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

Background: Physical inactivity is a risk factor for cancer that may be influenced by environmental factors. Indeed, dense and well-connected built environments and environments with natural vegetation may create opportunities for higher routine physical activity. However, studies have focused primarily on residential environments to define exposure and self-reported methods to estimate physical activity. This study explores the momentary association between minute-level global positioning systems (GPS)-based greenness exposure and time-matched objectively measured physical activity.Methods: Adult women were recruited from sites across the United States. Participants wore a GPS device and accelerometer on the hip for 7 days to assess location and physical activity at minute-level epochs. GPS records were linked to 250 m resolution satellite-based vegetation data and Census Block Group-level U.S. Environmental Protection Agency (EPA) Smart Location Database walkability data. Minute-level generalized additive mixed models were conducted to test for associations between GPS measures and accelerometer count data, accounting for repeated measures within participant and allowing for deviations from linearity using splines.Results: Among 360 adult women (mean age of 55.3 ± 10.2 years), we observed positive nonlinear relationships between physical activity and both greenness and walkability. In exploratory analyses, the relationships between environmental factors and physical activity were strongest among those who were white, had higher incomes, and who were middle-aged.Conclusions: Our results indicate that higher levels of physical activity occurred in areas with higher greenness and higher walkability.Impact: Findings suggest that planning and design policies should focus on these environments to optimize opportunities for physical activity. Cancer Epidemiol Biomarkers Prev; 26(4); 525–32. ©2017 AACR.See all the articles in this CEBP Focus section, “Geospatial Approaches to Cancer Control and Population Sciences.”

Supplemental Figure 1. GPS and Physical Activity Data Overlaid on a. Greenness and b. Neighborhood Walkability. Supplemental Figure 2. Nonlinear Relationship between Activity and a. Greenness and b. Neighborhood Walkability in Dataset Excluding Outliers (>3 Standard Deviations from Mean Accelerometer Counts). Supplemental Figure 3. Greenness and Physical Activity Stratified by Race. Supplemental Figure 4. Greenness and Physical Activity Stratified by Educational Attainment. Supplemental Figure 5. Greenness and Physical Activity Stratified by Employment Status. Supplemental Figure 6. Greenness and Physical Activity Stratified by Annual Household Income. Supplemental Figure 7. Greenness and Physical Activity Stratified by Sampling. Season Supplemental Figure 8. Greenness and Physical Activity Stratified by Weekday / Weekend. Supplemental Figure 9. Walkability Index and Physical Activity Stratified by Race. Supplemental Figure 10. Walkability Index and Physical Activity Stratified by Educational Attainment. Supplemental Figure 11. Walkability Index and Physical Activity Stratified by Employment Status. Supplemental Figure 12. Walkability Index and Physical Activity Stratified by Annual Household Income. Supplemental Figure 13. Walkability Index and Physical Activity Stratified by Sampling Season. Supplemental Figure 14. Walkability Index and Physical Activity Stratified by Weekday / Weekend.

Unequal access to healthy food in the local food retail environment contributes to diet quality disparities. We assessed whether in-store availability and prices of healthy foods differ by neighborhood-level income and racial and ethnic composition in a representative sample of food stores in Seattle, Washington.We developed and validated an in-store survey tool and surveyed 134 stores. We measured availability and prices of 19 items. For each store, we calculated a healthy food availability score (range, 0-25), and mean prices within each category. Using census tract data, we identified the median household income and proportions of Black and Hispanic residents for each store's neighborhood and grouped them by tertiles of these neighborhood characteristics across Seattle census tracts. We used Wald tests to compare mean availability scores and prices between tertiles and applied postestimation weights to reflect store-type distributions within each tertile.Neighborhoods with lower income and a larger proportion of Black residents had lower healthy food availability scores compared with neighborhoods with higher income (8.06 [95% CI, 7.04-9.07] vs 12.40 [95% CI, 10.63-14.17], P.001) and fewer Black residents (8.88 [95% CI, 7.79-9.98] vs 12.32 [95% CI, 10.51-14.14], P = .003). Availability did not differ by Hispanic population proportions. Mean prices of grains, eggs, and meat were lower in neighborhoods with larger proportions of Black residents.We found systematic differences in healthy food availability based on neighborhood-level income and racial composition. In-store assessments of the food retail environment can inform local, tailored strategies to improve healthy food access.

