Residential buffer, perceived neighborhood, and individual activity space: New refinements in the definition of exposure areas – The RECORD Cohort Study

Parenthood, spatial temporal environmental exposure, and leisure-time physical activity participation: Evidence from a micro-timescale retrospective longitudinal study

Parents with dependent children are at a high risk of physical inactivity. While previous studies have mostly focused on how parents' time constraints and changing social network may inhibit leisure time physical activity (LTPA) over the long-term, less is known about the integrated effects of parenting and spatial-temporal environmental exposure on the execution of LTPA during certain episodes of a day. By adopting an integrated social-spatiotemporal-environmental model (ST-ISEM) based on micro-timescale retrospective longitudinal analysis, we examine the association between LTPA participation and spatial-temporal environmental exposure at a micro-timescale, i.e., at the episode-level in working adults' workday, and specifically how parenting integrated with spatial-temporal environmental exposure can jointly influence episode-level LTPA participation. Using data from the day reconstruction method from 701 individuals in Shenzhen, China, we find that parenting may affect the participation of LTPA on workdays not only by shaping temporal environmental factors (time constraint path and social network path), but also by interacting with built environmental exposures (spatial path), both at the episode-level. This study contributes to the theorizing of an integrated social-environmental model for health and wellbeing by extending the ISEM from the life span to the micro-timescale and also by highlighting the importance of temporality in environmental exposure and health studies. It also contributes to the spatial temporal behavioral perspective of time geography literature by clarifying multiple pathways through which social and spatiotemporal environmental factors could interact and jointly affect health behaviors at a micro-timescale. This study contributes to the literature on parenting and LTPA decline by enriching and deepening the understanding of the time constraint and social network pathways through which parenting leads to LTPA change at the micro-timescale. While time constraints may decrease parents' LTPA at long-term, increasing physical activities related to childcare after work may strongly obstruct moderate-to-vigorous LTPA at a micro-timescale. This study also identifies a spatial pathway by which parenting hinders LTPA due to changing understanding and usage of urban spaces. This pathway warrants attention from social epidemiologists, health geographers, and urban planners since existing interventions promoting physical activity in urban spaces may be ineffective for parents.

Minute level smartphone derived exposure to greenness and consumer wearable derived physical activity in a cohort of US women

BACKGROUND

Inconsistent results have been found in the literature on associations of greenness, or vegetation quantity, and physical activity. However, few studies have assessed associations between mobility-based greenness and physical activity from mobile health data from smartphone and wearable devices with fine spatial and temporal resolution.

METHODS

We assessed mobility-based greenness exposure and wearable accelerometer data from participants in the US-based prospective Nurses' Health Study 3 cohort Mobile Health (mHealth) Substudy (2018-2020). We recruited 500 female participants with instructions to wear devices over four 7-day sampling periods equally spaced throughout the year. After restriction criteria there were 337 participants (mean age 36 years) with n = 639,364 unique observations. Normalized Difference Vegetation Index (NDVI) data were derived from 30 m x 30 m Landsat-8 imagery and spatially joined to GPS points recorded every 10 min. Fitbit proprietary algorithms provided physical activity summarized as mean number of steps per minute, which we averaged during the 10-min period following a GPS-based greenness exposure assessment. We utilized Generalized Additive Mixed Models to examine associations (every 10 min) between greenness and physical activity adjusting for neighborhood and individual socioeconomic status, Census region, season, neighborhood walkability, daily mean temperature and precipitation. We assessed effect modification through stratification and interaction models and conducted sensitivity analyses.

RESULTS

Mean 10-min step count averaged 7.0 steps (SD 14.9) and greenness (NDVI) averaged 0.3 (SD 0.2). Contrary to our hypotheses, higher greenness exposure was associated non-linearly with lower mean steps per minute after adjusting for confounders. We observed statistically significant effect modification by Census region and season.

DISCUSSION

We utilized objective physical activity data at fine temporal and spatial scales to present novel estimates of the association between mobility-based greenness and step count. We found higher levels of greenness were inversely associated with steps per minute.

A detour for snacks and beverages? A cross-sectional assessment of selective daily mobility bias in food outlet exposure along the commuting route and dietary intakes

The evidence of selective daily mobility bias distorting exposure-health associations is limited. Using 7-day smartphone-based global positioning system (GPS) tracking data for 67 Dutch adults aged 25-45, we conducted paired Wilcoxon tests to compare the absolute and relative exposure to food outlets along actual and modelled commuting routes. We fitted Tobit regressions to examine their associations with three daily snack and soft drink intake outcomes. We found significant differences in absolute food outlet exposure between two types of routes. Adjusted regression analyses yielded unexpected associations between dietary intakes and food outlet exposures. Our results suggested no evidence of a selective daily mobility bias in the association between the food environment along commuting routes and adults' snacks and soft drink consumption in this sample.

Nine-year exposure to residential greenness and the risk of metabolic syndrome among Luxembourgish adults: A longitudinal analysis of the ORISCAV-Lux cohort study

Growing evidence shows a beneficial effect of exposure to greenspace on cardiometabolic health, although limited by the cross-sectional design of most studies. This study examined the long-term associations of residential greenness exposure with metabolic syndrome (MetS) and MetS components within the ORISCAV-LUX study (Wave 1: 2007-2009, Wave 2: 2016-2017, n = 395 adults). Objective exposure to residential greenness was measured in both waves by the Soil-Adjusted Vegetation Index (SAVI) and by Tree Cover Density (TCD). Linear mixed models were fitted to estimate the effect of baseline levels and change in residential greenness on MetS (continuous score: siMS score) and its components (waist circumference, triglycerides, HDL-cholesterol, fasting plasma glucose and systolic blood pressure), respectively. This study provides evidence that an increase in SAVI, but not TCD, may play a role in preventing MetS, as well as improving HDL-cholesterol and fasting plasma glucose levels. Greater baseline SAVI was also associated with lower fasting plasma glucose levels in women and participants living in municipalities with intermediate housing price, and greater baseline TCD was associated with larger waist circumference. Overall, findings suggest a mixed impact of increased greenness on cardiometabolic outcomes. Further longitudinal research is needed to better understand the potential effects of different types of greenness exposure on cardiometabolic outcomes.

