An Analytical Framework for Integrating the Spatiotemporal Dynamics of Environmental Context and Individual Mobility in Exposure Assessment: A Study on the Relationship between Food Environment Exposures and Body Weight

Global positioning system-based food environment exposures, diet-related, and cardiometabolic health outcomes: a systematic review and research agenda

BACKGROUND

Geographic access to food may affect dietary choices and health outcomes, but the strength and direction of associations may depend on the operationalization of exposure measures. We aimed to systematically review the literature on up-to-date evidence on the association between food environment exposures based on Global Positioning System (GPS) and diet-related and cardiometabolic health outcomes.

METHODS

The databases PubMed, Embase.com, APA PsycInfo (via Ebsco), Cinahl (via Ebsco), the Web of Science Core Collection, Scopus, and the International Bibliography of the Social Sciences (via ProQuest) were searched from inception to October 31, 2022. We included studies that measured the activity space through GPS tracking data to identify exposure to food outlets and assessed associations with either diet-related or cardiometabolic health outcomes. Quality assessment was evaluated using the criteria from a modified version of the Newcastle-Ottawa Scale (NOS) for cross-sectional studies. We additionally used four items from a quality assessment tool to specifically assess the quality of GPS measurements.

RESULTS

Of 2949 studies retrieved, 14 studies fulfilled our inclusion criteria. They were heterogeneous and represent inconsistent evidence. Yet, three studies found associations between food outlets and food purchases, for example, more exposure to junk food outlets was associated with higher odds of junk food purchases. Two studies found associations between greater exposure to fast food outlets and higher fast food consumption and out of three studies that investigated food environment in relation to metabolic outcomes, two studies found that higher exposure to an unhealthy food environment was associated with higher odds of being overweight.

CONCLUSIONS

The current and limited evidence base does not provide strong evidence for consistent associations of GPS-based exposures of the food environment with diet-related and cardiometabolic health outcomes.

Systematic review of best practices for GPS data usage, processing, and linkage in health, exposure science and environmental context research

Global Positioning System (GPS) technology is increasingly used in health research to capture individual mobility and contextual and environmental exposures. However, the tools, techniques and decisions for using GPS data vary from study to study, making comparisons and reproducibility challenging.

OBJECTIVES

The objectives of this systematic review were to (1) identify best practices for GPS data collection and processing; (2) quantify reporting of best practices in published studies; and (3) discuss examples found in reviewed manuscripts that future researchers may employ for reporting GPS data usage, processing and linkage of GPS data in health studies.

DESIGN

A systematic review.

DATA SOURCES

Electronic databases searched (24 October 2023) were PubMed, Scopus and Web of Science (PROSPERO ID: CRD42022322166).

ELIGIBILITY CRITERIA

Included peer-reviewed studies published in English met at least one of the criteria: (1) protocols involving GPS for exposure/context and human health research purposes and containing empirical data; (2) linkage of GPS data to other data intended for research on contextual influences on health; (3) associations between GPS-measured mobility or exposures and health; (4) derived variable methods using GPS data in health research; or (5) comparison of GPS tracking with other methods (eg, travel diary).

DATA EXTRACTION AND SYNTHESIS

We examined 157 manuscripts for reporting of best practices including wear time, sampling frequency, data validity, noise/signal loss and data linkage to assess risk of bias.

RESULTS

We found that 6% of the studies did not disclose the GPS device model used, only 12.1% reported the per cent of GPS data lost by signal loss, only 15.7% reported the per cent of GPS data considered to be noise and only 68.2% reported the inclusion criteria for their data.

CONCLUSIONS

Our recommendations for reporting on GPS usage, processing and linkage may be transferrable to other geospatial devices, with the hope of promoting transparency and reproducibility in this research.

PROSPERO REGISTRATION NUMBER

CRD42022322166.

Measuring environmental exposures in people's activity space: The need to account for travel modes and exposure decay

BACKGROUND

Accurately quantifying people's out-of-home environmental exposure is important for identifying disease risk factors. Several activity space-based exposure assessments exist, possibly leading to different exposure estimates, and have neither considered individual travel modes nor exposure-related distance decay effects.

OBJECTIVE

We aimed (1) to develop an activity space-based exposure assessment approach that included travel modes and exposure-related distance decay effects and (2) to compare the size of such spaces and the exposure estimates derived from them across typically used activity space operationalizations.

