Cross-Sectional Study of Location-Based Built Environments, Physical Activity, Dietary Intake, and Body Mass Index in Adult Twins.

We examined relationships between walkability and health behaviors between and within identical twin pairs, considering both home (neighborhood) walkability and each twin's measured activity space. Continuous activity and location data (via accelerometry and GPS) were obtained in 79 pairs over 2 weeks. Walkability was estimated using Walk Score® (WS); home WS refers to neighborhood walkability, and GPS WS refers to the mean of individual WSs matched to every GPS point collected by each participant. GPS WS was assessed within (WHN) and out of the neighborhood (OHN), using 1-mile Euclidean (air1mi) and network (net1mi) buffers. Outcomes included walking and moderate-to-vigorous physical activity (MVPA) bouts, dietary energy density (DED), and BMI. Home WS was associated with WHN GPS WS (b = 0.71, SE = 0.03, p < 0.001 for air1mi; b = 0.79, SE = 0.03, p < 0.001 for net1mi), and OHN GPS WS (b = 0.18, SE = 0.04, p < 0.001 for air1mi; b = 0.22, SE = 0.04, p < 0.001 for net1mi). Quasi-causal relationships (within-twin) were observed for home and GPS WS with walking (ps < 0.01), but not MVPA, DED, or BMI. Results support previous literature that neighborhood walkability has a positive influence on walking.

Deciphering the impact of urban built environment density on respiratory health using a quasi-cohort analysis of 5495 non-smoking lung cancer cases.

INTRODUCTION: Lung cancer is a major health concern and is influenced by air pollution, which can be affected by the density of urban built environment. The spatiotemporal impact of urban density on lung cancer incidence remains unclear, especially at the sub-city level. We aimed to determine cumulative effect of community-level density attributes of the built environment on lung cancer incidence in high-density urban areas. METHODS: We selected 78 communities in the central city of Shanghai, China as the study site; communities included in the analysis had an averaged population density of 313 residents per hectare. Using data from the city cancer surveillance system, an age-period-cohort analysis of lung cancer incidence was performed over a five-year period (2009-2013), with a total of 5495 non-smoking/non-secondhand smoking exposure lung cancer cases. Community-level density measures included the density of road network, facilities, buildings, green spaces, and land use mixture. RESULTS: In multivariate models, built environment density and the exposure time duration had an interactive effect on lung cancer incidence. Lung cancer incidence of birth cohorts was associated with road density and building coverage across communities, with a relative risk of 1·142 (95 % CI: 1·056-1·234, P = 0·001) and 1·090 (95 % CI: 1·053-1·128, P < 0·001) at the baseline year (2009), respectively. The relative risk increased exponentially with the exposure time duration. As for the change in lung cancer incidence over the five-year period, lung cancer incidence of birth cohorts tended to increase faster in communities with a higher road density and building coverage. CONCLUSION: Urban planning policies that improve road network design and building layout could be important strategies to reduce lung cancer incidence in high-density urban areas.

Obesity, diet quality, physical activity, and the built environment: the need for behavioral pathways.

BACKGROUND: The built environment (BE) is said to influence local obesity rates. Few studies have explored causal pathways between home-neighborhood BE variables and health outcomes such as obesity. Such pathways are likely to involve both physical activity and diet. METHODS: The Seattle Obesity Study (SOS II) was a longitudinal cohort of 440 adult residents of King Co, WA. Home addresses were geocoded. Home-neighborhood BE measures were framed as counts and densities of food sources and physical activity locations. Tax parcel property values were obtained from County tax assessor. Healthy Eating Index (HEI 2010) scores were constructed using data from food frequency questionnaires. Physical activity (PA) was obtained by self-report. Weights and heights were measured at baseline and following 12 months' exposure. Multivariable regressions examined the associations among BE measures at baseline, health behaviors (HEI-2010 and physical activity) at baseline, and health outcome both cross-sectionally and longitudinally. RESULTS: None of the conventional neighborhood BE metrics were associated either with diet quality, or with meeting PA guidelines. Only higher property values did predict better diets and more physical activity. Better diets and more physical activity were associated with lower obesity prevalence at baseline and 12 mo, but did not predict weight change. CONCLUSION: Any links between the BE and health outcomes critically depend on establishing appropriate behavioral pathways. In this study, home-centric BE measures, were not related to physical activity or to diet. Further studies will need to consider a broader range of BE attributes that may be related to diets and health.

Worksite neighborhood and obesogenic behaviors: findings among employees in the Promoting Activity and Changes in Eating (PACE) trial.

