Residential Density Is Associated With BMI Trajectories in Children and Adolescents: Findings From the Moving to Health Study

Introduction

This study investigates the associations between built environment features and 3-year BMI trajectories in children and adolescents.

Methods

This retrospective cohort study utilized electronic health records of individuals aged 5-18 years living in King County, Washington, from 2005 to 2017. Built environment features such as residential density; counts of supermarkets, fast-food restaurants, and parks; and park area were measured using SmartMaps at 1,600-meter buffers. Linear mixed-effects models performed in 2022 tested whether built environment variables at baseline were associated with BMI change within age cohorts (5, 9, and 13 years), adjusting for sex, age, race/ethnicity, Medicaid, BMI, and residential property values (SES measure).

Results

At 3-year follow-up, higher residential density was associated with lower BMI increase for girls across all age cohorts and for boys in age cohorts of 5 and 13 years but not for the age cohort of 9 years. Presence of fast food was associated with higher BMI increase for boys in the age cohort of 5 years and for girls in the age cohort of 9 years. There were no significant associations between BMI change and counts of parks, and park area was only significantly associated with BMI change among boys in the age cohort of 5 years.

Conclusions

Higher residential density was associated with lower BMI increase in children and adolescents. The effect was small but may accumulate over the life course. Built environment factors have limited independent impact on 3-year BMI trajectories in children and adolescents.

Neighborhood built and food environment in relation to glycemic control in people with type 2 diabetes in the moving to health study

OBJECTIVE

To examine whether built environment and food metrics are associated with glycemic control in people with type 2 diabetes.

RESEARCH DESIGN AND METHODS

We included 14,985 patients with type 2 diabetes using electronic health records from Kaiser Permanente Washington. Patient addresses were geocoded with ArcGIS using King County and Esri reference data. Built environment exposures estimated from geocoded locations included residential unit density, transit threshold residential unit density, park access, and having supermarkets and fast food restaurants within 1600-m Euclidean buffers. Linear mixed effects models compared mean changes of HbA1c from baseline at 1, 3 (primary) and 5 years by each built environment variable.

RESULTS

Patients (mean age = 59.4 SD = 13.2, 49.5% female, 16.6% Asian, 9.8% Black, 5.5% Latino/Hispanic, 57.1% White, 20% insulin dependent, mean BMI = 32.7±7.7) had an average of 6 HbA1c measures available. Participants in the 1st tertile of residential density (lowest) had a greater decline in HbA1c (-0.42, -0.43, and -0.44 in years 1, 3, and 5 respectively) than those in the 3rd tertile (HbA1c = -0.37 at 1- and 3-years and -0.36 at 5-years; all p-values <0.05). Having any supermarkets within 1600 m of home was associated with a greater decrease in HbA1c at 1-year and 3-years compared to having none (all p-values <0.05).

CONCLUSIONS

Lower residential density and better proximity to supermarkets may benefit HbA1c control in people with people with type 2 diabetes. However, effects were small and indicate limited clinical significance.

Street environments and crime around low-income and minority schools: Adopting an environmental audit tool to assess crime prevention through environmental design (CPTED)

Crime prevention through environmental design (CPTED) suggests an association between micro-scale environmental conditions and crime, but little empirical research exists on the detailed street-level environmental features associated with crime near low-income and minority schools. This study focuses on the neighborhoods around 14 elementary schools serving lower income populations in Seattle, WA to assess if the distribution of crime incidences (2013-2017) is linked with the street-level environmental features that reflect CPTED principles. We used a total of 40 audit variables that were included in the four domains derived from the broken windows theory and CPTED principles: natural surveillance (e.g., number of windows, balconies, and a sense of surveillance), territoriality (e.g., crime watch signs, trees), image/maintenance (e.g., graffiti and a sense of maintenance/cleanness), and geographical juxtaposition (e.g., bus stops, presence of arterial). We found that multiple crime types had significant associations with CPTED components at the street level. Among the CPTED domains, two image/maintenance features (i.e., maintenance of streets and visual quality of buildings) and two geographical juxtaposition features (i.e., being adjacent to multi-family housing and bus stops) were consistently associated with both violent and property crime. The findings suggest that local efforts to improve maintenance of streets and visual quality of buildings and broader planning efforts to control specific land uses near schools are important to improve safety in marginalized neighborhoods near schools that tend to be more vulnerable to crime. Our research on micro-scale environmental determinants of crime can also serve as promising targets for CPTED research and initiatives.

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.

Differences in Weight Gain Following Residential Relocation in the Moving to Health (M2H) Study

BACKGROUND

Neighborhoods may play an important role in shaping long-term weight trajectory and obesity risk. Studying the impact of moving to another neighborhood may be the most efficient way to determine the impact of the built environment on health. We explored whether residential moves were associated with changes in body weight.

METHODS

Kaiser Permanente Washington electronic health records were used to identify 21,502 members aged 18-64 who moved within King County, WA between 2005 and 2017. We linked body weight measures to environment measures, including population, residential, and street intersection densities (800 m and 1,600 m Euclidian buffers) and access to supermarkets and fast foods (1,600 m and 5,000 m network distances). We used linear mixed models to estimate associations between postmove changes in environment and changes in body weight.

RESULTS

In general, moving from high-density to moderate- or low-density neighborhoods was associated with greater weight gain postmove. For example, those moving from high to low residential density neighborhoods (within 1,600 m) gained an average of 4.5 (95% confidence interval [CI] = 3.0, 5.9) lbs 3 years after moving, whereas those moving from low to high-density neighborhoods gained an average of 1.3 (95% CI = -0.2, 2.9) lbs. Also, those moving from neighborhoods without fast-food access (within 1600m) to other neighborhoods without fast-food access gained less weight (average 1.6 lbs [95% CI = 0.9, 2.4]) than those moving from and to neighborhoods with fast-food access (average 2.8 lbs [95% CI = 2.5, 3.2]).

CONCLUSIONS

Moving to higher-density neighborhoods may be associated with reductions in adult weight gain.