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

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-meter buffers. Counts of area supermarkets and fast food restaurants were obtained using network-based SmartMaps at 1,600, and 5,000-meter 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 kilograms at 1 year (95% CI: 0.03, 0.10); 0.64 kilograms at 3 years (95% CI: 0.59, 0.68), and 0.95 kilograms 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 kilograms. Conclusion: 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.

Background: Existing evidence indicates household income as a predictor of health-related quality of life (HRQoL) following a colorectal cancer diagnosis. This association likely varies with neighborhood socioeconomic status (nSES), but evidence is limited. Methods: We included data from 1,355 colorectal cancer survivors participating in the population-based Puget Sound Colorectal Cancer Cohort (PSCCC). Survivors reported current annual household income; we measured HRQoL via the Functional Assessment of Cancer Therapy - Colorectal (FACT-C) tool. Using neighborhood data summarized within a 1-km radial buffer of Census block group centroids, we constructed a multidimensional nSES index measure. We employed survivors' geocoded residential addresses to append nSES score for Census block group of residence. With linear generalized estimating equations clustered on survivor location, we evaluated associations of household income with differences in FACT-C mean score, overall and stratified by nSES. We used separate models to explore relationships for wellbeing subscales. Results: We found lower household income to be associated with clinically meaningful differences in overall FACT-C scores [ Conclusions: Our findings suggest that recently diagnosed lower income colorectal cancer survivors are likely to report lower HRQoL, and modestly more so in lower SES neighborhoods. Impact: The findings from this work will aid future investigators' ability to further consider the contexts in which the income of survivors can be leveraged as a means of improving HRQoL

PURPOSE To explore the usefulness of combining accelerometry, global positioning systems, and geographic information systems, to describe the time spent in different locations and physical activity (PA) duration/count levels by location for 4 children with cerebral palsy (CP) who use assistive devices (AD). METHODS A descriptive multiple-case study. RESULTS Combining the 3 instruments was useful in describing and differentiating duration by location, and amount and location of PA across differing functional levels and AD. For example, the child classified with a Gross Motor Function Classification System (GMFCS) level II exhibited large amounts of PA in community settings. In contrast, the child classified with a GMFCS level V had small amounts of PA and spent most measured time at home. CONCLUSIONS Combined accelerometry, global positioning system, and geographic information system have potential to capture time spent and amount/intensity of PA relative to locations within daily environments for children with CP who use AD.

Summary Background In‐person assessments of physical activity (PA) and body weight can be burdensome for participants and cost prohibitive for researchers. This study examined self‐reported PA and weight accuracy and identified patterns of misreporting in a diverse sample. Methods King, Pierce and Yakima county residents, aged 21–59 years (n = 728), self‐reported their moderate‐to‐vigorous PA (MVPA) and weight, in kilograms. Self‐reports were compared with minutes of bout‐level MVPA, from 3 days of accelerometer data, and measured weights. Regression models examined characteristics associated with underreporting and overreporting of MVPA and weight, the potential bias introduced using each measure and the relation between perceived and measured PA and weight. Results MVPA underreporting was higher among males and college educated participants; however, there was no differential MVPA overreporting. Weight underreporting was higher among males, those age 40–49 years and persons with obesity. Weight overreporting was higher among Hispanic participants and those reporting stress, unhappiness and fair or poor health. The estimated PA–obesity relation was similar using measured and self‐reported PA but not self‐reported weight. Perceived PA and weight predicted measured values. Conclusion Self‐reported PA and weight may be useful should objective measurement be infeasible; however, though population‐specific adjustment for differential reporting should be considered.

