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

Gender differences in the effects of urban environment on nighttime exercise behaviours: a qualitative study

Objective

With the acceleration of urbanization, nighttime exercise behaviours have rapidly emerged. Existing research indicates a correlation between urban environments and physical activity; however, studies focusing specifically on nighttime are still insufficient, particularly regarding gender differences. This study aims to identify the key factors in urban environments that influence residents' nighttime exercise behaviours and to explore the gender differences within these influences.

Methods

Purposeful sampling was employed to conduct semi-structured interviews with 30 residents who regularly engage in nighttime exercise. All transcribed interviews were analyzed using Colaizzi's phenomenological data analysis method.

Results

The findings revealed that physical environment and environmental perception are the two primary factors influencing nighttime exercise behaviour. These factors are further divided into 10 specific sub-themes: lighting, green spaces, site facilities and layout, traffic coherence, entertainment facilities, smart sports equipment, sense of safety, convenience, pleasure, and sense of belonging.

Conclusion

Females demonstrate a heightened sensitivity to the perception of the physical environment, placing greater emphasis on the feelings and experiences it provides. Males, on the other hand, focus more on the direct impact of the physical environment, such as its specific effects on exercise performance. Future urban planning and public policy should give greater consideration to gender differences in the use of urban exercise facilities, ensuring that nighttime exercise environments meet the needs of residents of different genders. This approach will contribute to enhancing overall community vitality and improving residents' health.

The Automatic Context Measurement Tool (ACMT) to Compile Participant-Specific Built and Social Environment Measures for Health Research: Development and Usability Study

BACKGROUND

The environment shapes health behaviors and outcomes. Studies exploring this influence have been limited to research groups with the geographic information systems expertise required to develop built and social environment measures (eg, groups that include a researcher with geographic information system expertise).

OBJECTIVE

The goal of this study was to develop an open-source, user-friendly, and privacy-preserving tool for conveniently linking built, social, and natural environmental variables to study participant addresses.

METHODS

We built the automatic context measurement tool (ACMT). The ACMT comprises two components: (1) a geocoder, which identifies a latitude and longitude given an address (currently limited to the United States), and (2) a context measure assembler, which computes measures from publicly available data sources linked to a latitude and longitude. ACMT users access both of these components using an RStudio/RShiny-based web interface that is hosted within a Docker container, which runs on a local computer and keeps user data stored in local to protect sensitive data. We illustrate ACMT with 2 use cases: one comparing population density patterns within several major US cities, and one identifying correlates of cannabis licensure status in Washington State.

RESULTS

In the population density analysis, we created a line plot showing the population density (x-axis) in relation to distance from the center of the city (y-axis, using city hall location as a proxy) for Seattle, Los Angeles, Chicago, New York City, Nashville, Houston, and Boston with the distances being 1000, 2000, 3000, 4000, and 5000 m. We found the population density tended to decrease as distance from city hall increased except for Nashville and Houston, 2 cities that are notably more sprawling than the others. New York City had a significantly higher population density than the others. We also observed that Los Angeles and Seattle had similarly low population densities within up to 2500 m of City Hall. In the cannabis licensure status analysis, we gathered neighborhood measures such as age, sex, commute time, and education. We found the strongest predictive characteristic of cannabis license approval to be the count of female children aged 5 to 9 years and the proportion of females aged 62 to 64 years who were not in the labor force. However, after accounting for Bonferroni error correction, none of the measures were significantly associated with cannabis retail license approval status.

CONCLUSIONS

The ACMT can be used to compile environmental measures to study the influence of environmental context on population health. The portable and flexible nature of ACMT makes it optimal for neighborhood study research seeking to attribute environmental data to specific locations within the United States.

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.

Mediating role of walking between perceived and objective walkability and cognitive function in older adults

The aim of this study was to examine the role of walking in explaining associations between perceived and objective measures of walkability and cognitive function among older adults. The study employed a cross-sectional design analyzing existing data. Data were obtained from the Adult Changes in Thought Activity Monitor study. Cognitive function and perceived walkability were measured by a survey. Objective walkability was measured using geographic information systems (GIS). Walking was measured using an accelerometer. We tested the mediating relationship based on 1,000 bootstrapped samples. Perceived walkability was associated with a 0.04 point higher cognitive function score through walking (p = 0.006). The mediating relationship accounted for 34% of the total relationship between perceived walkability and cognitive function. Walking did not have a significant indirect relationship on the association between objective walkability and cognitive function. Perceived walkability may be more relevant to walking behavior than objective walkability among older adults. Greater levels of perceived walkability may encourage older adults to undertake more walking, and more walking may in turn improve cognitive function in older adults.

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.

