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Neighborhood walkability-features of the built environment that promote pedestrian activity-has been associated with greater physical activity and lower body mass index (BMI; calculated as weight (kg)/height (m)2) among neighborhood residents. However, much of the literature has been cross-sectional and only a few cohort studies have assessed neighborhood features throughout follow-up. Using data from the Reasons for Geographic and Racial Differences in Stroke Study (2003-2016) and a neighborhood walkability index (NWI) measured annually during follow-up, we assessed whether the cumulative experience of neighborhood walkability (NWI-years) predicted BMI and waist circumference after approximately 10 years of follow-up, controlling for these anthropometric measures at enrollment. Analyses were adjusted for individual-level sociodemographic covariates and the cumulative experience of neighborhood poverty rate and neighborhood greenspace coverage. Almost a third (29%) of participants changed address at least once during follow-up. The first change of residence, on average, brought the participants to neighborhoods with higher home values and lower NWI scores than their originating neighborhoods. Compared with those having experienced the lowest quartile of cumulative NWI-years, those who experienced the highest quartile had 0.83 lower BMI (95% confidence interval, -1.5, -0.16) and 1.07-cm smaller waist circumference (95% confidence interval, -1.96, -0.19) at follow-up. These analyses provide additional longitudinal evidence that residential neighborhood features that support pedestrian activity are associated with lower adiposity., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.)

Background: Despite the links between neighbourhood walkability and physical activity, body size and risk of diabetes, there are few studies of neighbourhood walkability and risk of gestational diabetes (GD)., Objectives: Assess whether higher neighbourhood walkability is associated with lower risk of GD in New York City (NYC)., Methods: Cross-sectional analyses of a neighbourhood walkability index (NWI) score and density of walkable destinations (DWD) and risk of GD in 109,863 births recorded in NYC in 2015. NWI and DWD were measured for the land area of 1 km radius circles around the geographic centroid of each Census block of residence. Mixed generalised linear models, with robust standard error estimation and random intercepts for NYC Community Districts, were used to estimate risk ratios for GD for increasing quartiles of each of the neighbourhood walkability measures after adjustment for the pregnant individual's age, race and ethnicity, parity, education, nativity, and marital status and the neighbourhood poverty rate., Results: Overall, 7.5% of pregnant individuals experienced GD. Risk of GD decreased across increasing quartiles of NWI, with an adjusted risk ratio of 0.81 (95% Confidence Interval (CI) 0.75, 0.87) comparing those living in areas in the 4th quartile of NWI to those in the first quartile. Similarly, for comparisons of the 4th to 1st quartile of DWD, the adjusted risk ratio for GD was 0.77 (95% CI 0.71, 0.84)., Conclusions: These analyses find support for the hypothesis that higher neighbourhood walkability is associated with a lower risk of GD. The analyses provide further health related support for urban design policies to increase walkability., (© 2023 John Wiley & Sons Ltd.)

To examine how urban form shapes physical activity and health over time, a measure of neighborhood walkability is needed that can be linked to cohort studies with participants living across the United States (U.S.) that have been followed over the past decades. The Built Environment and Health-Neighborhood Walkability Index (BEH-NWI), a measure of neighborhood walkability that can be calculated for communities across the United States between 1990 and 2015, was conceptualized, developed, and tested using data from the New York City Tri-State Area. BEH-NWI measures were created for 1990 and 2010 using historical data on population density, street intersection density, density of rail stops, and density of pedestrian trip generating/supporting establishments. BEH-NWI scores were calculated for 1-km buffers around the 1990 residences of NYU Women's Health Study (NYUWHS) participants and NYC Department of Health and Mental Hygiene's Physical Activity and Transit (PAT) survey participants enrolled in 2011. Higher neighborhood BEH-NWI scores were significantly associated with greater self-reported walking per week (+ 0.31 MET-hours/week per unit BEH-NWI, 95% CI 0.23, 0.36) and lower body mass index (- 0.17 BMI units per unit BEH-NWI, 95% - 0.23, - 0.12) among NYUWHS participants. Higher neighborhood BEH-NWI scores were associated with significantly higher accelerometer-measured physical activity among PAT survey participants (39% more minutes of moderate-intensity equivalent activity/week across the interquartile range of BEH-NWI, 95% CI 21%, 60%). The BEH-NWI can be calculated using historical data going back to 1990, and BEH-NWI scores predict BMI, weekly walking, and physical activity in two NYC area datasets.