BACKGROUND: This study examined associations of local cannabis retail outlet availability and neighborhood disadvantage with cannabis use and related risk factors among young adults. METHODS: Data were from annual cross-sectional surveys administered from 2015 to 2019 to individuals ages 18 to 25 residing in Washington State (N = 10,009). As outcomes, this study assessed self-reported cannabis use at different margins/frequencies (any past year, at least monthly, at least weekly, at least daily) and perceived ease of access to cannabis and acceptability of cannabis use in the community. Cannabis retail outlet availability was defined as the presence of at least one retail outlet within a 1-kilometer road network buffer of one’s residence. Sensitivity analyses explored four other spatial metrics to define outlet availability (any outlet within 0.5-km, 2-km, and the census tract; and census tract density per 1000 residents). Census tract level disadvantage was a composite of five US census variables. RESULTS: Adjusting for individual- and area-level covariates, living within 1-kilometer of at least one cannabis retail outlet was statistically significantly associated with any past year and at least monthly cannabis use as well as high perceived access to cannabis. Results using a 2-km buffer and census tract-level metrics for retail outlet availability showed similar findings. Neighborhood disadvantage was statistically significantly associated with at least weekly and at least daily cannabis use and with greater perceived acceptability of cannabis use. CONCLUSIONS: Results may have implications for regulatory and prevention strategies to reduce the population burden of cannabis use and related harms.

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.

To explore the built environment (BE) and weight change relationship by age, sex, and racial/ethnic subgroups in adults.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.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 interaction0.001). These findings were consistent for other BE measures.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.

Background Epidemiological studies utilize residential histories to assess environmental exposure risk. The validity from using commercially-sourced residential histories within national longitudinal studies remains unclear. Our study assessed predictors of non-agreement between baseline addresses from the commercially-sourced LexisNexis database and participants in the national longitudinal study, REasons for Geographic and Racial Differences in Stroke (REGARDS). Additionally, we assessed differences in stroke risk by neighborhood socioeconomic score (nSES) based on participant reported address compared to nSES from LexisNexis/REGARDS matched baseline address. Methods From January 2003–October 2007, REGARDS enrolled 30,239 black and white adults aged 45 and older within the continental United States and collected their baseline address. ArcGIS Desktop 10.5.1 with ESRI 2016 Business Analyst Data was used to geocode baseline addresses from LexisNexis and REGARDS. Logistic regression was used to estimate the likelihood that LexisNexis address matched REGARDS baseline address for each participant. Survival analysis was used to estimate association between nSES and incident stroke. Results Approximately 91% of REGARDS participants had a LexisNexis address. Of these geocoded addresses, 93% of REGARDS baseline addresses matched LexisNexis addresses. Odds of agreement between LexisNexis and REGARDS was higher for older-aged participants (OR = 1.02 per year, 95% CI: 1.01, 1.02), blacks compared to whites (OR = 1.16, 95% CI: 1.05, 1.29), females compared to males (OR = 1.15, 95% CI: 1.04, 1.26), participants with an income of $34k-74k compared to an income less than $20k (OR = 1.62, 95% CI: 1.39, 1.89). Odds of agreement were lower for residents in Midwest compared to residents in the south (OR = 0.82, 95% CI: 0.73, 0.94). No significant differences in nSES-stroke associations were observed between REGARDS only and LexisNexis/REGARDS matched addresses; however, differences in interactions were observed. Conclusion Agreement between LexisNexis and REGARDS addresses varied by sociodemographic groups, potentially introducing bias in studies reliant on LexisNexis alone for residential address data., Highlights • Approximately 9% of REGARDS participants did not have a LexisNexis address history available. • Of participants with both REGARDS and LexisNexis addresses available, 93% of these addresses matched. • Agreement between REGARDS and LexisNexis address varied by socio-demographics—potentially biasing environmental exposures. • Compared to LexisNexis, REGARDS addresses may be valuable in detecting interactions contributing to stroke disparities.

Objectives: Aging in place (residential stability) is a desirable means of aging where adults remain in their homes, even when facing challenges that impair their capacity for self-care. Residential stability, especially following acute health challenges, depends on individual and community factors, possibly including proximity to medical facilities. Methods: We explored the association between the density of medical facilities around homes with risk of incident myocardial infarction (MI) and with aging in place following incident MI. Results: Densities of neighborhood pharmacies were not associated with aging in place or time to MI. High densities of neighborhood clinical care facilities were significantly associated with decreased residential stability. Discussion: The lack of significant associations between medical facility exposures and MI-related outcomes, coupled with prior findings, casts doubt on their salience and may indicate that other neighborhood features are more strongly associated with these outcomes.