Determining thresholds for spatial urban design and transport features that support walking to create healthy and sustainable cities: findings from the IPEN Adult study

An essential characteristic of a healthy and sustainable city is a physically active population. Effective policies for healthy and sustainable cities require evidence-informed quantitative targets. We aimed to identify the minimum thresholds for urban design and transport features associated with two physical activity criteria: at least 80% probability of engaging in any walking for transport and WHO's target of at least 15% relative reduction in insufficient physical activity through walking. The International Physical Activity and the Environment Network Adult (known as IPEN) study (N=11 615; 14 cities across ten countries) provided data on local urban design and transport features linked to walking. Associations of these features with the probability of engaging in any walking for transport and sufficient physical activity (≥150 min/week) by walking were estimated, and thresholds associated with the physical activity criteria were determined. Curvilinear associations of population, street intersection, and public transport densities with walking were found. Neighbourhoods exceeding around 5700 people per km², 100 intersections per km², and 25 public transport stops per km² were associated with meeting one or both physical activity criteria. Shorter distances to the nearest park were associated with more physical activity. We use the results to suggest specific target values for each feature as benchmarks for progression towards creating healthy and sustainable cities.

Using open data and open-source software to develop spatial indicators of urban design and transport features for achieving healthy and sustainable cities

Benchmarking and monitoring of urban design and transport features is crucial to achieving local and international health and sustainability goals. However, most urban indicator frameworks use coarse spatial scales that either only allow between-city comparisons, or require expensive, technical, local spatial analyses for within-city comparisons. This study developed a reusable, open-source urban indicator computational framework using open data to enable consistent local and global comparative analyses. We show this framework by calculating spatial indicators-for 25 diverse cities in 19 countries-of urban design and transport features that support health and sustainability. We link these indicators to cities' policy contexts, and identify populations living above and below critical thresholds for physical activity through walking. Efforts to broaden participation in crowdsourcing data and to calculate globally consistent indicators are essential for planning evidence-informed urban interventions, monitoring policy effects, and learning lessons from peer cities to achieve health, equity, and sustainability goals.

What next? Expanding our view of city planning and global health, and implementing and monitoring evidence-informed policy

This Series on urban design, transport, and health aimed to facilitate development of a global system of health-related policy and spatial indicators to assess achievements and deficiencies in urban and transport policies and features. This final paper in the Series summarises key findings, considers what to do next, and outlines urgent key actions. Our study of 25 cities in 19 countries found that, despite many well intentioned policies, few cities had measurable standards and policy targets to achieve healthy and sustainable cities. Available standards and targets were often insufficient to promote health and wellbeing, and health-supportive urban design and transport features were often inadequate or inequitably distributed. City planning decisions affect human and planetary health and amplify city vulnerabilities, as the COVID-19 pandemic has highlighted. Hence, we offer an expanded framework of pathways through which city planning affects health, incorporating 11 integrated urban system policies and 11 integrated urban and transport interventions addressing current and emerging issues. Our call to action recommends widespread uptake and further development of our methods and open-source tools to create upstream policy and spatial indicators to benchmark and track progress; unmask spatial inequities; inform interventions and investments; and accelerate transitions to net zero, healthy, and sustainable cities.

How Does Ride-Hailing Influence Individual Mode Choice? An Examination Using Longitudinal Trip Data from the Seattle Region

This study investigates the impacts of ride-hailing, which we define as mobility services consisting of both conventional taxis and app-based services offered by transportation network companies, on individual mode choice. We examine whether ride-hailing substitutes for or complements travel by driving, public transit, or walking and biking. The study overcomes some of the limitations of convenience samples or cross-sectional surveys used in past research by employing a longitudinal dataset of individual travel behavior and socio-demographic information. The data include three waves of travel log data collected between 2012 to 2018 in transit-rich areas of the Seattle region. We conducted individual-level panel data modeling, estimating independently pooled models and fixed-effect models of average daily trip count and duration for each mode, while controlling for various factors that affect travel behavior. The results provide evidence of substitution effects of ride-hailing on driving. We found that cross-sectionally, participants who used more ride-hailing tended to drive less, and that longitudinally, an increase in ride-hailing usage was associated with fewer driving trips. No significant associations were found between ride-hailing and public transit usage or walking and biking. Based on detailed travel data of a large population in a major US metropolitan area, the study highlights the value of collecting and analyzing longitudinal data to understand the impacts of new mobility services.

Differential associations of the built environment on weight gain by sex and race/ethnicity but not age

OBJECTIVE

To explore the built environment (BE) and weight change relationship by age, sex, and racial/ethnic subgroups in adults.

METHODS

Weight trajectories were estimated using electronic health records for 115,260 insured Kaiser Permanente Washington members age 18-64 years. Member home addresses were geocoded using ArcGIS. Population, residential, and road intersection densities and counts of area supermarkets and fast food restaurants were measured with SmartMaps (800 and 5000-meter buffers) and categorized into tertiles. Linear mixed-effect models tested whether associations between BE features and weight gain at 1, 3, and 5 years differed by age, sex, and race/ethnicity, adjusting for demographics, baseline weight, and residential property values.

RESULTS

Denser urban form and greater availability of supermarkets and fast food restaurants were associated with differential weight change across sex and race/ethnicity. At 5 years, the mean difference in weight change comparing the 3rd versus 1st tertile of residential density was significantly different between males (-0.49 kg, 95% CI: -0.68, -0.30) and females (-0.17 kg, 95% CI: -0.33, -0.01) (P-value for interaction = 0.011). Across race/ethnicity, the mean difference in weight change at 5 years for residential density was significantly different among non-Hispanic (NH) Whites (-0.47 kg, 95% CI: -0.61, -0.32), NH Blacks (-0.86 kg, 95% CI: -1.37, -0.36), Hispanics (0.10 kg, 95% CI: -0.46, 0.65), and NH Asians (0.44 kg, 95% CI: 0.10, 0.78) (P-value for interaction <0.001). These findings were consistent for other BE measures.

CONCLUSION

The relationship between the built environment and weight change differs across demographic groups. Careful consideration of demographic differences in associations of BE and weight trajectories is warranted for investigating etiological mechanisms and guiding intervention development.