The association of green space, tree canopy and parks with life expectancy in neighborhoods of Los Angeles

Substantial evidence suggests that access to urban green spaces and parks is associated with positive health outcomes, including decreased mortality. Few existing studies have investigated the association between green spaces and life expectancy (LE), and none have used small-area data in the U.S. Here we used the recently released U.S. Small-Area Life Expectancy Estimates Project data to quantify the relationship between LE and green space in Los Angeles County, a large diverse region with inequities in park access. We developed a model to quantify the association between green space and LE at the census tract level. We evaluated three green space metrics: normalized difference vegetation index (NDVI, 0.6-meter scale), percent tree canopy cover, and accessible park acres. We statistically adjusted for 15 other determinants of LE. We also developed conditional autoregressive models to account for spatial dependence. Tree canopy and NDVI were both significantly associated with higher LE. For an interquartile range (IQR) increase in each metric respectively, the spatial models demonstrated a 0.24 to 0.33-year increase in LE. Tree canopy and NDVI also modified the effect of park acreage on LE. ln areas with tree canopy levels below the county median, an IQR increase in park acreage was associated with an increase of 0.12 years. Although on an individual level these effects were modest, we predicted 155,300 years of LE gains across the population in LA County if all areas below median tree canopy were brought to the county median of park acres. If tree canopy or NDVI were brought to median levels, between 570,300 and 908,800 years of LE could be gained. The majority of potential gains are in areas with predominantly Hispanic/Latinx and Black populations. These findings suggest that equitable access to green spaces could result in substantial population health benefits.

Selective Daily Mobility Bias in the Community Food Environment: Case Study of Greater Hartford, Connecticut

The community food environment has potential influences on community members' dietary health outcomes, such as obesity and Type II diabetes. However, most existing studies evaluating such health effects neglect human mobility. In food patrons' daily travels, certain locations may be preferred and patronized more frequently than others. This behavioral uncertainty, known as the selective daily mobility bias (SDMB), is less explored in community-food-environment research. In this paper, we aim to confirm the existence of the SDMB by systematically exploring the large-scale GPS-based restaurant-visit patterns in the Greater Harford region, Connecticut. Next, we explore the restaurant and neighborhood characteristics that are associated with the restaurant-visit patterns. Our primary results demonstrate that (1) most restaurant customers originate from areas outside of the census tract where the restaurant is located, and (2) restaurants located in socially vulnerable areas attract more customers in total, more customers from local areas, and more customers from other socially vulnerable areas. These results confirm the relevance of the SDMB to the community food environment, and suggest ways that the SDMB can be moderated by an uneven socio-economic landscape. The findings demonstrate the necessity of incorporating human-mobility data into the study of the community food environment.

What triggers selective daily mobility among older adults? A study comparing trip and environmental characteristics between observed path and shortest path

Interest is growing in neighborhood effects on health beyond individual's home locations. However, few studies accounted for selective daily mobility bias. Selective mobility of 470 older adults (aged 67-94) living in urban and suburban areas of Luxembourg, was measured through detour percentage between their observed GPS-based paths and their shortest paths. Multilevel negative binomial regression tested associations between detour percentage, trips characteristics and environmental exposures. Detour percentage was higher for walking trips (28%) than car trips (16%). Low-speed areas and connectivity differences between observed and shortest paths vary by transport mode, indicating a potential selective daily mobility bias. The positive effects of amenities, street connectivity, low-speed areas and greenness on walking detour reinforce the existing evidence on older adults' active transportation. Urban planning interventions favoring active transportation will also promote walking trips with longer detours, helping older adults to increase their physical activity levels and ultimately promote healthy aging.

Analyzing differences between spatial exposure estimation methods: A case study of outdoor food and beverage advertising in London, Canada

Exposure assessment in the context of mobility-oriented health research often is challenged by the type of spatial measurement technique used to estimate exposures to environmental features. The purpose of this study is to compare smartphone global positioning system (GPS), shortest network path mobility, and buffer-based approaches in estimating exposure to outdoor food and beverage advertising among a sample of 154 teenagers involved in the SmartAPPetite study during 2018 in London, Ontario, Canada. Participants were asked to report their home postal code, age, gender identity, ethnicity, and number of purchases they had made at a retail food outlet in the past month. During the same time period, a mobile phone application was used to log their mobility and specifically record when a participant was in close proximity to outdoor advertising. The results of negative binomial regression modelling reveal significant differences in estimates of advertising exposure, and the relationship to self-reported purchasing. Spatial exposure estimation methods showed differences across regression models, with the buffer and observed GPS approaches delivering the best fitting models, depending on the type of retail food outlet. There is a clear need for more robust research of spatial exposure measurement techniques in the context of mobility and food (information) environment research.

Investigating how researcher-defined buffers and self-drawn neighbourhoods capture adolescent availability to physical activity facilities and greenspaces: An exploratory study

BACKGROUND

Modifying the environment is considered an effective population-level approach for increasing healthy behaviours, but associations remain ambiguous. This exploratory study aims to compare researcher-defined buffers and self-drawn neighbourhoods (SDN) to objectively measured availability of physical activity (PA) facilities and greenspaces in adolescents.

METHODS

Seven consecutive days of GPS data were collected in an adolescent sample of 14-18 year olds (n = 69). Using Points of Interest and greenspace data, availability of PA opportunities within activity spaces were determined. We compared 30 different definitions of researcher-defined neighbourhoods and SDNs to objectively measured availability.

RESULTS

Findings showed low agreement for all researcher-defined buffers in measuring the availability of PA facilities in activity spaces. However, results were less clear for greenspace. SDNs also demonstrate low agreement for capturing availability to the PA environment.

CONCLUSION

This exploratory study highlights the inadequacy of researcher-defined buffers and SDNs to define availability to environmental features.

The impact of GPS-derived activity spaces on personal PM2.5 exposures in the MADRES cohort

BACKGROUND

In-utero exposure to particulate matter with aerodynamic diameter less than 2.5 μm (PM_2.5) is associated with low birth weight and health risks later in life. Pregnant women are mobile and locations they spend time in contribute to their personal PM_2.5 exposures. Therefore, it is important to understand how mobility and exposures encountered within activity spaces contribute to personal PM_2.5 exposures during pregnancy.