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.

Seattle's Sweetened Beverage Tax is an excise tax of 1.75 cents per ounce on sugar-sweetened beverages and is one of the highest beverage taxes in the U.S. This study examined the impact of Seattle's tax on the prices of beverages. We conducted audits of 407 retail food stores and eating places (quick service restaurants and coffee shops) before and 6 months after the tax was implemented in Seattle and in a comparison area. Ordinary least squares difference-in-differences models with store fixed effects were used to estimate the effect of the tax on prices, stratified by beverage type and store type. In secondary analyses, we assessed the effect of the tax on the price of non-taxed beverages and foods. Results from the adjusted difference-in-differences models indicated the tax was associated with an average increase of 1.58 cents per ounce among Seattle retailers, representing 90 % of the price of the tax. By store type, price increases were highest in smaller grocery stores and drug stores. By beverage type, price increases were highest for energy beverages and soda and lowest for bottled coffee and juice drinks. Prices of some non-taxed beverages also increased while the prices of select healthy foods generally did not. The sweetened beverage tax in Seattle is higher than beverage taxes in most other cities, and nearly the full cost of the tax is being passed through to consumers for many beverage types and stores types.

Background Device-collected data from GPS and accelerometers for identifying active travel behaviors have dramatically changed research methods in transportation planning and public health. Automated algorithms have helped researchers to process large datasets with likely fewer errors than found in other collection methods (e.g., self-report travel diary). In this study, we compared travel modes identified by a commonly used automated algorithm (PALMS) that integrates GPS and accelerometer data with those obtained from travel diary estimates. Methods Sixty participants, who made 2100 trips during seven consecutive days of data collection, were selected from among the baseline sample of a project examining the travel behavior impact of a new light rail system in the greater Seattle, WA (USA) area. GPS point level analyses were first conducted to compare trip/place and travel mode detection results using contingency tables. Trip level analyses were then performed to investigate the effect of proportions of time overlap between travel logs and device-collected data on agreement rates. Global performance (with all subjects’ data combined) and subject-level performance of the algorithm were compared at the trip level. Results At the GPS point level, the overall agreement rate of travel mode detection was 77.4% between PALMS and the travel diary. The agreement rate for vehicular trip detection (84.5%) was higher than for bicycling (53.5%) and walking (58.2%). At the trip level, the global performance and subject-level performance of the PALMS algorithm were 46.4% and 42.4%, respectively. Vehicular trip detection showed highest agreement rates in all analyses. Study participants’ primary travel mode and car ownership were significantly related to the subject-level mode agreement rates. Conclusions The PALMS algorithm showed moderate identification power at the GPS point level. However, trip level analyses found lower agreement rates between PALMS and travel diary data, especially for active transportation. Testing different PALMS parameter settings may serve to improve the detection of active travel and help expand PALMS’s applicability in geographically different urbanized areas with a variety of travel modes.

BackgroundSafeBoda is a motorcycle taxi company that provides road safety training and helmets to its drivers in Kampala, Uganda. We sought to determine whether SafeBoda drivers are more likely to engage in safe riding behaviours than regular drivers (motorcycle taxi drivers not part of SafeBoda).Methods We measured riding behaviours in SafeBoda and regular drivers through: (1) computer-assisted personal interview (CAPI), where 400 drivers were asked about their riding behaviours (eg, helmet and mobile phone use) and (2) roadside observation, where riding behaviours were observed in 3000 boda-boda drivers and their passengers along major roads in Kampala.ResultsAcross the two cross-sectional studies, a higher proportion of SafeBoda drivers than regular drivers engaged in safe riding behaviours. For instance, helmet use among SafeBoda compared with regular drivers was 21% points higher (95% CI 0.15 to 0.27; pSafeBoda drivers were more likely to report having a driver’s license (66.3% vs 33.5 %; pConclusionThe SafeBoda programme is associated with increased safe riding behaviours among motorcycle taxi drivers in Kampala. Therefore, the promotion and expansion of such programmes may lead to a reduction in morbidity and mortality due to road injuries.