What county-level factors influence COVID-19 incidence in the United States? Findings from the first wave of the pandemic

Effective control of the COVID-19 pandemic via appropriate management of the built environment is an urgent issue. This study develops a research framework to explore the relationship between COVID-19 incidence and influential factors related to protection of vulnerable populations, intervention in transmission pathways, and provision of healthcare resources. Relevant data for regression analysis and structural equation modeling is collected during the first wave of the pandemic in the United States, from counties with over 100 confirmed cases. In addition to confirming certain factors found in the existing literature, we uncover six new factors significantly associated with COVID-19 incidence. Furthermore, incidence during the lockdown is found to significantly affect incidence after the reopening, highlighting that timely quarantining and treating of patients is essential to avoid the snowballing transmission over time. These findings suggest ways to mitigate the negative effects of subsequent waves of the pandemic, such as special attention of infection prevention in neighborhoods with unsanitary and overcrowded housing, minimization of social activities organized by neighborhood associations, and contactless home delivery service of healthy food. Also worth noting is the need to provide support to people less capable of complying with the stay-at-home order because of their occupations or socio-economic disadvantage.

Does the built environment have independent obesogenic power? Urban form and trajectories of weight gain

OBJECTIVE

To determine whether selected features of the built environment can predict weight gain in a large longitudinal cohort of adults.

METHODS

Weight trajectories over a 5-year period were obtained from electronic health records for 115,260 insured patients aged 18-64 years in the Kaiser Permanente Washington health care system. Home addresses were geocoded using ArcGIS. Built environment variables were population, residential unit, and road intersection densities captured using Euclidean-based SmartMaps at 800-m buffers. Counts of area supermarkets and fast food restaurants were obtained using network-based SmartMaps at 1600, and 5000-m buffers. Property values were a measure of socioeconomic status. Linear mixed effects models tested whether built environment variables at baseline were associated with long-term weight gain, adjusting for sex, age, race/ethnicity, Medicaid insurance, body weight, and residential property values.

RESULTS

Built environment variables at baseline were associated with differences in baseline obesity prevalence and body mass index but had limited impact on weight trajectories. Mean weight gain for the full cohort was 0.06 kg at 1 year (95% CI: 0.03, 0.10); 0.64 kg at 3 years (95% CI: 0.59, 0.68), and 0.95 kg at 5 years (95% CI: 0.90, 1.00). In adjusted regression models, the top tertile of density metrics and frequency counts were associated with lower weight gain at 5-years follow-up compared to the bottom tertiles, though the mean differences in weight change for each follow-up year (1, 3, and 5) did not exceed 0.5 kg.

CONCLUSIONS

Built environment variables that were associated with higher obesity prevalence at baseline had limited independent obesogenic power with respect to weight gain over time. Residential unit density had the strongest negative association with weight gain. Future work on the influence of built environment variables on health should also examine social context, including residential segregation and residential mobility.

Measurement of neighborhood-based physical activity bouts

This study examined how buffer type (shape), size, and the allocation of activity bouts inside buffers that delineate the neighborhood spatially produce different estimates of neighborhood-based physical activity. A sample of 375 adults wore a global positioning system (GPS) data logger and accelerometer over 2 weeks under free-living conditions. Analytically, the amount of neighborhood physical activity measured objectively varies substantially, not only due to buffer shape and size, but by how GPS-based activity bouts are identified with respect to containment within neighborhood buffers. To move the "neighborhood-effects" literature forward, it is critical to delineate the spatial extent of the neighborhood, given how different ways of measuring GPS-based activity containment will result in different levels of physical activity across different buffer types and sizes.

Small increments in diet cost can improve compliance with the Dietary Guidelines for Americans

Adherence to the Dietary Guidelines for Americans (DGA) may involve higher diet costs. This study assessed the relation between two measures of food spending and diet quality among adult participants (N = 768) in the Seattle Obesity Study (SOS III). All participants completed socio-demographic and food expenditure surveys and the Fred Hutch food frequency questionnaire. Dietary intakes were joined with local supermarket prices to estimate individual-level diet costs. Healthy Eating Index (HEI- 2015) scores measured compliance with DGA. Multiple linear regressions using Generalized Estimating Equations with robust standard errors showed that lower food spending was associated with younger age, Hispanic ethnicity, and lower socioeconomic status. Even though higher HEI-2015 scores were associated with higher diet costs per 2000 kcal, much individual variability was observed. A positive curvilinear relationship was observed in adjusted models. At lower cost diets, a $100/month increase in cost (from $150 to $250) was associated with a 20.6% increase in HEI-2015. For higher levels of diet cost (from $350 to $450) there were diminishing returns (2.8% increase in HEI- 2015). These findings indicate that increases in food spending at the lower end of the range have the most potential to improve diet quality.

Residential neighborhood features associated with objectively measured walking near home: Revisiting walkability using the Automatic Context Measurement Tool (ACMT)

Many distinct characteristics of the social, natural, and built neighborhood environment have been included in walkability measures, and it is unclear which measures best describe the features of a place that support walking. We developed the Automatic Context Measurement Tool, which measures neighborhood environment characteristics from public data for any point location in the United States. We explored these characteristics in home neighborhood environments in relation to walking identified from integrated GPS, accelerometer, and travel log data from 681 residents of King Country, WA. Of 146 neighborhood characteristics, 92 (63%) were associated with walking bout counts after adjustment for individual characteristics and correction for false discovery. The strongest built environment predictor of walking bout count was housing unit count. Models using data-driven and a priori defined walkability measures exhibited similar fit statistics. Walkability measures consisting of different neighborhood characteristic measurements may capture the same underlying variation in neighborhood conditions.

Impact of Built Environments on Body Weight (the Moving to Health Study): Protocol for a Retrospective Longitudinal Observational Study

BACKGROUND

Studies assessing the impact of built environments on body weight are often limited by modest power to detect residential effects that are small for individuals but may nonetheless comprise large attributable risks.

OBJECTIVE

We used data extracted from electronic health records to construct a large retrospective cohort of patients. This cohort will be used to explore both the impact of moving between environments and the long-term impact of changing neighborhood environments.

METHODS

We identified members with at least 12 months of Kaiser Permanente Washington (KPWA) membership and at least one weight measurement in their records during a period between January 2005 and April 2017 in which they lived in King County, Washington. Information on member demographics, address history, diagnoses, and clinical visits data (including weight) was extracted. This paper describes the characteristics of the adult (aged 18-89 years) cohort constructed from these data.