METHODS

We collected 48-h integrated personal PM_2.5 samples and continuous geolocation (GPS) data for 213 predominantly Hispanic/Latina pregnant women in their 3^rd trimester in Los Angeles, CA. We also collected questionnaires and modeled outdoor air pollution and meteorology in their residential neighborhood. We calculated three GPS-derived activity space measures of exposure to road networks, greenness (NDVI), parks, traffic volume, walkability, and outdoor PM_2.5 and temperature. We used bivariate analyses to screen variables (GPS-extracted exposures in activity spaces, individual characteristics, and residential neighborhood exposures) based on their relationship with personal, 48-h integrated PM_2.5 concentrations. We then built a generalized linear model to explain the variability in personal PM_2.5 exposure and identify key contributing factors.

RESULTS

Indoor PM_2.5 sources, parity, and home ventilation were significantly associated with personal exposure. Activity-space based exposure to roads was associated with significantly higher personal PM_2.5 exposure, while greenness was associated with lower personal PM_2.5 exposure (β = -3.09 μg/m³ per SD increase in NDVI, p-value = 0.018). The contribution of outdoor PM_2.5 to personal exposure was positive but relatively lower (β = 2.05 μg/m³ per SD increase, p-value = 0.016) than exposures in activity spaces and the indoor environment. The final model explained 34% of the variability in personal PM_2.5 concentrations.

CONCLUSIONS

Our findings highlight the importance of activity spaces and the indoor environment on personal PM_2.5 exposures of pregnant women living in Los Angeles, CA. This work also showcases the multiple, complex factors that contribute to total personal PM_2.5 exposure.

Community social environments and cigarette smoking

Cigarette smoking remains a primary contributor to health disparities in the United States, and significant evidence suggests that smoking behavior is socially influenced. Though residential neighborhoods are important for health disparities, recent evidence suggests that people spend the majority of their waking time away from the residential neighborhood. We advance research on neighborhoods and smoking by using individual, neighborhood, and activity space data for adults in the Los Angeles Family and Neighborhood Survey (L.A.FANS). Moving beyond socioeconomic indicators of neighborhoods, we investigate the ways in which residential neighborhood social cohesion, neighborly exchange, and perceived danger impact smoking behavior after accounting for confounding factors in both the residential neighborhood and other activity spaces in which adults spend their days. We find that perceptions of danger in the residential neighborhood is robustly associated with the likelihood of smoking cigarettes. Further, measures of community social organization interact with perceived danger to influence smoking behavior. Adults with high levels of perceived danger are twice as likely to smoke if residing in communities with lower levels of social organization in the form of helpful, trusting, and supportive relationships. Understanding how the social organization of communities contributes to smoking disparities is important for curbing smoking's impact on population health.

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Adults with high perceived neighborhood danger are more likely current smokers.

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Adults in high danger low social cohesion neighborhoods twice as likely to smoke.

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Adults in high danger low neighborly exchange neighborhoods twice as likely to smoke.

Bayesian Forecasting with a Regime-Switching Zero-Inflated Multilevel Poisson Regression Model: An Application to Adolescent Alcohol Use with Spatial Covariates

In this paper, we present and evaluate a novel Bayesian regime-switching zero-inflated multilevel Poisson (RS-ZIMLP) regression model for forecasting alcohol use dynamics. The model partitions individuals' data into two phases, known as regimes, with: (1) a zero-inflation regime that is used to accommodate high instances of zeros (non-drinking) and (2) a multilevel Poisson regression regime in which variations in individuals' log-transformed average rates of alcohol use are captured by means of an autoregressive process with exogenous predictors and a person-specific intercept. The times at which individuals are in each regime are unknown, but may be estimated from the data. We assume that the regime indicator follows a first-order Markov process as related to exogenous predictors of interest. The forecast performance of the proposed model was evaluated using a Monte Carlo simulation study and further demonstrated using substance use and spatial covariate data from the Colorado Online Twin Study (CoTwins). Results showed that the proposed model yielded better forecast performance compared to a baseline model which predicted all cases as non-drinking and a reduced ZIMLP model without the RS structure, as indicated by higher AUC (the area under the receiver operating characteristic (ROC) curve) scores, and lower mean absolute errors (MAEs) and root-mean-square errors (RMSEs). The improvements in forecast performance were even more pronounced when we limited the comparisons to participants who showed at least one instance of transition to drinking.

Development of a neighborhood drivability index and its association with transportation behavior in Toronto

BACKGROUND

Car driving is a form of passive transport that is associated with an increase in physical inactivity, obesity, air pollution and noise. Built environment characteristics may influence transport mode choice, but comprehensive indices for built environment characteristics that drive car use are still lacking, while such an index could provide tangible policy entry points.

OBJECTIVE

We developed and validated a neighbourhood drivability index, capturing combined dimensions of the neighbourhood environment in the City of Toronto, and investigated its association with transportation choices (car, public transit or active transport), overall, by trip length, and combined for residential neighbourhood and workplace drivability.

METHODS

We used exploratory factor analysis to derive distinct factors (clusters of one or more environmental characteristics) that reflect the degree of car dependency in each neighbourhood, drawing from candidate variables that capture density, diversity, design, destination accessibility, distance to transit, and demand management. Area-level factor scores were then combined into a single composite score, reflecting neighbourhood drivability. Negative binomial generalized estimating equations were used to test the association between driveability quintiles (Q) and primary travel mode (>50% of trips by car, public transit, or walking/cycling) in a population-based sample of 63,766 Toronto residents enrolled in the Transportation Tomorrow Survey (TTS) wave 2016, adjusting for individual and household characteristics, and accounting for clustering of respondents within households.

RESULTS

The drivability index consisted of three factors: Urban sprawl, pedestrian facilities and parking availability. Relative to those living in the least drivable neighbourhoods (Q1), those in high drivability areas (Q5) had a significantly higher rate of car travel (adjusted Risk Ratio (RR): 1.80, 95%CI: 1.77-1.88), and lower rate of public transit use (RR: 0.90, 95%CI: 0.85-0.94) and walking/cycling (RR: 0.22, 95%CI: 0.19-0.25). Associations were strongest for short trips (<3 km) (RR: 2.72, 95%CI: 2.48-2.92), and in analyses where both residential and workplace drivability was considered (RR for car use in high/high vs. low/low residential/workplace drivability: 2.18, 95%CI: 2.08-2.29).