Objectives: To investigate accuracy of distance measures computed from Global Positioning System (GPS) points in New York City., Methods: We performed structured walks along urban streets carrying Globalsat DG-100 GPS Data Logger devices in highest and lowest quartiles of building height and tree canopy cover. We used ArcGIS version 10.1 to select walks and compute the straight-line distance (Geographic Information System-measured) and sum of distances between consecutive GPS waypoints (GPS-measured) for each walk., Results: GPS distance overestimates were associated with building height (median overestimate = 97% for high vs 14% for low building height) and to a lesser extent tree canopy (43% for high vs 28% for low tree canopy)., Conclusions: Algorithms using distances between successive GPS points to infer speed or travel mode may misclassify trips differentially by context. Researchers studying urban spaces may prefer alternative mode identification techniques.

Introduction: Urban form characteristics intended to support pedestrian activity, collectively referred to as neighborhood walkability, are thought to increase total physical activity. However, little is known about how neighborhood walkability influences utilization of neighborhood space by residents and their overall physical activity., Methods: Sociodemographic information and data on mobility and physical activity over 1-week periods measured by GPS loggers and accelerometers were collected from 803 residents of New York City between November 2010 and November 2011. Potentially accessible neighborhood areas were defined as land area within a 1-kilometer distance of the subject's home (radial buffer) and within a 1-kilometer journey on the street network from the home (network buffer). To define actual areas utilized by subjects, a minimum convex polygon was plotted around GPS waypoints falling within 1 kilometer of the home. A neighborhood walkability scale was calculated for each neighborhood area. Data were analyzed in 2014., Results: Total residential neighborhood space utilized by subjects was significantly associated with street intersection density and was significantly negatively associated with residential density and subway stop density within 1 kilometer of the home. Walkability scale scores were significantly higher within utilized as compared with non-utilized neighborhood areas. Neighborhood walkability in the utilized neighborhood area was positively associated with total weekly physical activity (32% [95% CI=17%, 49%] more minutes of moderate-equivalent physical activity across the interquartile range of walkability)., Conclusion: Neighborhood walkability is associated with neighborhood spaces utilized by residents and total weekly physical activity., (Copyright © 2016 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.)

Neighborhood built environment characteristics may encourage physical activity, but previous literature on the topic has been critiqued for its reliance on cross-sectional data. In this issue of the Journal, Knuiman et al. (Am J Epidemiol. 2014;180(5):453-461) present longitudinal analyses of built environment characteristics as predictors of neighborhood transportation walking. We take this opportunity to comment on self-selection, exposure measurement, outcome form, analyses, and future directions. The Residential Environments (RESIDE) Study follows individuals as they relocate into new housing. The outcome, which is neighborhood transportation walking, has several important limitations with regards to public health relevance, dichotomization, and potential bias. Three estimation strategies were pursued: marginal modeling, random-effects modeling, and fixed-effects modeling. Knuiman et al. defend fixed-effects modeling as the one that most effectively controls for unmeasured time-invariant confounders, and it will do so as long as confounders have a constant effect over time. Fixed-effects modeling requires no distributional assumptions regarding the heterogeneity of subject-specific effects. Associations of time-varying neighborhood characteristics with walking are interpreted at the subject level for both fixed- and random-effects models. Cross-sectional data have set the stage for the next generation of neighborhood research, which should leverage longitudinal changes in both place and health behaviors. Careful interpretation is warranted as longitudinal data become available for analysis., (© The Author 2014. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Objective: To evaluate the association between adult individuals' body mass index (BMI) and characteristics of parks (size and cleanliness) in an urban environment taking into account the physical and social environments of the neighborhood., Methods: Cross-sectional, hierarchical linear models were used to determine whether park effects were associated with BMI using self-reported height and weight data obtained from the Community Health Survey in New York City (2002-2006)., Results: Both the proportion of the residential zip code that was large park space and the proportion that was small park space had significant inverse associations with BMI after controlling for individual socio-demographic and zip code built environment characteristics (-0.20 BMI units across the inter-quartile range (IQR) for large parks, 95% CI -0.32, -0.08; -0.21 BMI units across the IQR for small parks, 95% CI -0.31, -0.10, respectively). Poorer scores on the park cleanliness index were associated with higher BMI, 0.18 BMI units across the IQR of the park cleanliness index (95% CI 0.05, 0.30)., Conclusions: This study demonstrated that proportion of neighborhoods that was large or small park space and park cleanliness were associated with lower BMI among NYC adults after adjusting for other neighborhood features such as homicides and walkability, characteristics that could influence park usage., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Background: One strategy to address health problems related to insufficient physical activity is to examine modifiable neighborhood characteristics associated with active transportation., Purpose: The aim of this study is to evaluate whether neighborhoods with more aesthetic amenities (sidewalk cafés, street trees, and clean sidewalks) and fewer safety hazards (pedestrian-auto fatalities and homicides) are associated with active transportation., Methods: The 2003 Community Health Survey in New York City, which asked about active transportation (walking or bicycling >10 blocks) in the past 30 days, was linked to ZIP-code population census and built environment characteristics. Adjusted associations were estimated for dichotomous (any active transportation versus none) and continuous (trip frequency) active transportation outcomes., Results: Among 8,034 adults, those living near sidewalk cafés were 10 % more likely to report active transportation (p = 0.01). Homicide rate was associated with less frequent active transportation among those reporting any active transportation (p = 0.002)., Conclusions: Investments in aesthetic amenities or homicide prevention may help to promote active transportation.