RESULTS

We identified 229,755 adults representing nearly 1.2 million person-years of follow-up. The mean age at baseline was 45 years, and 58.0% (133,326/229,755) were female. Nearly one-fourth of people (55,150/229,755) moved within King County at least once during the follow-up, representing 84,698 total moves. Members tended to move to new neighborhoods matching their origin neighborhoods on residential density and property values.

CONCLUSIONS

Data were available in the KPWA database to construct a very large cohort based in King County, Washington. Future analyses will directly examine associations between neighborhood conditions and longitudinal changes in body weight and diabetes as well as other health conditions.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/16787.

Intersections and Non-Intersections: A Protocol for Identifying Pedestrian Crash Risk Locations in GIS

Intersection and non-intersection locations are commonly used as spatial units of analysis for modeling pedestrian crashes. While both location types have been previously studied, comparing results is difficult given the different data and methods used to identify crash-risk locations. In this study, a systematic and replicable protocol was developed in GIS (Geographic Information System) to create a consistent spatial unit of analysis for use in pedestrian crash modelling. Four publicly accessible datasets were used to identify unique intersection and non-intersection locations: Roadway intersection points, roadway lanes, legal speed limits, and pedestrian crash records. Two algorithms were developed and tested using five search radii (ranging from 20 to 100 m) to assess the protocol reliability. The algorithms, which were designed to identify crash-risk locations at intersection and non-intersection areas detected 87.2% of the pedestrian crash locations (r: 20 m). Agreement rates between algorithm results and the crash data were 94.1% for intersection and 98.0% for non-intersection locations, respectively. The buffer size of 20 m generally showed the highest performance in the analyses. The present protocol offered an efficient and reliable method to create spatial analysis units for pedestrian crash modeling. It provided researchers a cost-effective method to identify unique intersection and non-intersection locations. Additional search radii should be tested in future studies to refine the capture of crash-risk locations.

Probabilistic walking models using built environment and sociodemographic predictors

Background

Individual sociodemographic and home neighborhood built environment (BE) factors influence the probability of engaging in health-enhancing levels of walking or moderate-to-vigorous physical activity (MVPA). Methods are needed to parsimoniously model the associations.

Methods

Participants included 2392 adults drawn from a community-based twin registry living in the Seattle region. Objective BE measures from four domains (regional context, neighborhood composition, destinations, transportation) were taken for neighborhood sizes of 833 and 1666 road network meters from home. Hosmer and Lemeshow’s methods served to fit logistic regression models of walking and MVPA outcomes using sociodemographic and BE predictors. Backward elimination identified variables included in final models, and comparison of receiver operating characteristic (ROC) curves determined model fit improvements.

Results

Built environment variables associated with physical activity were reduced from 86 to 5 or fewer. Sociodemographic and BE variables from all four BE domains were associated with activity outcomes but differed by activity type and neighborhood size. For the study population, ROC comparisons indicated that adding BE variables to a base model of sociodemographic factors did not improve the ability to predict walking or MVPA.

Conclusions

Using sociodemographic and built environment factors, the proposed approach can guide the estimation of activity prediction models for different activity types, neighborhood sizes, and discrete BE characteristics. Variables associated with walking and MVPA are population and neighborhood BE-specific.

Electronic supplementary material

The online version of this article (10.1186/s12963-019-0186-8) contains supplementary material, which is available to authorized users.

The association between park facilities and the occurrence of physical activity during park visits

Prior research has found a positive relationship between the variety of park facilities and park-based physical activity (PA), but has not provided an estimate of the effect that additional different PA facilities have on whether an individual is active during a park visit. Using objective measures of park visits and PA from an urban sample of 225 adults in King County, Washington, we compared the variety of PA facilities in parks visited where an individual was active to PA facilities in parks where the same individual was sedentary. Each additional different PA facility at a park was associated with a 6% increased probability of being active during a visit. Adding additional different PA facilities to a park appears to have a moderate effect on whether an individual is active during a park visit, which could translate into large community health impacts when scaled up to multiple park visitors.

The Association Between Park Facilities and Duration of Physical Activity During Active Park Visits

Public parks provide places for urban residents to obtain physical activity (PA), which is associated with numerous health benefits. Adding facilities to existing parks could be a cost-effective approach to increase the duration of PA that occurs during park visits. Using objectively measured PA and comprehensively measured park visit data among an urban community-dwelling sample of adults, we tested the association between the variety of park facilities that directly support PA and the duration of PA during park visits where any PA occurred. Cross-classified multilevel models were used to account for the clustering of park visits (n = 1553) within individuals (n = 372) and parks (n = 233). Each additional different PA facility at a park was independently associated with a 6.8% longer duration of PA bouts that included light-intensity activity, and an 8.7% longer duration of moderate to vigorous PA time. Findings from this study are consistent with the hypothesis that more PA facilities increase the amount of PA that visitors obtain while already active at a park.

Environmental and Individual Predictors of Healthy Dietary Behaviors in a Sample of Middle Aged Hispanic and Caucasian Women

The objective of this effort is to gather data to tailor interventions appropriately. Greater understanding of the correlates of socioeconomic status and obesogenic dietary behaviors was the focus of this manuscript. Using multistage sampling, women with varied education levels completed a baseline assessment in a longitudinal study of women aged 30 to 50 years. This study was conducted in low-SES areas of South King County, Washington State. This study included 530 Caucasian and 510 Hispanic women. Fruit and vegetable consumption was positively associated and soft drink consumption inversely associated with the level of education in Caucasian women. In contrast, percentage calories from fat was positively associated with the level of education in Hispanic women. In Hispanic women, level of education interacted significantly with food security in relation to percentage calories from fat, and with eating norms in relation to soft drink consumption. Neighborhood presence of ethnic food stores was associated with outcomes for Hispanic women, but for Caucasians, presence of fast food restaurants was important. Education was consistently associated with two of the three obesogenic dietary behaviors studied among Caucasian women. Education played a moderating role in the associations of food security and eating norms, independent of area level food availability, in two of three obesogenic dietary behaviors studied. However, these patterns differed for Hispanic women, indicating the need for more research into important variables to support change in Hispanic women. Women of differing ethnic groups did not respond similarly to environmental conditions and policy-relevant surroundings. These data have meaning for considering urban policy that impacts obesity levels in the population.