CONCLUSION

This novel neighbourhood drivability index predicted whether local residents drive or use active modes of transportation and can be used to investigate the association between drivability, physical activity, and chronic disease risk.

Evaluating the Effects of Built Environment on Street Vitality at the City Level: An Empirical Research Based on Spatial Panel Durbin Model

There is evidence that the built environment has an influence on street vitality. However, previous studies seldom assess the direct, indirect, and total effect of multiple environmental elements at the city level. In this study, the features of the street vitality on Xiamen Island are described based on the location-based service Big Data. Xiamen Island is the central urban area of Xiamen, one of the national central cities in China. With the help of multi-source data such as street view images, the condition of design that is difficult to effectively measure with traditional data can be better explored in detail on a macro scale. The built environment is measured through a 5D system at the city level, including Density, Diversity, Design, Destination accessibility, and Distance to transit. Spatial panel Durbin models are constructed to analyze the influence of the built environment on the street vitality on weekdays and weekends, and the direct, indirect, and total effects are evaluated. Results indicate that at the city level, the built environment plays a significant role in promoting street vitality. Functional density is not statistically significant. Most of the elements have spatial effects, except for several indicators in the condition of the design. Compared with the conclusions of previous studies, some indicators have different effects on different spatial scales. For instance, on the micro scale, greening can enhance the attractiveness of streets. However, on the macro scale, too much greening brings fewer functions along the street, which inhibits the street vitality. The condition of design has the greatest effect, followed by destination accessibility. The differences in the influences of weekdays and weekends are mainly caused by commuting behaviors. Most of the built environment elements have stronger effects on weekends, indicating that people interact with the environment more easily during this period.

Delineating the geographic context of physical activities: A systematic search and scoping review of the methodological approaches used in social ecological research over two decades

BACKGROUND

The social ecological approach suggests that the spatial context among other factors influence physical activity behavior. Ample research documents physical environmental effects on physical activity. Yet, to date inconsistent associations remain, which might be explained by conceptual and methodological challenges in measuring the spatial dimensions of health behavior. We review methods applied to measure the spatial contexts in the social ecological physical activity literature.

METHODS

Online databases and selected reviews were used to identify papers published between 1990 and 2020. A total of 2167 records were retrieved, from which 412 studies that used physical activity as a primary outcome variable, included measures of the physical environment and applied the main principles of the social ecological approach, were included.

RESULTS

Subjective approaches were the dominant method to capture the spatial context of physical activities. These approaches were applied in 67% (n=279) of the studies. From the objective approaches an administrative unit was most prevalent and was applied in 29% (n=118) of the studies. The most comprehensive objective spatial methods that capture the true environmental exposure, were used only in 2% (n=10) of the studies.

CONCLUSIONS

Current social ecological physical activity research applies simple conceptualizations and methods of the spatial context. While conceptual and methodological concerns have been repeatedly expressed, no substantive progress has been made in the use of spatial approaches. To further our understanding on place effects on health, future studies should carefully consider the choice of spatial approaches, and their effect on study results.

GPS2space: An Open-source Python Library for Spatial Measure Extraction from GPS Data

Global Positioning System (GPS) data have become one of the routine data streams collected by wearable devices, cell phones, and social media platforms in this digital age. Such data provide research opportunities in that they may provide contextual information to elucidate where, when, and why individuals engage in and sustain particular behavioral patterns. However, raw GPS data consisting of densely sampled time series of latitude and longitude coordinate pairs do not readily convey meaningful information concerning intra-individual dynamics and inter-individual differences; substantial data processing is required. Raw GPS data need to be integrated into a Geographic Information System (GIS) and analyzed, from which the mobility and activity patterns of individuals can be derived, a process that is unfamiliar to many behavioral scientists. In this tutorial article, we introduced GPS2space, a free and open-source Python library that we developed to facilitate the processing of GPS data, integration with GIS to derive distances from landmarks of interest, as well as extraction of two spatial features: activity space of individuals and shared space between individuals, such as members of the same family. We demonstrated functions available in the library using data from the Colorado Online Twin Study to explore seasonal and age-related changes in individuals' activity space and twin siblings' shared space, as well as gender, zygosity and baseline age-related differences in their initial levels and/or changes over time. We concluded with discussions of other potential usages, caveats, and future developments of GPS2space.

GPS2space: An Open-source Python Library for Spatial Measure Extraction from GPS Data

Global Positioning System (GPS) data have become one of the routine data streams collected by wearable devices, cell phones, and social media platforms in this digital age. Such data provide research opportunities in that they may provide contextual information to elucidate where, when, and why individuals engage in and sustain particular behavioral patterns. However, raw GPS data consisting of densely sampled time series of latitude and longitude coordinate pairs do not readily convey meaningful information concerning intra-individual dynamics and inter-individual differences; substantial data processing is required. Raw GPS data need to be integrated into a Geographic Information System (GIS) and analyzed, from which the mobility and activity patterns of individuals can be derived, a process that is unfamiliar to many behavioral scientists. In this tutorial article, we introduced GPS2space, a free and open-source Python library that we developed to facilitate the processing of GPS data, integration with GIS to derive distances from landmarks of interest, as well as extraction of two spatial features: activity space of individuals and shared space between individuals, such as members of the same family. We demonstrated functions available in the library using data from the Colorado Online Twin Study to explore seasonal and age-related changes in individuals' activity space and twin siblings' shared space, as well as gender, zygosity and baseline age-related differences in their initial levels and/or changes over time. We concluded with discussions of other potential usages, caveats, and future developments of GPS2space.

Activity Locations, Residential Segregation, and the Significance of Residential Neighborhood Boundary Perceptions

The inadequacies of residential census geography in capturing urban residents' routine exposures have motivated efforts to more directly measure residents' activity spaces. In turn, insights regarding urban activity patterns have been used to motivate alternative residential neighborhood measurement strategies incorporating dimensions of activity space in the form of egocentric neighborhoods-measurement approaches that place individuals at the center of their own residential neighborhood units. Unexamined, however, is the extent to which the boundaries of residents' own self-defined residential neighborhoods compare with census-based and egocentric neighborhood measurement approaches in aligning with residents' routine activity locations. We first assess this question, examining whether the boundaries of residents' self-defined residential neighborhoods are in closer proximity to the coordinates of a range of activity location types than are the boundaries of their census and egocentric residential neighborhood measurement approaches. We find little evidence that egocentric or, crucially, self-defined residential neighborhoods better align with activity locations, suggesting a division in residents' activity locations and conceptions of their residential neighborhoods. We then examine opposing hypotheses about how self-defined residential neighborhoods and census tracts compare in socioeconomic and racial composition. Overall, our findings suggest that residents bound less segregated neighborhoods than those produced by census geography, but self-defined residential neighborhoods still reflect a preference toward homophily when considering areas beyond the immediate environment of their residence. These findings underscore the significance of individuals' conceptions of residential neighborhoods to understanding and measuring urban social processes such as residential segregation and social disorganization.