BACKGROUND: Understanding mechanisms linking neighborhood context to health behaviors may provide targets for increasing lifestyle intervention effectiveness. Although associations between home neighborhood and obesogenic behaviors have been studied, less is known about the role of worksite neighborhood. PURPOSE: To evaluate associations between worksite neighborhood context at baseline (2006) and change in obesogenic behaviors of adult employees at follow-up (2007-2009) in a worksite randomized trial to prevent weight gain. METHODS: Worksite property values were used as an indicator of worksite neighborhood SES (NSES). Worksite neighborhood built environment attributes associated with walkability were evaluated as explanatory factors in relationships among worksite NSES, diet, and physical activity behaviors of employees. Behavioral data were collected at baseline (2005-2007) and follow-up (2007-2009). Multilevel linear and logistic models were constructed adjusting for covariates and accounting for clustering within worksites. Product-of-coefficients methods were used to assess mediation. Analyses were performed after study completion (2011-2012). RESULTS: Higher worksite NSES was associated with more walking (OR=1.16, 95% CI=1.03, 1.30, p=0.01). Higher density of residential units surrounding worksites was associated with more walking and eating five or more daily servings of fruits and vegetables, independent of worksite NSES. Residential density partially explained relationships among worksite NSES, fruit and vegetable consumption, and walking. CONCLUSIONS: Worksite neighborhood context may influence employees' obesogenic behaviors. Furthermore, residential density around worksites could be an indicator of access to dietary and physical activity-related infrastructure in urban areas. This may be important given the popularity of worksites as venues for obesity prevention efforts.

BALANCE (Bioengineering Approaches for Lifestyle Activity and Nutrition Continuous Engagement): developing new technology for monitoring energy balance in real time.

Methods that measure energy balance accurately in real time represent promising avenues to address the obesity epidemic. We developed an electronic food diary on a mobile phone that includes an energy balance visualization and computes and displays the difference between energy intake from food entries and energy expenditure from a multiple-sensor device that provides objective estimates of energy expenditure in real time. A geographic information system dataset containing locations associated with activity and eating episodes is integrated with an ArcPad mapping application on the phone to provide users with a visual display of food sources and locations associated with physical activity within their proximal environment. This innovative tool captures peoples' movement through space and time under free-living conditions and could potentially have many health-related applications in the future.

Work group II: Using Geographic Information Systems for enhancing research relevant to policy on diet, physical activity, and weight.

Geographic Information Systems (GIS) was a theme for one of the four workgroups convened for the Measures of the Food and Built Environment meeting held in Bethesda, Maryland in November 2007. This summary of group discussions frames several critical conceptual, methodologic, and data challenges regarding the use of GIS to enhance research relevant to policy on diet, physical activity, and weight. Broad recommendations are offered in five areas: (1) theoretical and conceptual development in framing place effects on health; (2) contextualizing people and spatial behavior in built environments and improving empirical representations of place; (3) geospatial data availability, quality, and standards; (4) privacy and confidentiality; and, (5) building capacity in GIS personnel and infrastructure. These topics are inter-related. Although our discussion focuses on issues relevant to the role of the built environment in diet and physical activity outcomes, our recommendations also are salient to health and environment research generally.

Protective association between neighborhood walkability and depression in older men.

OBJECTIVES: To evaluate the association between neighborhood walkability and depression in older adults. DESIGN: Cross-sectional analysis using data from Adult Changes in Thought (ACT), a prospective, longitudinal cohort study. SETTING: King County, Washington. PARTICIPANTS: Seven hundred forty randomly selected men and women aged 65 and older, cognitively intact, living in the same home for at least 2 years. MEASUREMENTS: Depressive symptoms were measured with the Center for Epidemiologic Studies Depression Scale. The Walkable and Bikable Communities Project provided objective data predicting the probability of walking at least 150 minutes per week in a particular neighborhood. ACT data were linked at the individual level via a geographic information system to this walkability score using buffer radii of 100, 500, and 1,000 meters around the subject's home. Multiple regression analysis tests were conducted for associations between the buffer-specific neighborhood walkability score and depressive symptoms. RESULTS: There was a significant association between neighborhood walkability and depressive symptoms in men when adjusted for individual-level factors of income, physical activity, education, smoking status, living alone, age, ethnicity, and chronic disease. The odds ratio for the interquartile range (25th to 75th percentile) of walkability score was 0.31 to 0.33 for the buffer radii (P=.02), indicating a protective association with neighborhood walkability. This association was not significant in women. CONCLUSION: This study demonstrates a significant association between neighborhood walkability and depressive symptoms in older men. Further research on the effects of neighborhood walkability may inform community-level mental health treatment and focus depression screening in less-walkable areas.