Comparisons of Physical Activity and Walking Between Korean Immigrant and White Women in King County, WA

Immigrant and minority women are less physically active than White women particularly during leisure time. However, prior research demonstrates that reported household physical activity (PA) and non-leisure time walking/biking were higher among the former. Using accelerometers, GPS, and travel logs, transport-related, home-based, and leisure time PA were measured objectively for 7 days from a convenience sample of 60 first-generation Korean immigrant women and 69 matched White women from the Travel Assessment and Community Project in King County, Washington. Time spent in total PA, walking, and home-based PA was higher among Whites than Korean immigrants regardless of PA type or location. 58 % of the White women but only 20 % of the Korean women met CDC's PA recommendations. Socio-economic status, psychosocial factors, and participants' neighborhood built environmental factors failed to account for the observed PA differences between these groups.

Utilitarian and Recreational Walking Among Spanish- and English-Speaking Latino Adults in Micropolitan US Towns

BACKGROUND

Walking among Latinos in US Micropolitan towns may vary by language spoken.

METHODS

In 2011-2012, we collected telephone survey and built environment (BE) data from adults in six towns located within micropolitan counties from two states with sizable Latino populations. We performed mixed-effects logistic regression modeling to examine relationships between ethnicity-language group [Spanish-speaking Latinos (SSLs); English-speaking Latinos (ESLs); and English-speaking non-Latinos (ENLs)] and utilitarian walking and recreational walking, accounting for socio-demographic, lifestyle and BE characteristics.

RESULTS

Low-income SSLs reported higher amounts of utilitarian walking than ENLs (p = 0.007), but utilitarian walking in this group decreased as income increased. SSLs reported lower amounts of recreational walking than ENLs (p = 0.004). ESL-ENL differences were not significant. We identified no statistically significant interactions between ethnicity-language group and BE characteristics.

DISCUSSION

Approaches to increase walking in micropolitan towns with sizable SSL populations may need to account for this group's differences in walking behaviors.

The Causal Effect of Bus Rapid Transit on Changes in Transit Ridership

Numerous studies have reported ridership increases along routes when Bus rapid transit (BRT) replaces conventional bus service, but these increases could be due simply to broader temporal trends in transit ridership. To address this limitation, we compared changes in ridership among routes where BRT was implemented to routes where BRT was planned or already existed in King County, Washington. Ridership was measured at 2010, 2013, and 2014. Ridership increased by 35% along routes where BRT was implemented from 2010 to 2013 compared to routes that maintained conventional bus service. Ridership increased by 29% along routes where BRT was implemented from 2013 to 2014 compared to consistent existing BRT service. These results provide stronger evidence for a causal relationship between BRT and increased transit ridership and a more accurate estimate of the independent effect of BRT on ridership.

Secondary GIS built environment data for health research: guidance for data development

Built environment (BE) data in geographic information system (GIS) format are increasingly available from public agencies and private providers. These data can provide objective, low-cost BE data over large regions and are often used in public health research and surveillance. Yet challenges exist in repurposing GIS data for health research. The GIS data do not always capture desired constructs; the data can be of varying quality and completeness; and the data definitions, structures, and spatial representations are often inconsistent across sources. Using the Small Town Walkability study as an illustration, we describe (a) the range of BE characteristics measurable in a GIS that may be associated with active living, (b) the availability of these data across nine U.S. small towns, (c) inconsistencies in the GIS BE data that were available, and (d) strategies for developing accurate, complete, and consistent GIS BE data appropriate for research. Based on a conceptual framework and existing literature, objectively measurable characteristics of the BE potentially related to active living were classified under nine domains: generalized land uses, morphology, density, destinations, transportation system, traffic conditions, neighborhood behavioral conditions, economic environment, and regional location. At least some secondary GIS data were available across all nine towns for seven of the nine BE domains. Data representing high-resolution or behavioral aspects of the BE were often not available. Available GIS BE data - especially tax parcel data - often contained varying attributes and levels of detail across sources. When GIS BE data were available from multiple sources, the accuracy, completeness, and consistency of the data could be reasonable ensured for use in research. But this required careful attention to the definition and spatial representation of the BE characteristic of interest. Manipulation of the secondary source data was often required, which was facilitated through protocols.

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.

Walking and Bicycling: An Evaluation of Environmental Audit Instruments

Purpose.

This paper reviews existing environmental audit instruments used to capture the walkability and bikability of environments. The review inventories and evaluates individual measures of environmental factors used in these instruments. It synthesizes the current state of knowledge in quantifying the built environment. The paper provides health promotion professionals an understanding of the essential aspects of environments influencing walking and bicycling for both recreational and transportation purposes. It serves as a basis to develop valid and efficient tools to create activity-friendly communities.

Data Sources.

Keyword searches identified journal articles from the computer-based Academic Citation Databases, including the National Transportation Library, the Web of Science Citation Database, and MEDLINE. Governmental publications and conference proceedings were also searched.

Study Inclusion and Exclusion Criteria.

All instruments to audit physical environments have been included in this review, considering both recreation- and transportation-related walking and bicycling. Excluded are general methods devised to estimate walking and cycling trips, those used in empirical studies on land use and transportation, and research on walking inside buildings.

Data Extraction Methods.

Data have been extracted from each instrument using a template of key items developed for this review. The data were examined for quality assurance among three experienced researchers.

Data Synthesis.

A behavioral model of the built environment guides the synthesis according to three components: the origin and destination of the walk or bike trip, the characteristics of the road traveled, and the characteristics of the areas surrounding the trip's origin and destination. These components, combined with the characteristics of the instruments themselves, lead to a classification of the instruments into the four categories of inventory, route quality assessment, area quality assessment, and approaches to estimating latent demand for walking and bicycling. Furthermore, individual variables used in each instrument to measure the environment are grouped into four classes: spatiophysical, spatiobehavioral, spatiopsychosocial, and policy-based.

Major Conclusions.