METHODS

We used 7-day-long global positioning system (GPS)-enabled smartphone-based tracking data of 269 Dutch adults. People's GPS trajectory points were classified into passive and active travel modes. Exposure-related distance decay effects were modeled through linear, exponential, and Gaussian decay functions. We performed cross-comparisons on these three functional decay models and an unweighted model in conjunction with four activity space models (i.e., home-based buffers, minimum convex polygons, two standard deviational ellipses, and time-weighted GPS-based buffers). We applied non-parametric Kruskal-Wallis tests, pair-wise Wilcoxon signed-rank tests, and Spearman correlations to assess mean differences in the extent of the activity spaces and correlations across exposures to particulate matter (PM2.5), noise, green space, and blue space.

RESULTS

Participants spent, on average, 42% of their daily life out-of-home. We observed that including travel modes into activity space delineation resulted in significantly more compact activity spaces. Exposure estimates for PM2.5 and blue space were significantly (p < 0.05) different between exposure estimates that did or did not account for travel modes, unlike noise and green space, for which differences did not reach significance. While the inclusion of distance decay effects significantly affected noise and green space exposure assessments, the decay functions applied appear not to have had any impact on the results. We found that residential exposure estimates appear appropriate for use as proxy values for the overall amount of PM2.5 exposure in people's daily lives, while GPS-based assessments are suitable for noise, green space, and blue space.

SIGNIFICANCE

For some exposures, the tested activity space definitions, although significantly correlated, exhibited differing exposure estimate results based on inclusion or exclusion of travel modes or distance decay effect. Results only supported using home-based buffer values as proxies for individuals' daily short-term PM2.5 exposure.

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.

Non-traditional data sources in obesity research: a systematic review of their use in the study of obesogenic environments

BACKGROUND

The complex nature of obesity increasingly requires a comprehensive approach that includes the role of environmental factors. For understanding contextual determinants, the resources provided by technological advances could become a key factor in obesogenic environment research. This study aims to identify different sources of non-traditional data and their applications, considering the domains of obesogenic environments: physical, sociocultural, political and economic.

METHODS

We conducted a systematic search in PubMed, Scopus and LILACS databases by two independent groups of reviewers, from September to December 2021. We included those studies oriented to adult obesity research using non-traditional data sources, published in the last 5 years in English, Spanish or Portuguese. The overall reporting followed the PRISMA guidelines.

RESULTS

The initial search yielded 1583 articles, 94 articles were kept for full-text screening, and 53 studies met the eligibility criteria and were included. We extracted information about countries of origin, study design, observation units, obesity-related outcomes, environment variables, and non-traditional data sources used. Our results revealed that most of the studies originated from high-income countries (86.54%) and used geospatial data within a GIS (76.67%), social networks (16.67%), and digital devices (11.66%) as data sources. Geospatial data were the most utilised data source and mainly contributed to the study of the physical domains of obesogenic environments, followed by social networks providing data to the analysis of the sociocultural domain. A gap in the literature exploring the political domain of environments was also evident.

CONCLUSION

The disparities between countries are noticeable. Geospatial and social network data sources contributed to studying the physical and sociocultural environments, which could be a valuable complement to those traditionally used in obesity research. We propose the use of information available on the Internet, addressed by artificial intelligence-based tools, to increase the knowledge on political and economic dimensions of the obesogenic environment.

The Mann-Kendall-Sneyers test to identify the change points of COVID-19 time series in the United States

Background

One critical variable in the time series analysis is the change point, which is the point where an abrupt change occurs in chronologically ordered observations. Existing parametric models for change point detection, such as the linear regression model and the Bayesian model, require that observations are normally distributed and that the trend line cannot have extreme variability. To overcome the limitations of the parametric model, we apply a nonparametric method, the Mann-Kendall-Sneyers (MKS) test, to change point detection for the state-level COVID-19 case time series data of the United States in the early outbreak of the pandemic.

Methods

The MKS test is implemented for change point detection. The forward sequence and the backward sequence are calculated based on the new weekly cases between March 22, 2020 and January 31, 2021 for each of the 50 states. Points of intersection between the two sequences falling within the 95% confidence intervals are identified as the change points. The results are compared with two other change point detection methods, the pruned exact linear time (PELT) method and the regression-based method. Also, an open-access tool by Microsoft Excel is developed to facilitate the model implementation.