Assessing individual environmental exposure derived from the spatiotemporal behavior context and its impacts on mental health

Research on the associations between environmental exposures and mental health has attracted considerable attention. Most studies to date have mainly estimated environmental health effects based on static geographic contexts (e.g., residential neighborhoods, administrative units), ignoring the dynamic nature of individual spatiotemporal behavior, which may lead to unreliable results. To address this limitation, this study collects survey data from 1003 adults in Guangzhou, China. Then, it delineates dynamic geographic context to capture individual daily activity and travel and assesses individual exposure to environmental factors derived from the home buffer (HB) and the time-weighted activity and travel buffer (TATB). Finally, multiple linear regression models are used in this paper to examine and compare the relationships between individual environmental exposure and mental health based on the HB and TATB. The results of this study indicate that there are great differences in individual environmental exposure levels based on the HB and TATB. The explanatory power of the environmental factors obtained from the TATB on mental health is greater than that derived from the HB. Specifically, exposures to some environmental factors (i.e., green space coverage, blue space coverage, fitness facility density, and recreational facility density) derived from the TATB have mental health-promoting effects, while exposures to the other environmental factors (i.e., public transit station density) have mental health-constraining effects. These findings enrich our knowledge of spatiotemporal behavior and the effects of the dynamic contextual environment on mental health, as well as provide valuable implications for urban planning and public health service.

Comparing competing geospatial measures to capture the relationship between the neighborhood food environment and diet

PURPOSE

To examine how the choice of neighborhood food environment definition impacts the association with diet.

METHODS

Using food frequency questionnaire data from the Reasons for Geographic and Racial Differences in Stroke study at baseline (2003-2007), we calculated participants' dietary inflammation score (DIS) (n = 20,331); higher scores indicate greater pro-inflammatory exposure. We characterized availability of supermarkets and fast food restaurants using several geospatial measures, including density (i.e., counts/km2) and relative measures (i.e., percentage of all food stores or restaurants); and various buffer distances, including administrative units (census tract) and empirically derived buffers ("classic" network, "sausage" network) tailored to community type (higher density urban, lower density urban, suburban/small town, rural). Using generalized estimating equations, we estimated the association between each geospatial measure and DIS, controlling for individual- and neighborhood-level sociodemographics.

RESULTS

The choice of buffer-based measure did not change the direction or magnitude of associations with DIS. Effect estimates derived from administrative units were smaller than those derived from tailored empirically derived buffer measures. Substantively, a 10% increase in the percentage of fast food restaurants using a "classic" network buffer was associated with a 6.3 (SE = 1.17) point higher DIS (P< .001). The relationship between the percentage of supermarkets and DIS, however, was null. We observed high correlation coefficients between buffer-based density measures of supermarkets and fast food restaurants (r = 0.73-0.83), which made it difficult to estimate independent associations by food outlet type.

CONCLUSIONS

Researchers should tailor buffer-based measures to community type in future studies, and carefully consider the theoretical and statistical implications for choosing relative (vs. absolute) measures.

Dimensions of Community Assets for Health. A Systematised Review and Meta-Synthesis

Since Aaron Antonovsky’s salutogenesis theory and Morgan and Ziglio’s health assets model were first proposed, there has been a growing concern to define the resources available to the individual and the community to maintain or improve health and well-being. The aim of the present study was to identify the dimensions that characterise community assets for health. To this end, we conducted a systematised review with a meta-synthesis and content analysis of research or projects involving asset mapping in the community. Articles that met our eligibility criteria were: (1) based on the salutogenic approach and (2) described an assets mapping process and among their results, explained what, how and why particular community assets for health had been selected. The search included primary studies in the published and grey literature which were selected from websites and electronic databases (Web of Science, MEDLINE, Scopus, EBSCOhost, Dialnet, SciELO). Of the 607 records examined by a single reviewer, 34 were included in the content analysis and 14 in the qualitative synthesis. Using an inductive process, we identified 14 dimensions with 24 categories, for which in-depth literature reviews were then carried out to define specific indicators and items. These dimensions were: utility, intention, previous use, accessibility (“circumstances–opportunity–affordability”), proximity-walkability, connectivity, intelligibility (visibility, transparency), identity (uniqueness, appropriability, attachment), design (configuration, functionality, comfort), safety (objective/subjective), diversity, the dimension of public and private, and sustainability (which includes maintenance, profitability or economic sustainability, environmental sustainability, centrality-participation and equity-inclusiveness).

Identifying the Daily Activity Spaces of Older Adults Living in a High-Density Urban Area: A Study Using the Smartphone-Based Global Positioning System Trajectory in Shanghai

The characteristics of the built environment and the configuration of public facilities can affect the health and well-being of older adults. Recognizing the range of daily activities and understanding the utilization of public facilities among older adults has become essential in planning age-friendly communities. However, traditional methods are unable to provide large-scale objective measures of older adults’ travel behaviors. To address this issue, we used the smartphone-based global positioning system (GPS) trajectory to explore the activity spaces of 76 older adults in a high-density urban community in Shanghai for 102 consecutive days. We found that activity spaces are centered around older adults’ living communities, with 46.3% within a 1.5 km distance. The older adults’ daily activities are within a 15 min walking distance, and accessibility is the most important factor when making a travel choice to parks and public facilities. We also found that the travel range and spatial distribution of points of interest are different between age and gender groups. In addition, we found that using a concave hull with Alpha shape algorithm is more applicable and robust than the traditional convex hull algorithm. This is a unique case study in a high-density urban area with objective measures for assessing the activity spaces of older adults, thus providing empirical evidence for promoting healthy aging in cities.