Individually, existing instruments rely on selective classes of variables and therefore assess only parts of built environments that affect walking and bicycling. Most of the instruments and individual measures have not been rigorously tested because of a lack of available data on walking and bicycling and because of limited research budgets. Future instrument development will depend on the acquisition of empirical data on walking and bicycling, on inclusion of all three components of the behavioral model, and on consideration of all classes of variables identified.

The impact of area residential property values on self-rated health: A cross-sectional comparative study of Seattle and Paris

This study analyzed the impact of area residential property values, an objective measure of socioeconomic status (SES), on self-rated health (SRH) in Seattle, Washington and Paris, France. This study brings forth a valuable comparison of SRH between cities that have contrasting urban forms, population compositions, residential segregation, food systems and transportation modes. The SOS (Seattle Obesity Study) was based on a representative sample of 1394 adult residents of Seattle and King County in the United States. The RECORD Study (Residential Environment and Coronary Heart Disease) was based on 7131 adult residents of Paris and its suburbs in France. Socio-demographics, SRH and body weights were obtained from telephone surveys (SOS) and in-person interviews (RECORD). All home addresses were geocoded using ArcGIS 9.3.1 (ESRI, Redlands, CA). Residential property values were obtained from tax records (Seattle) and from real estate sales (Paris). Binary logistic regression models were used to test the associations among demographic and SES variables and SRH. Higher area property values significantly associated with better SRH, adjusting for age, gender, individual education, incomes, and BMI. The associations were significant for both cities. A one-unit increase in body mass index (BMI) was more detrimental to SRH in Seattle than in Paris. In both cities, higher area residential property values were related to a significantly lower obesity risk and better SRH. Ranked residential property values can be useful for health and weight studies, including those involving social inequalities and cross-country comparisons.

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We studied the impact of area property values on health in Seattle and Paris.

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Higher area property values associated with better SRH in both cities

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Ranked area property values can be useful for health and weight studies.

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BMI was more detrimental to SRH in Seattle than in Paris.

Self-reported and measured weights and heights among adults in Seattle and King County

Background

Self-reported weights and heights can be subject to gender, socio-economic, and other biases. On the other hand, obtaining measured anthropometric data can pose a significant respondent burden.

Methods

Seattle Obesity Study II (SOS II) participants (n = 419) provided self-reported height, weight, and demographic data through an interviewer-assisted behavior survey. Participants were then weighed and measured by trained staff. The entire process was repeated 12 months later. At the follow up visit, participants were also asked to recall their weight from 12 months ago. The concordance between measured and self-reported data was assessed using Bland-Altman plots.

Results

Some weight underreporting by obese individuals was observed. Gender or socio-economic status (SES) did not affect self-reports. Bland-Altman plots provided 95 % limits of agreement of −3.13 to 5.83 for weight (kg), and 1.21 to 2.52 for BMI (kg/m²). The concordance between measured and self-reported BMI categories was excellent (Kappa = 0.82 for men, and 0.86 for women). At the follow up visit, participants estimated their weight 12 months ago more accurately than their current weight.

Conclusions

Self-reported heights and weights were highly correlated with objective measures at two points in time. No gender or SES biases were observed. Minor, yet statistically significant under-reporting (<1.5 kg) was observed for obese participants. Caution should be used when using self-reported data in obese populations.

Electronic supplementary material

The online version of this article (doi:10.1186/s40608-016-0088-2) contains supplementary material, which is available to authorized users.

The association between park visitation and physical activity measured with accelerometer, GPS, and travel diary

Public parks are promoted as places that support physical activity (PA), but evidence of how park visitation contributes to overall PA is limited. This study observed adults living in the Seattle metropolitan area (n=671) for one week using accelerometer, GPS, and travel diary. Park visits, measured both objectively (GPS) and subjectively (travel diary), were temporally linked to accelerometer-measured PA. Park visits occurred at 1.4 per person-week. Participants who visited parks at least once (n=308) had an adjusted average of 14.3 (95% CI: 8.9, 19.6)min more daily PA than participants who did not visit a park. Even when park-related activity was excluded, park visitors still obtained more minutes of daily PA than non-visitors. Park visitation contributes to a more active lifestyle, but is not solely responsible for it. Parks may best serve to complement broader public health efforts to encourage PA.

Geographic disparities in Healthy Eating Index scores (HEI-2005 and 2010) by residential property values: Findings from Seattle Obesity Study (SOS)

BACKGROUND

Higher socioeconomic status (SES) has been linked with higher-quality diets. New GIS methods allow for geographic mapping of diet quality at a very granular level.

OBJECTIVE

To examine the geographic distribution of two measures of diet quality: Healthy Eating Index (HEI 2005 and HEI 2010) in relation to residential property values in Seattle-King County.

METHODS

The Seattle Obesity Study (SOS) collected data from a population-based sample of King County adults in 2008-09. Socio-demographic data were obtained by 20-min telephone survey. Dietary data were obtained from food frequency questionnaires (FFQs). Home addresses were geocoded to the tax parcel and residential property values were obtained from the King County tax assessor. Multivariable regression analyses using 1116 adults tested associations between SES variables and diet quality measured (HEI scores).

RESULTS

Residential property values, education, and incomes were associated with higher HEI scores in bivariate analyses. Property values were not collinear with either education or income. In adjusted multivariable models, education and residential property were better associated with HEI, compared to than income. Mapping of HEI-2005 and HEI-2010 at the census block level illustrated the geographic distribution of diet quality across Seattle-King County.

CONCLUSION

The use of residential property values, an objective measure of SES, allowed for the first visual exploration of diet quality at high spatial resolution: the census block level.

Health Implications of Adults' Eating at and Living near Fast Food or Quick Service Restaurants

Background:

This paper examined whether the reported health impacts of frequent eating at a fast food or quick service restaurant on health were related to having such a restaurant near home.

Methods:

Logistic regressions estimated associations between frequent fast food or quick service restaurant use and health status, being overweight or obese, having a cardiovascular disease or diabetes, as binary health outcomes. In all, 2001 participants in the 2008–2009 Seattle Obesity Study survey were included in the analyses.

Results:

Results showed eating ⩾2 times a week at a fast food or quick service restaurant was associated with perceived poor health status, overweight and obese. However, living close to such restaurants was not related to negative health outcomes.