Results

By applying the MKS test to COVID-19 cases in the United States, we have identified that 30 states (60.0%) have at least one change point within the 95% confidence intervals. Of these states, 26 states have one change point, 4 states (i.e., LA, OH, VA, and WA) have two change points, and one state (GA) has three change points. Additionally, most downward changes appear in the Northeastern states (e.g., CT, MA, NJ, NY) at the first development stage (March 23 through May 31, 2020); most upward changes appear in the Western states (e.g., AZ, CA, CO, NM, WA, WY) and the Midwestern states (e.g., IL, IN, MI, MN, OH, WI) at the third development stage (November 19, 2020 through January 31, 2021).

Conclusions

This study is among the first to explore the potential of the MKS test applied for change point detection of COVID-19 cases. The MKS test is characterized by several advantages, including high computational efficiency, easy implementation, the ability to identify the change of direction, and no assumption for data distribution. However, due to its conservative nature in change point detection and moderate agreement with other methods, we recommend using the MKS test primarily for initial pattern identification and data pruning, especially in large data. With modification, the method can be further applied to other health data, such as injuries, disabilities, and mortalities.

Spatial Differences in the Effect of Communities' Built Environment on Residents' Health: A Case Study in Wuhan, China

After 40 years of reform and opening-up policies, urbanization in China has significantly improved residents' living standards; however, simultaneously, it has caused a series of health problems among Chinese citizens. Communities' built environment is closely related to their residents' health. However, few studies have examined the spatial differences in the health effects of community-built environments. Based on a 2013 health survey of residents in 20 communities in Wuhan, this study uses multilevel linear models to explore the effects of the built environment on residents' health, analyzing the differences in its health-effect within different types of communities. The results showed that there were significant differences in the self-rated health status of residents in different communities, with those in high-end communities reporting a higher self-rated health status. The effect of the built environment on the health of residents in different communities was found to be inconsistent. For instance, the effect of the built environment on low-end community residents was very significant, but it was not obvious for residents in high-end communities. There are significant community-specific differences in the health- effect of the built environment: in high-end communities, residents' health status was mainly restricted by travel accessibility, while in low-end communities, residents' health status was mainly restricted by the accessibility of health facilities. Therefore, this paper proposes a built-environment optimization strategy for different types of communities to provide valuable insights for healthy community planning from a policy perspective.

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.

An Integrated Individual Environmental Exposure Assessment System for Real-Time Mobile Sensing in Environmental Health Studies

The effects of environmental exposure on human health have been widely explored by scholars in health geography for decades. However, recent advances in geospatial technologies, especially the development of mobile approaches to collecting real-time and high-resolution individual data, have enabled sophisticated methods for assessing people’s environmental exposure. This study proposes an individual environmental exposure assessment system (IEEAS) that integrates objective real-time monitoring devices and subjective sensing tools to provide a composite way for individual-based environmental exposure data collection. With field test data collected in Chicago and Beijing, we illustrate and discuss the advantages of the proposed IEEAS and the composite analysis that could be applied. Data collected with the proposed IEEAS yield relatively accurate measurements of individual exposure in a composite way, and offer new opportunities for developing more sophisticated ways to measure individual environmental exposure. With the capability to consider both the variations in environmental risks and human mobility in high spatial and temporal resolutions, the IEEAS also helps mitigate some uncertainties in environmental exposure assessment and thus enables a better understanding of the relationship between individual environmental exposure and health outcomes.

Mobility during the COVID-19 Pandemic: A Data-Driven Time-Geographic Analysis of Health-Induced Mobility Changes

The COVID-19 pandemic has profoundly affected the spatial mobility of a major part of the population in many countries. For most people, this was an extremely disruptive shock, resulting in loss of income, social contact and quality of life. However, forced to reduce human physical interaction, most businesses, individuals and households developed new action lines and routines, and were gradually learning to adapt to the new reality. Some of these changes might result in long-term changes in opportunity structures and in spatial preferences for working, employment or residential location choice, and for mobility behavior. In this paper we aim to extend the time-geographic approach to analyzing people’s spatial activities, by focusing on health-related geographical mobility patterns during the pandemic in Sweden. Starting from a micro-approach at individual level and then looking at an aggregate urban scale, we examine the space-time geography during the coronavirus pandemic, using Hägerstrand’s time-geography model. We utilize a massive but (location-wise) fuzzy dataset to analyze aggregate spatiotemporal impacts of the COVID-19 pandemic using a contemporary time-geographical approach. First, we address micro-level behavior in time-space to understand the mechanisms of change and to illustrate that a temporal drastic change in human mobility seems to be plausible. Then we analyze the changes in individuals’ mobility by analyzing their activity spaces in aggregate using mobile phone network data records. Clearly, it is too early for predicting long-term spatial changes, but a clear heterogeneity in spatial behavior can already be detected. It seems plausible that the corona pandemic may have long-lasting effects on employment centers, city roles and spatial mobility patterns.