Neighbourhood correlates of average population walking: using aggregated, anonymised mobile phone data to identify where people walk

Understanding and monitoring socio-spatial patterns of population walking mobility can inform urban planning and geographically targeted health promotion strategies aimed at increasing population levels of physical activity. In this study we use aggregated, anonymous mobile phone mobility data to examine the association between neighbourhood physical and social characteristics and residents' weekly walking behaviour across 313 neighbourhoods in a large metropolitan region of Queensland, Australia. We find that residents in neighbourhoods that are highly fragmented by streets with speed limits above 50 kmph, residents in neighbourhoods with high retail density and those living is economically disadvantaged neighbourhoods walk fewer kilometres and minutes on average per week than their counterparts. These findings can inform urban planning policy on the minimum specifications required in newly developing neighbourhoods and provide targets for retro-fitting features into existing neighbourhoods.

Machine Learning Approaches for Measuring Neighborhood Environments in Epidemiologic Studies

Purpose of review

Innovations in information technology, initiatives by local governments to share administrative data, and growing inventories of data available from commercial data aggregators have immensely expanded the information available to describe neighborhood environments, supporting an approach to research we call Urban Health Informatics. This review evaluates the application of machine learning to this new wealth of data for studies of the effects of neighborhood environments on health.

Recent findings

Prominent machine learning applications in this field include automated image analysis of archived imagery such as Google Street View images, variable selection methods to identify neighborhood environment factors that predict health outcomes from large pools of exposure variables, and spatial interpolation methods to estimate neighborhood conditions across large geographic areas.

Summary

In each domain, we highlight successes and cautions in the application of machine learning, particularly highlighting legal issues in applying machine learning approaches to Google’s geo-spatial data.

Spatial Pattern of the Walkability Index, Walk Score and Walk Score Modification for Elderly

Contemporary cities require excellent walking conditions to support human physical activity, increase humans’ well-being, reduce traffic, and create a healthy urban environment. Various indicators and metrics exist to evaluate walking conditions. To evaluate the spatial pattern of objective-based indicators, two popular indices were selected—the Walkability Index (WAI), representing environmental-based indicators, and Walk Score (WS), which applies an accessibility-based approach. Both indicators were evaluated using adequate spatial units (circle buffers with radii from 400 m to 2414 m) in two Czech cities. A new software tool was developed for the calculation of WS using OSM data and freely available network services. The new variant of WS was specifically designed for the elderly. Differing gait speeds, and variable settings of targets and their weights enabled the adaptation of WS to local conditions and personal needs. WAI and WS demonstrated different spatial pattern where WAI is better used for smaller radii (up to approx. 800 m) and WS for larger radii (starting from 800 m). The assessment of WS for both cities indicates that approx. 40% of inhabitants live in unsatisfactory walking conditions. A sensitivity analysis discovered the major influences of gait speed and the β coefficient on the walkability assessment.

An open science study of ageing in companion dogs

The Dog Aging Project is a long-term longitudinal study of ageing in tens of thousands of companion dogs. The domestic dog is among the most variable mammal species in terms of morphology, behaviour, risk of age-related disease and life expectancy. Given that dogs share the human environment and have a sophisticated healthcare system but are much shorter-lived than people, they offer a unique opportunity to identify the genetic, environmental and lifestyle factors associated with healthy lifespan. To take advantage of this opportunity, the Dog Aging Project will collect extensive survey data, environmental information, electronic veterinary medical records, genome-wide sequence information, clinicopathology and molecular phenotypes derived from blood cells, plasma and faecal samples. Here, we describe the specific goals and design of the Dog Aging Project and discuss the potential for this open-data, community science study to greatly enhance understanding of ageing in a genetically variable, socially relevant species living in a complex environment.

Exploring the spatial scale effects of built environments on transport walking: Multi-Ethnic Study of Atherosclerosis

We employed a longitudinal distributed lag modeling approach to systematically estimate how associations between built environment features and transport walking decayed with the increase of distance from home to built environment destinations. Data came from a cohort recruited from six U.S. cities (follow-up 2000-2010, N = 3913, baseline mean age 60). Built environment features included all walkable destinations, consisting of common and popular destinations for daily life. We also included two subsets frequent social destinations and food stores to examine if the spatial scale effects differed by varying density for different types of built environment destinations. Adjusted results found that increases in transport walking diminished when built environment destinations were farther, although distance thresholds varied across different types of built environment destinations. Higher availability of walking destinations within 2-km and frequent social destinations within 1.6-km were associated with transport walking. Food stores were not associated with transport walking. This new information will help policymakers and urban designers understand at what distances each type of built environment destinations influences transport walking, in turn informing the development of interventions and/or the placement of amenities within neighborhoods to promote transport walking. The findings that spatial scales depend on specific built environment features also highlight the need for methods that can more flexibly estimate associations between outcomes and different built environment features across varying contexts, in order to improve our understanding of the spatial mechanisms involved in said associations.

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.

Walkability measures to predict the likelihood of walking in a place: A classification and regression tree analysis

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).Thus there may be contexts where transparent and longitudinally available measures of urban form are worth a marginal tradeoff in prediction accuracy. This comparison of walkability measures using CART highlights the importance for public health and urban design researchers to think carefully about how and why particular walkability measures are used.

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.