Measuring neighborhood deprivation for childhood health and development - scale implications in rural and urban context

Neighborhood deprivation plays an important role in childhood health and development, but defining the appropriate neighborhood definition presents theoretical as well as practical challenges. Few studies have compared neighborhood definitions outside of highly urbanized settings. The purpose of the current study was to evaluate how various administrative and ego-centric neighborhood definitions may impact measured exposure to deprivation across the urban-rural continuum. We do so using the Family Life Project, a prospective longitudinal population-based sample of families living in North Carolina and Pennsylvania (USA), which also sets the stage for future investigations of neighborhood impacts on childhood health and development. To measure neighborhood deprivation, a standardized index of socioeconomic deprivation was calculated using data from the 2007-2011 American Community Survey. Families' residential addresses when children were 2 months of age (n=1036) were geocoded and overlaid onto a deprivation index layer created at the census block group level to construct multiple administrative and ego-centric neighborhood definitions. Friedman tests were used to compare distributions of neighborhood deprivation across these neighborhood definitions within urbanized areas, urban clusters, and rural areas. Results indicated differences in urbanized areas (Chisquare= 897.75, P<0.001) and urban clusters (Chi-square=687.83, P<0.001), but not in rural areas (Chi-square=13.52, P=0.332). Findings imply that in urban areas, choice of neighborhood definition impacts measured exposure to neighborhood deprivation. Although exposure to neighborhood deprivation appears to be less sensitive to neighborhood definition in rural areas, researchers should apply theoretical reasoning to choose appropriate definitions of children's neighborhood.

Older adults' activity destinations before and during COVID-19 restrictions: From a variety of activities to mostly physical exercise close to home

The aim was to study various types of older adult's activity destinations (counts, frequency of visitation, and distance from home) in the pre-COVID-19 era, and to study prospectively how COVID-19-related regulations limiting mobility affected these. Using a map-based questionnaire, 75-85-year-old participants reported activity destinations, that is, any destinations for physical exercise, destinations facilitating one's outdoor mobility, and destinations for other activities, which they had visited several times during the past month. At baseline, a variety of activity destinations was reported, but during COVID-19, destinations reported markedly declined in number, they were reported predominantly for physical exercise, and they were located closer to home.

Measuring the association of objective and perceived neighborhood environment with physical activity in older adults: challenges and implications from a systematic review

BACKGROUND

A supportive environment is a key factor in addressing the issue of health among older adults. There is already sufficient evidence that objective and self-reported measures of the neighborhood environment should be taken into account as crucial components of active aging, as they have been shown to influence physical activity; particularly in people aged 60+. Thus, both could inform policies and practices that promote successful aging in place. An increasing number of studies meanwhile consider these exposures in analyzing their impact on physical activity in the elderly. However, there is a wide variety of definitions, measurements and methodological approaches, which complicates the process of obtaining comparable estimates of the effects and pooled results. The aim of this review was to identify and summarize these differences in order to emphasize methodological implications for future reviews and meta analyzes in this field and, thus, to create a sound basis for synthesized evidence.

METHODS

A systematic literature search across eight databases was conducted to identify peer-reviewed articles examining the association of objective and perceived measures of the neighborhood environment and objectively measured or self-reported physical activity in adults aged ≥ 60 years. Two authors independently screened the articles according to predefined eligibility criteria, extracted data, and assessed study quality. A qualitative synthesis of the findings is provided.

RESULTS

Of the 2967 records retrieved, 35 studies met the inclusion criteria. Five categories of methodological approaches, numerous measurement instruments to assess the neighborhood environment and physical activity, as well as several clusters of definitions of neighborhood, were identified.

CONCLUSIONS

The strength of evidence of the associations of specific categories of environmental attributes with physical activity varies across measurement types of the outcome and exposures as well as the physical activity domain observed and the operationalization of neighborhood. The latter being of great importance for the targeted age group. In the light of this, future reviews should consider these variations and stratify their summaries according to the different approaches, measures and definitions. Further, underlying mechanisms should be explored.

Neighbourhood effects on obesity: scoping review of time-varying outcomes and exposures in longitudinal designs

CONTEXT AND OBJECTIVES

Neighbourhood effect research on obesity took off in the early 2000s and was composed of mostly cross-sectional observational studies interested in various characteristics of the built environment and the socioeconomic environment. To limit biases related to self-selection and life course exposures, many researchers apply longitudinal designs in their studies. Until now, no review has specifically and exclusively examined longitudinal studies and the specific designs of these studies. In this review, we intend to answer the following research question: how are the temporal measurements of contextual exposure and obesity outcomes integrated into longitudinal studies that explore how neighbourhood-level built and socioeconomic environments impact adult obesity?

DESIGN

A systematic search strategy was designed to address the research question. The search was performed in Embase, Web of Science and PubMed, targeting scientific papers published before 1 January 2018. The eligible studies reported results on adults, included exposure that was limited to neighbourhood characteristics at the submunicipal level, included an outcome limited to obesity proxies, and reported a design with at least two exposure measurements or two outcome measurements.

RESULTS

This scoping review identified 66 studies that fit the eligibility criteria. A wide variety of neighbourhood characteristics were also measured, making it difficult to draw general conclusions about associations between neighbourhood exposure and obesity. We applied a typology that classified studies by whether exposure and outcome were measured as varying or fixed. Using this typology, we found that 32 studies reported both neighbourhood exposure and obesity outcomes that were varying in time; 28 reported varying outcomes but fixed exposures; and 6 had fixed outcomes and varying exposures.

CONCLUSION

Our typology illustrates the variety of longitudinal designs that were used in the selected studies. In the light of our results, we make recommendations on how to better report longitudinal designs and facilitate comparisons between studies.

Obesity and the Built Environment: A Reappraisal

The built environment (BE) has been viewed as an important determinant of health. Numerous studies have linked BE exposure, captured using a variety of methods, to diet quality and to area prevalence of obesity, diabetes, and cardiovascular disease. First-generation studies defined the neighborhood BE as the area around the home. Second-generation studies turned from home-centric to person-centric BE measures, capturing an individual's movements in space and time. Those studies made effective uses of global positioning system tracking devices and mobile phones, sometimes coupled with accelerometers and remote sensors. Activity space metrics explored travel paths, modes, and destinations to assess BE exposure that was both person and context specific. However, as measures of the contextual exposome have become ever more fine-grained and increasingly complex, connections to long-term chronic diseases with complex etiologies, such as obesity, are in danger of being lost. Furthermore, few studies on obesity and the BE have included intermediate energy balance behaviors, such as diet and physical activity, or explored the potential roles of social interactions or psychosocial pathways. Emerging survey-based applications that identify habitual destinations and associated travel patterns may become the third generation of tools to capture health-relevant BE exposures in the long term.