Background: Neighborhood attractiveness and safety may encourage physical activity and help individuals maintain a healthy weight. However, these neighborhood characteristics may not be equally relevant to health across all settings and population subgroups., Purpose: To evaluate whether potentially attractive neighborhood features are associated with lower BMI, whether safety hazards are associated with higher BMI, and whether environment-environment interactions are present such that associations for a particular characteristic are stronger in an otherwise supportive environment., Methods: Survey data and measured height and weight were collected from a convenience sample of 13,102 adult New York City (NYC) residents in 2000-2002; data analyses were completed 2008-2012. Built-environment measures based on municipal GIS data sources were constructed within 1-km network buffers to assess walkable urban form (density, land-use mix, transit access); attractiveness (sidewalk cafés, landmark buildings, street trees, street cleanliness); and safety (homicide rate, pedestrian-auto collision and fatality rate). Generalized linear models with cluster-robust SEs controlled for individual and area-based sociodemographic characteristics., Results: The presence of sidewalk cafés, density of landmark buildings, and density of street trees were associated with lower BMI, whereas the proportion of streets rated as clean was associated with higher BMI. Interactions were observed for sidewalk cafés with neighborhood poverty, for street-tree density with walkability, and for street cleanliness with safety. Safety hazard indicators were not independently associated with BMI., Conclusions: Potentially attractive community and natural features were associated with lower BMI among adults in NYC, and there was some evidence of effect modification., (Copyright © 2012 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.)

Objectives: We sought to test whether the association between walkable environments and lower body mass index (BMI) was stronger within disadvantaged groups that may be particularly sensitive to environmental constraints., Methods: We measured height and weight in a diverse sample of 13 102 adults living throughout New York City from 2000-2002. Each participant's home address was geocoded and surrounded by a circular buffer with a 1-km radius. The composition and built environment characteristics of these areas were used to predict BMI through the use of generalized estimating equations. Indicators of individual or area disadvantage included low educational attainment, low household income, Black race, and Hispanic ethnicity., Results: Higher population density, more mixed land use, and greater transit access were most consistently associated with a lower BMI among those with more education or higher incomes and among non-Hispanic Whites. Significant interactions were observed for education, income, race, and ethnicity., Conclusions: Contrary to expectations, built environment characteristics were less consistently associated with BMI among disadvantaged groups. This pattern may be explained by other barriers to maintaining a healthy weight encountered by disadvantaged groups.

Background: Environments conducive to walking may help people avoid sedentary lifestyles and associated diseases. Recent studies developed walkability models combining several built environment characteristics to optimally predict walking. Developing and testing such models with the same data could lead to overestimating one's ability to predict walking in an independent sample of the population. More accurate estimates of model fit can be obtained by splitting a single study population into training and validation sets (holdout approach) or through developing and evaluating models in different populations. We used these two approaches to test whether built environment characteristics near the home predict walking for exercise. Study participants lived in western Washington State and were adult members of a health maintenance organization. The physical activity data used in this study were collected by telephone interview and were selected for their relevance to cardiovascular disease. In order to limit confounding by prior health conditions, the sample was restricted to participants in good self-reported health and without a documented history of cardiovascular disease., Results: For 1,608 participants meeting the inclusion criteria, the mean age was 64 years, 90 percent were white, 37 percent had a college degree, and 62 percent of participants reported that they walked for exercise. Single built environment characteristics, such as residential density or connectivity, did not significantly predict walking for exercise. Regression models using multiple built environment characteristics to predict walking were not successful at predicting walking for exercise in an independent population sample. In the validation set, none of the logistic models had a C-statistic confidence interval excluding the null value of 0.5, and none of the linear models explained more than one percent of the variance in time spent walking for exercise. We did not detect significant differences in walking for exercise among census areas or postal codes, which were used as proxies for neighborhoods., Conclusion: None of the built environment characteristics significantly predicted walking for exercise, nor did combinations of these characteristics predict walking for exercise when tested using a holdout approach. These results reflect a lack of neighborhood-level variation in walking for exercise for the population studied.