Conclusions:

Frequent eating at a fast food or quick service restaurant was associated with perceived poor health status and higher body mass index, but living close to such facilities was not.

Multistate evaluation of safe routes to school programs

PURPOSE

State Safe Routes to School (SRTS) programs provide competitive grants to local projects that support safe walking, bicycling, and other modes of active school travel (AST). This study assessed changes in rates of AST after implementation of SRTS projects at multiple sites across four states.

DESIGN

One-group pretest and posttest.

SETTING

Florida, Mississippi, Washington, and Wisconsin.

SUBJECTS

Convenience sample of 48 completed SRTS projects and 53 schools affected by a completed SRTS project.

INTERVENTION

State-funded SRTS project.

MEASURES

AST was measured as the percentage of students walking, bicycling, or using any AST mode. SRTS project characteristics were measured at the project, school, and school neighborhood levels.

ANALYSIS

Paired-samples t-tests were used to assess changes in AST. Bivariate analysis was used to identify SRTS project characteristics associated with increases in AST. Data were analyzed separately at the project (n = 48) and school (n = 53) levels.

RESULTS

Statistically significant increases in AST were observed across projects in all four states. All AST modes increased from 12.9% to 17.6%; walking from 9.8% to 14.2%; and bicycling from 2.5% to 3.0%. Increases in rates of bicycling were negatively correlated with baseline rates of bicycling.

CONCLUSION

State-funded SRTS projects are achieving one of the primary program goals of increasing rates of AST. They may be particularly effective at introducing bicycling to communities where it is rare. The evaluation framework introduced in this study can be used to continue tracking the effect of state SRTS programs as more projects are completed.

Residential property values predict prevalent obesity but do not predict 1-year weight change

OBJECTIVE

Lower socio economic status (SES) has been linked with higher obesity rates but not with weight gain. This study examined whether SES can predict short-term weight change.

METHODS

The Seattle Obesity Study II was based on an observational cohort of 440 adults. Weights and heights were measured at baseline and at 1 year. Self-reported education and incomes were obtained by questionnaire. Home addresses were linked to tax parcel property values from the King County, Washington, tax assessor. Associations among SES variables, prevalent obesity, and 1-year weight change were examined using multivariable linear regressions.

RESULTS

Low residential property values at the tax parcel level predicted prevalent obesity at baseline and at 1 year. Living in the top quartile of house prices reduced obesity risk by 80% at both time points. At 1 year, about 38% of the sample lost >1 kg body weight; 32% maintained (± 1 kg); and 30% gained >1 kg. In adjusted models, none of the baseline SES measures had any impact on 1-year weight change.

CONCLUSIONS

SES variables, including tax parcel property values, predicted prevalent obesity but did not predict short-term weight change. These findings, based on longitudinal cohort data, suggest other mechanisms are involved in short-term weight change.

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.

The built environment and utilitarian walking in small U.S. towns

OBJECTIVES

The role of the built environment on walking in rural United States (U.S.) locations is not well characterized. We examined self-reported and measured built environment correlates of walking for utilitarian purposes among adult residents of small rural towns.

METHODS

In 2011-12, we collected telephone survey and geographic data from 2152 adults in 9 small towns from three U.S. regions. We performed mixed-effects logistic regression modeling to examine relationships between built environment measures and utilitarian walking ("any" versus "none"; "high" [≥150min per week] versus "low" [<150min per week]) to retail, employment and public transit destinations.

RESULTS

Walking levels were lower than those reported for populations living in larger metropolitan areas. Environmental factors significantly (p<0.05) associated with higher odds of utilitarian walking in both models included self-reported presence of crosswalks and pedestrian signals and availability of park/natural recreational areas in the neighborhood, and also objectively measured manufacturing land use.

CONCLUSIONS

Environmental factors associated with utilitarian walking in cities and suburbs were important in small rural towns. Moreover, manufacturing land use was associated with utilitarian walking. Modifying the built environment of small towns could lead to increased walking in a sizeable segment of the U.S. population.

The spatial clustering of obesity: does the built environment matter?

BACKGROUND

Obesity rates in the USA show distinct geographical patterns. The present study used spatial cluster detection methods and individual-level data to locate obesity clusters and to analyse them in relation to the neighbourhood built environment.

METHODS

The 2008-2009 Seattle Obesity Study provided data on the self-reported height, weight, and sociodemographic characteristics of 1602 King County adults. Home addresses were geocoded. Clusters of high or low body mass index were identified using Anselin's Local Moran's I and a spatial scan statistic with regression models that searched for unmeasured neighbourhood-level factors from residuals, adjusting for measured individual-level covariates. Spatially continuous values of objectively measured features of the local neighbourhood built environment (SmartMaps) were constructed for seven variables obtained from tax rolls and commercial databases.

RESULTS

Both the Local Moran's I and a spatial scan statistic identified similar spatial concentrations of obesity. High and low obesity clusters were attenuated after adjusting for age, gender, race, education and income, and they disappeared once neighbourhood residential property values and residential density were included in the model.

CONCLUSIONS

Using individual-level data to detect obesity clusters with two cluster detection methods, the present study showed that the spatial concentration of obesity was wholly explained by neighbourhood composition and socioeconomic characteristics. These characteristics may serve to more precisely locate obesity prevention and intervention programmes.

Relation between higher physical activity and public transit use

OBJECTIVES

We isolated physical activity attributable to transit use to examine issues of substitution between types of physical activity and potential confounding of transit-related walking with other walking.

METHODS

Physical activity and transit use data were collected in 2008 to 2009 from 693 Travel Assessment and Community study participants from King County, Washington, equipped with an accelerometer, a portable Global Positioning System, and a 7-day travel log. Physical activity was classified into transit- and non-transit-related walking and nonwalking time. Analyses compared physical activity by type between transit users and nonusers, between less and more frequent transit users, and between transit and nontransit days for transit users.

RESULTS

Transit users had more daily overall physical activity and more total walking than did nontransit users but did not differ on either non-transit-related walking or nonwalking physical activity. Most frequent transit users had more walking time than least frequent transit users. Higher physical activity levels for transit users were observed only on transit days, with 14.6 minutes (12.4 minutes when adjusted for demographics) of daily physical activity directly linked with transit use.