Impact of multiple food environments on body mass index

Background

Although the relationship between residential food environments and health outcomes have been extensively studied, the relationship between body mass index (BMI) and multiple food environments have not been fully explored. We examined the relationship between characteristics of three distinct food environments and BMI among elementary school employees in the metropolitan area of New Orleans, LA. We assessed the food environments around the residential and worksite neighborhoods and the commuting corridors.

Research methodology/principal findings

This study combined data from three different sources: individual and worksite data (ACTION), food retailer database (Dunn and Bradstreet), and the U.S. Census TIGER/Line Files. Spatial and hierarchical analyses were performed to explore the impact of predictors at the individual and environmental levels on BMI. When the three food environments were combined, the number of supermarkets and the number of grocery stores at residential food environment had a significant association with BMI (β = 0.56 and β = 0.24, p < 0.01), whereas the number of full-service restaurants showed an inverse relationship with BMI (β = -0.15, p < 0.001). For the commute corridor food environment, it was found that each additional fast-food restaurant in a vicinity of one kilometer traveled contributed to a higher BMI (β = 0.80, p <0.05), while adjusting for other factors. No statistical associations were found between BMI and worksite food environment.

Conclusions

The current study was the first to examine the relationship between BMI and food environments around residential neighborhoods, work neighborhoods, and the commuting corridor. Significant results were found between BMI and the availability of food stores around residential neighborhoods and the commuting corridor, adjusted for individual-level factors. This study expands the analysis beyond residential neighborhoods, illustrating the importance of multiple environmental factors in relation to BMI.

Environmental, Individual and Personal Goal Influences on Older Adults' Walking in the Helsinki Metropolitan Area

Physical activity is a fundamental factor in healthy ageing, and the built environment has been linked to individual health outcomes. Understanding the linkages between older adult’s walking and the built environment are key to designing supportive environments for active ageing. However, the variety of different spatial scales of human mobility has been largely overlooked in the environmental health research. This study used an online participatory mapping method and a novel modelling of individual activity spaces to study the associations between both the environmental and the individual features and older adults’ walking in the environments where older adult’s actually move around. Study participants (n = 844) aged 55+ who live in Helsinki Metropolitan Area, Finland reported their everyday errand points on a map and indicated which transport mode they used and how frequently they accessed the places. Respondents walking trips were drawn from the data and the direct and indirect effects of the personal, psychological as well as environmental features on older adults walking were examined. Respondents marked on average, six everyday errand points and walked for transport an average of 20 km per month. Residential density and the density of walkways, public transit stops, intersections and recreational sports places were significantly and positively associated with older adult’s walking for transport. Transit stop density was found having the largest direct effect to older adults walking. Built environment had an independent effect on older adults walking regardless of individual demographic or psychological features. Education and personal goals related to physical activities had a direct positive, and income a direct negative, effect on walking. Gender and perceived health had an indirect effect on walking, which was realized through individuals’ physical activity goals.

Capturing exposure in environmental health research: challenges and opportunities of different activity space models

Background

The built environment health promotion has attracted notable attention across a wide spectrum of health-related research over the past decade. However, the results about the contextual effects on health and PA are highly heterogeneous. The discrepancies between the results can potentially be partly explained by the diverse use of different spatial units of analysis in assessing individuals’ exposure to various environment characteristics. This study investigated whether different residential and activity space units of analysis yield distinct results regarding the association between the built environment and health. In addition, this study examines the challenges and opportunities of the different spatial units of analysis for environmental health-related research.

Methods

Two common residential units of analysis and two novel activity space models were used to examine older adults’ wellbeing in relation to the built environment features in the Helsinki Metropolitan Area, Finland. An administrative unit, 500 m residential buffer, home range model and individualized residential exposure model were used to assess the associations between the built environment and wellbeing of respondent’s (n = 844).

Results

All four different spatial units of analysis yield distinct results regarding the associations between the built environment characteristics and wellbeing. A positive association between green space and health was found only when exposure was assessed with individualized residential exposure model. Walkability index and the length of pedestrian and bicycle roads were found to positively correlate with perceived wellbeing measures only with a home range model. Additionally, all units of analysis differed from each other in terms of size, shape, and how they capture different contextual measures.

Conclusions

The results show that different spatial units of analysis result in considerably different measurements of built environment. In turn, the differences derived from the use of different spatial units seem to considerably affect the associations between environment characteristics and wellbeing measures. Although it is not easy to argue about the correctness of these measurements, what is evident is that they can reveal different wellbeing outcomes. While some methods are especially usable to determine the availability of environmental opportunities that promote active travel and the related health outcomes, others can provide us with insight into the mechanisms how the actual exposure to green structure can enhance wellbeing.

Using Individual GPS Trajectories to Explore Foodscape Exposure: A Case Study in Beijing Metropolitan Area

With the growing interest in studying the characteristics of people’s access to the food environment and its influence upon individual health, there has been a focus on assessing individual food exposure based on GPS trajectories. However, existing studies have largely focused on the overall activity space using short-period trajectories, which ignores the complexity of human movements and the heterogeneity of the spaces that are experienced by the individual over daily life schedules. In this study, we propose a novel framework to extract the exposure areas consisting of the localized activity spaces around daily life centers and non-motorized commuting routes from long-term GPS trajectories. The newly proposed framework is individual-specific and can incorporate the internal heterogeneity of individual activities (spatial extent, stay duration, and timing) in different places as well as the dynamics of the context. A pilot study of the GeoLife dataset suggests that there are significant variations in the magnitude as well as the composition of the food environment in different parts of the individual exposure area, and residential environment is not representative of the overall foodscape exposure.

Kids in space: Measuring children's residential neighborhoods and other destinations using activity space GPS and wearable camera data

INTRODUCTION

Defining the boundary of children's 'neighborhoods' has important implications for understanding the contextual influences on child health. Additionally, insight into activities that occur outside people's neighborhoods may indicate exposures that place-based studies cannot detect. This study aimed to 1) extend current neighborhood research, using data from wearable cameras and GPS devices that were worn over several days in an urban setting; 2) define the boundary of children's neighborhoods by using leisure time activity space data; and 3) determine the destinations visited by children in their leisure time, outside their neighborhoods.