Background Medical establishments in the neighborhood, such as pharmacies and primary care clinics, may play a role in improving access to preventive care and treatment and could explain previously reported neighborhood variations in sudden cardiac arrest (SCA) incidence and survival. Methods The Cardiac Arrest Blood Study Repository is a population-based repository of data from adult cardiac arrest patients and population-based controls residing in King County, Washington. We examined the association between the availability of medical facilities near home with SCA risk, using adult (age 18–80) Seattle residents experiencing cardiac arrest (n = 446) and matched controls (n = 208) without a history of heart disease. We also analyzed the association of major medical centers near the event location with emergency medical service (EMS) response time and survival among adult cases (age 18+) presenting with ventricular fibrillation from throughout King County (n = 1537). The number of medical facilities per census tract was determined by geocoding business locations from the National Establishment Time-Series longitudinal database 1990–2010. Results More pharmacies in the home census tract was unexpectedly associated with higher odds of SCA (OR:1.28, 95% CI: 1.03, 1.59), and similar associations were observed for other medical facility types. The presence of a major medical center in the event census tract was associated with a faster EMS response time (−53 s, 95% CI: −84, −22), but not with short-term survival. Conclusions We did not observe a protective association between medical facilities in the home census tract and SCA risk, orbetween major medical centers in the event census tract and survival.

Walkability is a popular and ubiquitous term at the intersection of urban planning and public health. As the number of potential walkability measures grows in the literature, there is a need to compare their relative importance for specific research objectives. This study demonstrates a classification and regression tree (CART) model to compare five familiar measures of walkability from the literature for their relative ability to predict whether or not walking occurs in a dataset of objectively measured locations. When analyzed together, the measures had moderate-to-high accuracy (87.8% agreement: 65.6% of true walking GPS-measured points classified as walking and 93.4% of non-walking points as non-walking). On its own, the most well-known composite measure, Walk Score, performed only slightly better than measures of the built environment composed of a single variable (transit ridership, employment density, and residential density), suggesting there may be contexts where transparent and longitudinally available measures of urban form are worth a marginal tradeoff in prediction accuracy. This demonstration of CART for the comparison of walkability measures also highlights the importance for public health and urban design researchers to think carefully about how and why particular walkability measures are used.

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.

We determined associations of cumulative exposures to neighborhood physical activity opportunities with risk of incident cardiovascular disease (CVD). We included 3595 participants from the Cardiovascular Health Study recruited between 1989 and 1993 (mean age = 73; 60% women; 11% black). Neighborhood environment measures were calculated using Geographic Information Systems (GIS) and annual information from the National Establishment Time Series database, including the density of (1) walking destinations and (2) physical activity/recreational facilities in a 1- and 5-km radius around the respondent's home. Incident CVD was defined as the development of myocardial infarction, stroke, or cardiovascular death and associations with time to incident CVD were estimated using Cox proportional hazards models. A total of 1986 incident CVD cases occurred over a median follow-up of 11.2 years. After adjusting for baseline and time-varying individual and neighborhood-level confounding, a one standard deviation increase in walking destinations and physical activity/recreational facilities within 5 km of home was associated with a respective 7% (95% confidence interval (CI) = 0.87–0.99) and 12% (95% CI = 0.73–1.0) decreased risk of incident CVD. No significant associations were noted within a 1-km radius. Efforts to improve the availability of physical activity resources in neighborhoods may be an important strategy for lowering CVD.

