Background: Although previous research has highlighted the association between the built environment and individual health, methodological challenges in assessing the built environment remain. In particular, many researchers have demonstrated the high inter-rater reliability of assessing large or objective built environment features and the low inter-rater reliability of assessing small or subjective built environment features using Google Street View. New methods for auditing the built environment must be evaluated to understand if there are alternative tools through which researchers can assess all types of built environment features with high agreement. This paper investigates measures of inter-rater reliability of GigaPan ® , a tool that assists with capturing high-definition panoramic images, relative to Google Street View. Methods: Street segments (n = 614) in Pittsburgh, Pennsylvania in the United States were randomly selected to audit using GigaPan ® and Google Street View. Each audit assessed features related to land use, traffic and safety, and public amenities. Inter-rater reliability statistics, including percent agreement, Cohen's kappa, and the prevalence-adjusted bias-adjusted kappa (PABAK) were calculated for 106 street segments that were coded by two, different, human auditors. Results: Most large-scale, objective features (e.g. bus stop presence or stop sign presence) demonstrated at least substantial inter-rater reliability for both methods, but significant differences emerged across finely detailed features (e.g. trash) and features at segment endpoints (e.g. sidewalk continuity). After adjusting for the effects of bias and prevalence, the inter-rater reliability estimates were consistently higher for almost all built environment features across GigaPan ® and Google Street View. Conclusion: GigaPan ® is a reliable, alternative audit tool to Google Street View for studying the built environment. GigaPan ® may be particularly well-suited for built environment projects with study settings in areas where Google Street View imagery is nonexistent or updated infrequently. The potential for enhanced, detailed imagery using Giga-Pan ® will be most beneficial in studies in which current, time sensitive data are needed or microscale built environment features would be challenging to see in Google Street View. Furthermore, to better understand the effects of

The purpose of this study is to identify statistically distinguishable trajectories of childhood body mass index (BMI), an important indicator of developmental status of children, and to provide a summary description of demographic characteristics of children based on these distinctive trajectories. Using data from the Healthy Communities Study (HCS), a large longitudinal dataset with oversamples of Hispanic and Black children across 130 communities in the USA, a group-based trajectory analysis approach was used to estimate trajectories of children based on their BMI-z scores. The three most distinguishable BMI trajectory groups identified for the HCS children show no marked increase or decrease in standardized BMI over an age range of 2 to 11. Approximately 28.5% of children were in a trajectory group with consistently obese BMI-z scores for their sex and age. The patterns of BMI trajectory groups identified for boys and girls are similar, but BMI-z scores for boys tend to be slightly higher than those for girls. These BMI trajectories are characterized by racial/ethnic and socioeconomic status disparities. Hispanic and Black children were more likely to be in the obese trajectory group than White children. Children with parents having less education, or children from low family income level, were more likely to be in the obese trajectory group than counterpart children. The findings suggest that BMI disparities exist from the early years of childhood and persist across childhood, with higher BMI associated with Black and Hispanic children as well as those from low socioeconomic status backgrounds.

Background Studies have shown neighborhood walkability is associated with obesity. To advance this research, study designs involving longer follow-up, broader geographic regions, appropriate neighborhood characterization, assessment of exposure length and severity, and consideration of stayers and movers are needed. Using a cohort spanning the conterminous United States, this study examines the longitudinal relationship between a network buffer-derived, duration-weighted neighborhood walkability measure and two adiposity-related outcomes. Methods This study included 12,846 Black/African American and White adults in the REasons for Geographic And Racial Differences in Stroke study. Body mass index (BMI) and waist circumference (WC) were assessed at baseline and up to 13.3 years later (M (SD) = 9.4 (1.0) years). BMI and WC were dichotomized. Walk Score® was duration-weighted based on time at each address and categorized as Very Car-Dependent, Car-Dependent, Somewhat Walkable, Very Walkable, and Walker’s Paradise. Unadjusted and adjusted logistic regression models tested each neighborhood walkability-adiposity association. Adjusted models controlled for demographics, health factors, neighborhood socioeconomic status, follow-up time, and either baseline BMI or baseline WC. Adjusted models also tested for interactions. Post-estimation Wald tests examined whether categorical variables had coefficients jointly equal to zero. Orthogonal polynomial contrasts tested for a linear trend in the neighborhood walkability-adiposity relationships. Results The odds of being overweight/obese at follow-up were lower for residents with duration-weighted Walk Score® values in the Walker’s Paradise range and residents with values in the Very Walkable range compared to residents with values in the Very Car-Dependent range. Residents with duration-weighted Walk Score® values classified as Very Walkable had significantly lower odds of having a moderate-to-high risk WC at follow-up relative to those in the Very Car-Dependent range. For both outcomes, the effects were small but meaningful. The negative linear trend was significant for BMI but not WC. Conclusion People with cumulative neighborhood walkability scores in the Walker’s Paradise range were less likely to be overweight/obese independent of other factors, while people with scores in the Very Walkable range were less likely to be overweight/obese and less likely to have a moderate-to-high risk WC. Addressing neighborhood walkability is one approach to combating obesity.