged 70-78 years, four distinct subgroups, defined by combinations of perceived environment and race-ethnicity variables, were identified. The subgroups with the lowest obesity prevalence (45.5-59.4 %) consisted of participants who reported living in neighborhoods with higher residential density. Among participants aged 79-89 years, the subgroup (of three distinct subgroups identified) with the lowest obesity prevalence (19.4 %) consisted of non-African American/Black participants who reported living in neighborhoods with friends or acquaintances similar in demographic characteristics to themselves. Overall support for the partitioned subgroupings was obtained using mixed model regression analysis.
The results suggest that, in combination with race/ethnicity, features of the perceived neighborhood built and social environments differentiated distinct groups of vulnerable older adults from different age strata that differed in obesity prevalence. Pending further verification, the results may help to inform subsequent targeting of such subgroups for further investigation.
Clinicaltrials.gov Identifier = NCT01072500.
Full-text · Article · Dec 2015 · International Journal of Behavioral Nutrition and Physical Activity
"After obtaining the Sina Weibo points of interest (POIs) ① data and the check-in data ② through internet crawling technology, Long and Liu (2013) firstly managed to divide an entire research area into grid cells in consideration of various land uses reflected by these data and the actual and planned land use data of Beijing. Then, borrowing the mixed land use index proposed by Frank et al. (2004), they were able to identify the land use mix of each grid cell. Yuan et al. (2012) evaluated urban functions of the traffic analysis zone (TAZ) by using the taxi trajectory and POIs data in Beijing, and they suggested combining the bus card data with the taxi trajectory data to achieve a more comprehensive evaluation on urban functional structure. "