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 DERR1-10.2196/16787

Background Activity spaces (AS), captured using GPS tracking devices, are measures of dynamic exposure to the built environment (BE). Methods Seven days of Global Positioning Systems (GPS) tracking data were obtained for 433 adult participants in the Seattle Obesity Study (SOS II). Heights and weights were measured. Dietary intakes from a food frequency questionnaire were used to calculate Healthy Eating Index (HEI 2010) scores. Linear regression analyses examined associations between AS measures: daily route length, convex hull, and radius of gyration, and diet quality and health outcomes, adjusting for covariates. Results AS measures did not vary by age, gender, race/ethnicity, or socioeconomic status. AS measures were not associated with diet quality or with self-reported obesity or diabetes. One AS measure, route length (in miles), was associated with being employed, living in the suburbs, and with distance and time commuting to work. Conclusion Spatial mobility studies based on GPS tracking of environmental exposure need to demonstrate a link to relevant health outcomes.

SafeBoda is a transportation company that provides road safety training and helmets to its motorcycle taxi drivers in Kampala. We sought to determine whether risk of road traffic crash (RTC) was lower in SafeBoda compared to regular (non-SafeBoda) motorcycle taxi drivers during a 6-month follow-up period. We collected participant demographic and behavioural data at baseline using computer-assisted personal interview, and occurrence of RTC every 2 months using text messaging and telephone interview from a cohort of 342 drivers. There were 85 crashes (31 in SafeBoda and 54 in regular drivers) during follow-up. Over the 6-month follow-up period, SafeBoda drivers were 39% less likely to be involved in a RTC than regular drivers after adjusting for age, possession of a driver’s license, and education (RR: 0.61, 95% CI: 0.39–0.97, p = .04). These findings suggest that the SafeBoda programme results in safer driving and fewer RTCs among motorcycle taxi drivers in Kampala.

Exposure to food environments has mainly been limited to counting food outlets near participants&rsquo, homes. This study considers food environment exposures in time and space using global positioning systems (GPS) records and fast food restaurants (FFRs) as the environment of interest. Data came from 412 participants (median participant age of 45) in the Seattle Obesity Study II who completed a survey, wore GPS receivers, and filled out travel logs for seven days. FFR locations were obtained from Public Health Seattle King County and geocoded. Exposure was conceptualized as contact between stressors (FFRs) and receptors (participants&rsquo, mobility records from GPS data) using four proximities: 21 m, 100 m, 500 m, and &frac12, mile. Measures included count of proximal FFRs, time duration in proximity to &ge, 1 FFR, and time duration in proximity to FFRs weighted by FFR counts. Self-reported exposures (FFR visits) were excluded from these measures. Logistic regressions tested associations between one or more reported FFR visits and the three exposure measures at the four proximities. Time spent in proximity to an FFR was associated with significantly higher odds of FFR visits at all proximities. Weighted duration also showed positive associations with FFR visits at 21-m and 100-m proximities. FFR counts were not associated with FFR visits. Duration of exposure helps measure the relationship between the food environment, mobility patterns, and health behaviors. The stronger associations between exposure and outcome found at closer proximities (&lt, 100 m) need further research.

Amenities at bus stops are thought to affect transit ridership, yet this relationship is rarely quantified. We assembled data on ridership and stop amenities, including Real-Time Information System (RTIS), shelters, lighting, litter receptacles, benches, and bike hoops, at 96 stops along two new Bus Rapid Transit lines in King County, Washington, between Fall 2012 and Fall 2014. We applied the Conditional Inference Tree method to identify combinations of amenity changes that characterized amenity installation patterns and examined the associations between changes in amenities and changes in ridership at stop-level. Compared to stops with the fewest changes, stops had a higher likelihood of increases in boardings if their combination of amenity changes included new RTIS, shelters, and bike hoops. There were no significant relationships between amenity changes and alightings. Stop amenities are positively associated with higher transit ridership, however, some amenities are more impactful than others.

To address the current obesity and inactivity epidemics, public health researchers have attempted to identify spatial factors that influence physical inactivity and obesity. Technologic and methodologic developments have led to a revolutionary ability to examine dynamic, high-resolution measures of temporally matched location and behavior data through GPS, accelerometry, and GIS. These advances allow the investigation of spatial energetics, high-spatiotemporal resolution data on location and time-matched energetics, to examine how environmental characteristics, space, and time are linked to activity-related health behaviors with far more robust and detailed data than in previous work. Although the transdisciplinary field of spatial energetics demonstrates promise to provide novel insights on how individuals and populations interact with their environment, there remain significant conceptual, technical, analytical, and ethical challenges stemming from the complex data streams that spatial energetics research generates. First, it is essential to better understand what spatial energetics data represent, the relevant spatial context of analysis for these data, and if spatial energetics can establish causality for development of spatially relevant interventions. Second, there are significant technical problems for analysis of voluminous and complex data that may require development of spatially aware scalable computational infrastructures. Third, the field must come to agreement on appropriate statistical methodologies to account for multiple observations per person. Finally, these challenges must be considered within the context of maintaining participant privacy and security. This article describes gaps in current practice and understanding and suggests solutions to move this promising area of research forward.