METHOD

One hundred and fourteen children (mean age 12y) from Wellington, New Zealand wore wearable cameras and GPS recorders. Residential Euclidean buffers at incremental distances were paired with GPS data (thereby identifying time spent in different places) to explore alternative definitions of neighborhood boundaries. Children's neighborhood boundary was at 500 m. A newly developed software application was used to identify 'destinations' visited outside the neighborhood by specifying space-time parameters. Image data from wearable cameras were used to determine the type of destination.

RESULTS

Children spent over half of their leisure time within 500 m of their homes. Children left their neighborhood predominantly to visit school (for leisure purposes), other residential locations (e.g. to visit friends) and food retail outlets (e.g. convenience stores, fast food outlets). Children spent more time at food retail outlets than at structured sport and in outdoor recreation locations combined.

CONCLUSION

Person-centered neighborhood definitions may serve to better represent children's everyday experiences and neighborhood exposures than previous methods based on place-based measures. As schools and other residential locations (friends and family) are important destinations outside the neighborhood, such destinations should be taken into account. The combination of image data and activity space GPS data provides a more robust approach to understanding children's neighborhoods and activity spaces.

The Impact of Neighborhood Conditions and Psychological Distress on Preterm Birth in African-American Women

OBJECTIVE

Prior research suggests that adverse neighborhood conditions are related to preterm birth. One potential pathway by which neighborhood conditions increase the risk for preterm birth is by increasing women's psychological distress. Our objective was to examine whether psychological distress mediated the relationship between neighborhood conditions and preterm birth.

DESIGN AND SAMPLE

One hundred and one pregnant African-American women receiving prenatal care at a medical center in Chicago participated in this cross-sectional design study.

MEASURES

Women completed the self-report instruments about their perceived neighborhood conditions and psychological distress between 15-26 weeks gestation. Objective measures of the neighborhood were derived using geographic information systems (GIS). Birth data were collected from medical records.

RESULTS

Perceived adverse neighborhood conditions were related to psychological distress: perceived physical disorder (r = .26, p = .01), perceived social disorder (r = .21, p = .03), and perceived crime (r = .30, p = .01). Objective neighborhood conditions were not related to psychological distress. Psychological distress mediated the effects of perceived neighborhood conditions on preterm birth.

CONCLUSIONS

Psychological distress in the second trimester mediated the effects of perceived, but not objective, neighborhood conditions on preterm birth. If these results are replicable in studies with larger sample sizes, intervention strategies could be implemented at the individual level to reduce psychological distress and improve women's ability to cope with adverse neighborhood conditions.

Physical Limitations, Walkability, Perceived Environmental Facilitators and Physical Activity of Older Adults in Finland

The aim was to study objectively assessed walkability of the environment and participant perceived environmental facilitators for outdoor mobility as predictors of physical activity in older adults with and without physical limitations. 75–90-year-old adults living independently in Central Finland were interviewed (n = 839) and reassessed for self-reported physical activity one or two years later (n = 787). Lower-extremity physical limitations were defined as Short Physical Performance Battery score ≤9. Number of perceived environmental facilitators was calculated from a 16-item checklist. Walkability index (land use mix, street connectivity, population density) of the home environment was calculated from geographic information and categorized into tertiles. Accelerometer-based step counts were registered for one week (n = 174). Better walkability was associated with higher numbers of perceived environmental facilitators (p < 0.001) and higher physical activity (self-reported p = 0.021, step count p = 0.010). Especially among those with physical limitations, reporting more environmental facilitators was associated with higher odds for reporting at least moderate physical activity (p < 0.001), but not step counts. Perceived environmental facilitators only predicted self-reported physical activity at follow-up. To conclude, high walkability of the living environment provides opportunities for physical activity in old age, but among those with physical limitations especially, awareness of environmental facilitators may be needed to promote physical activity.

Geographic regions for assessing built environmental correlates with walking trips: A comparison using different metrics and model designs

There is growing international evidence that supportive built environments encourage active travel such as walking. An unsettled question is the role of geographic regions for analyzing the relationship between the built environment and active travel. This paper examines the geographic region question by assessing walking trip models that use two different regions: walking activity spaces and self-defined neighborhoods. We also use two types of built environment metrics, perceived and audit data, and two types of study design, cross-sectional and longitudinal, to assess these regions. We find that the built environment associations with walking are dependent on the type of metric and the type of model. Audit measures summarized within walking activity spaces better explain walking trips compared to audit measures within self-defined neighborhoods. Perceived measures summarized within self-defined neighborhoods have mixed results. Finally, results differ based on study design. This suggests that results may not be comparable among different regions, metrics and designs; researchers need to consider carefully these choices when assessing active travel correlates.

Quantifying spatial misclassification in exposure to noise complaints among low-income housing residents across New York City neighborhoods: a Global Positioning System (GPS) study

PURPOSE

To examine if there was spatial misclassification in exposure to neighborhood noise complaints among a sample of low-income housing residents in New York City, comparing home-based spatial buffers and Global Positioning System (GPS) daily path buffers.

METHODS

Data came from the community-based NYC Low-Income Housing, Neighborhoods and Health Study, where GPS tracking of the sample was conducted for a week (analytic n = 102). We created a GPS daily path buffer (a buffering zone drawn around GPS tracks) of 200 m and 400 m. We also used home-based buffers of 200 m and 400 m. Using these "neighborhoods" (or exposure areas), we calculated neighborhood exposure to noisy events from 311 complaints data (analytic n = 143,967). Friedman tests (to compare overall differences in neighborhood definitions) were applied.

RESULTS

There were differences in neighborhood noise complaints according to the selected neighborhood definitions (P < .05). For example, the mean neighborhood noise complaint count was 1196 per square kilometer for the 400-m home-based and 812 per square kilometer for the 400-m activity space buffer, illustrating how neighborhood definition influences the estimates of exposure to neighborhood noise complaints.

CONCLUSIONS

These analyses suggest that, whenever appropriate, GPS neighborhood definitions can be used in spatial epidemiology research in spatially mobile populations to understand people's lived experience.