CONCLUSIONS

Because transit use was directly related to higher physical activity, future research should examine whether substantive increases in transit access and use lead to more physical activity and related health improvements.

Access to supermarkets and fruit and vegetable consumption

OBJECTIVES

We examined whether supermarket choice, conceptualized as a proxy for underlying personal factors, would better predict access to supermarkets and fruit and vegetable consumption than mere physical proximity.

METHODS

The Seattle Obesity Study geocoded respondents' home addresses and locations of their primary supermarkets. Primary supermarkets were stratified into low, medium, and high cost according to the market basket cost of 100 foods. Data on fruit and vegetable consumption were obtained during telephone surveys. Linear regressions examined associations between physical proximity to primary supermarkets, supermarket choice, and fruit and vegetable consumption. Descriptive analyses examined whether supermarket choice outweighed physical proximity among lower-income and vulnerable groups.

RESULTS

Only one third of the respondents shopped at their nearest supermarket for their primary food supply. Those who shopped at low-cost supermarkets were more likely to travel beyond their nearest supermarket. Fruit and vegetable consumption was not associated with physical distance but, with supermarket choice, after adjusting for covariates.

CONCLUSIONS

Mere physical distance may not be the most salient variable to reflect access to supermarkets, particularly among those who shop by car. Studies on food environments need to focus beyond neighborhood geographic boundaries to capture actual food shopping behaviors.

Food environment and socioeconomic status influence obesity rates in Seattle and in Paris

OBJECTIVE

To compare the associations between food environment at the individual level, socioeconomic status (SES) and obesity rates in two cities: Seattle and Paris.

METHODS

Analyses of the SOS (Seattle Obesity Study) were based on a representative sample of 1340 adults in metropolitan Seattle and King County. The RECORD (Residential Environment and Coronary Heart Disease) cohort analyses were based on 7131 adults in central Paris and suburbs. Data on sociodemographics, health and weight were obtained from a telephone survey (SOS) and from in-person interviews (RECORD). Both studies collected data on and geocoded home addresses and food shopping locations. Both studies calculated GIS (Geographic Information System) network distances between home and the supermarket that study respondents listed as their primary food source. Supermarkets were further stratified into three categories by price. Modified Poisson regression models were used to test the associations among food environment variables, SES and obesity.

RESULTS

Physical distance to supermarkets was unrelated to obesity risk. By contrast, lower education and incomes, lower surrounding property values and shopping at lower-cost stores were consistently associated with higher obesity risk.

CONCLUSION

Lower SES was linked to higher obesity risk in both Paris and Seattle, despite differences in urban form, the food environments and in the respective systems of health care. Cross-country comparisons can provide new insights into the social determinants of weight and health.

Emerging technologies for assessing physical activity behaviors in space and time

Precise measurement of physical activity is important for health research, providing a better understanding of activity location, type, duration, and intensity. This article describes a novel suite of tools to measure and analyze physical activity behaviors in spatial epidemiology research. We use individual-level, high-resolution, objective data collected in a space-time framework to investigate built and social environment influences on activity. First, we collect data with accelerometers, global positioning system units, and smartphone-based digital travel and photo diaries to overcome many limitations inherent in self-reported data. Behaviors are measured continuously over the full spectrum of environmental exposures in daily life, instead of focusing exclusively on the home neighborhood. Second, data streams are integrated using common timestamps into a single data structure, the "LifeLog." A graphic interface tool, "LifeLog View," enables simultaneous visualization of all LifeLog data streams. Finally, we use geographic information system SmartMap rasters to measure spatially continuous environmental variables to capture exposures at the same spatial and temporal scale as in the LifeLog. These technologies enable precise measurement of behaviors in their spatial and temporal settings but also generate very large datasets; we discuss current limitations and promising methods for processing and analyzing such large datasets. Finally, we provide applications of these methods in spatially oriented research, including a natural experiment to evaluate the effects of new transportation infrastructure on activity levels, and a study of neighborhood environmental effects on activity using twins as quasi-causal controls to overcome self-selection and reverse causation problems. In summary, the integrative characteristics of large datasets contained in LifeLogs and SmartMaps hold great promise for advancing spatial epidemiologic research to promote healthy behaviors.

Does neighborhood walkability moderate the effects of intrapersonal characteristics on amount of walking in post-menopausal women?

This study identifies factors associated with walking among postmenopausal women and tests whether neighborhood walkability moderates the influence of intrapersonal factors on walking. We used data from the Women's Health Initiative Seattle Center and linear regression models to estimate associations and interactions. Being white and healthy, having a high school education or beyond and greater non-walking exercise were significantly associated with more walking. Neighborhood walkability was not independently associated with greater walking, nor did it moderate influence of intrapersonal factors on walking. Specifying types of walking (e.g., for transportation) can elucidate the relationships among intrapersonal factors, the built environment, and walking.

Home versus nonhome neighborhood: quantifying differences in exposure to the built environment

BACKGROUND

Built environment and health research have focused on characteristics of home neighborhoods, whereas overall environmental exposures occur over larger spatial ranges.

PURPOSE

Differences in built environment characteristics were analyzed for home and nonhome locations using GPS data.

METHODS

GPS data collected in 2007-2008 were analyzed for 41 subjects in the Seattle area in 2010. Environmental characteristics for 3.8 million locations were measured using novel GIS data sets called SmartMaps, representing spatially continuous values of local built environment variables in the domains of neighborhood composition, utilitarian destinations, transportation infrastructure, and traffic conditions. Using bootstrap sampling, CIs were estimated for differences in built environment values for home (<833 m of home address) and nonhome (>1666 m) GPS locations.

RESULTS

Home and nonhome built environment values were significantly different for more than 90% of variables across subjects (p<0.001). Only 51% of subjects had higher counts of supermarkets near than away from home. Different measures of neighborhood parks yielded varying results.

CONCLUSIONS

SmartMaps helped measure local built environment characteristics for a large set of GPS locations. Most subjects had significantly different home and nonhome built environment exposures. Considering the full range of individuals' environmental exposures may improve understanding of effects of the built environment on behavior and health outcomes.