Background Studies have shown associations between neighborhood disadvantage and alcohol misuse among adults. Less is known about the role of neighborhood context in young adults (YAs), who engage in more disordered forms of alcohol use compared to other age groups. Using data collected monthly, this study examined whether YAs reported more alcohol use and consequences when they were living in neighborhoods with greater concentration of poverty. Method This study used data from 746 participants aged 18–23 years living in the Seattle, WA, region. Surveys were administered each month for 24 consecutive months. Measures included typical number of drinks per week and past month count of alcohol-related consequences. Residential addresses at each month were geocoded and linked to census-tract level percentage of households living at or below poverty threshold. Multilevel over-dispersed Poisson models were used to estimate associations between standardized monthly deviations in tract-level poverty from one’s average and alcohol outcomes. Results Across 14,247 monthly observations, the mean number of typical drinks per week was 4.8 (SD = 7.4) and the mean number of alcohol consequences was 2.1 (SD = 3.5). On months when they were living in neighborhoods with higher levels of poverty than their average, participants reported significantly higher levels of alcohol consequences (Count Ratio = 1.05; p = .045). Conclusion YAs may engage in more problematic forms of drinking when they reside in neighborhoods with higher levels of disadvantage. During a time of frequent residential changes, YAs moving to more disadvantaged neighborhoods may benefit from additional supports.

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

Objectives Utilitarian and recreational walking both contribute to physical activity. Yet walking for these two purposes may be different behaviors. We sought to provide operational definitions of utilitarian and recreational walking and to objectively measure their behavioral, spatial, and temporal differences in order to inform transportation and public health policies and interventions. Methods Data were collected 2008–2009 from 651 Seattle-King County residents, wearing an accelerometer and a GPS unit, and filling-in a travel diary for 7 days. Walking activity bouts were classified as utilitarian or recreational based on whether walking had a destination or not. Differences between the two walking purposes were analyzed, adjusting for the nested structure of walking activity within participants. Results Of the 4905 observed walking bouts, 87.4% were utilitarian and 12.6% recreational walking. Utilitarian walking bouts were 45% shorter in duration (−12.1 min) and 9% faster in speed (+0.3 km/h) than recreational walking bouts. Recreational walking occurred more frequently in the home neighborhood and was not associated with recreational land uses. Utilitarian walking occurred in areas having higher residential, employment, and street density, lower residential property value, higher area percentage of mixed-use neighborhood destinations, lower percentage of parks/trails, and lower average topographic slope than recreational walking. Conclusion Utilitarian and recreational walking are substantially different in terms of frequency, speed, duration, location, and related built environment. Policies that promote walking should adopt type-specific strategies. The high occurrence of recreational walking near home highlights the importance of the home neighborhood for this activity.

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.

In the past 15 years, a major research enterprise has emerged that is aimed at understanding associations between geographic and contextual features of the environment (especially the built environment) and elements of human energy balance, including diet, weight, and physical activity. Here we highlight aspects of this research area with a particular focus on research and opportunities in the United States as an example. We address four main areas: 1) The importance of valid and comparable data concerning behavior across geographies, 2) The ongoing need to identify and explore new environmental variables, 3) The challenge of identifying the causally relevant context, and 4) The pressing need for stronger study designs and analytical methods. Additionally, we discuss existing sources of geo-referenced health data which might be exploited by interdisciplinary research teams, personnel challenges and some aspects of funding for geospatial research by the US National Institutes of Health in the past decade, including funding for international collaboration and training opportunities.