Extracting Stops from Spatio-Temporal Trajectories within Dynamic Contextual Features

Identifying stops from GPS trajectories is one of the main concerns in the study of moving objects and has a major effect on a wide variety of location-based services and applications. Although the spatial and non-spatial characteristics of trajectories have been widely investigated for the identification of stops, few studies have concentrated on the impacts of the contextual features, which are also connected to the road network and nearby Points of Interest (POIs). In order to obtain more precise stop information from moving objects, this paper proposes and implements a novel approach that represents a spatio-temproal dynamics relationship between stopping behaviors and geospatial elements to detect stops. The relationship between the candidate stops based on the standard time–distance threshold approach and the surrounding environmental elements are integrated in a complex way (the mobility context cube) to extract stop features and precisely derive stops using the classifier classification. The methodology presented is designed to reduce the error rate of detection of stops in the work of trajectory data mining. It turns out that 26 features can contribute to recognizing stop behaviors from trajectory data. Additionally, experiments on a real-world trajectory dataset further demonstrate the effectiveness of the proposed approach in improving the accuracy of identifying stops from trajectories.

Daily activity locations k-anonymity for the evaluation of disclosure risk of individual GPS datasets

Background

Personal privacy is a significant concern in the era of big data. In the field of health geography, personal health data are collected with geographic location information which may increase disclosure risk and threaten personal geoprivacy. Geomasking is used to protect individuals’ geoprivacy by masking the geographic location information, and spatial k-anonymity is widely used to measure the disclosure risk after geomasking is applied. With the emergence of individual GPS trajectory datasets that contains large volumes of confidential geospatial information, disclosure risk can no longer be comprehensively assessed by the spatial k-anonymity method.

Methods

This study proposes and develops daily activity locations (DAL) k-anonymity as a new method for evaluating the disclosure risk of GPS data. Instead of calculating disclosure risk based on only one geographic location (e.g., home) of an individual, the new DAL k-anonymity is a composite evaluation of disclosure risk based on all activity locations of an individual and the time he/she spends at each location abstracted from GPS datasets. With a simulated individual GPS dataset, we present case studies of applying DAL k-anonymity in various scenarios to investigate its performance. The results of applying DAL k-anonymity are also compared with those obtained with spatial k-anonymity under these scenarios.

Results

The results of this study indicate that DAL k-anonymity provides a better estimation of the disclosure risk than does spatial k-anonymity. In various case-study scenarios of individual GPS data, DAL k-anonymity provides a more effective method for evaluating the disclosure risk by considering the probability of re-identifying an individual’s home and all the other daily activity locations.

Conclusions

This new method provides a quantitative means for understanding the disclosure risk of sharing or publishing GPS data. It also helps shed new light on the development of new geomasking methods for GPS datasets. Ultimately, the findings of this study will help to protect individual geoprivacy while benefiting the research community by promoting and facilitating geospatial data sharing.

Who Could Not Avoid Exposure to High Levels of Residence-Based Pollution by Daily Mobility? Evidence of Air Pollution Exposure from the Perspective of the Neighborhood Effect Averaging Problem (NEAP)

It has been widely acknowledged that air pollution has a considerable adverse impact on people’s health. Disadvantaged groups such as low-income people are often found to experience greater negative effects of environmental pollution. Thus, improving the accuracy of air pollution exposure assessment might be essential to policy-making. Recently, the neighborhood effect averaging problem (NEAP) has been identified as a specific form of possible bias when assessing individual exposure to air pollution and its health impacts. In this paper, we assessed the real-time air pollution exposure and residential-based exposure of 106 participants in a high-pollution community in Beijing, China. The study found that: (1) there are significant differences between the two assessments; (2) most participants experienced the NEAP and could lower their exposure by their daily mobility; (3) three vulnerable groups with low daily mobility and could not avoid the high pollution in their residential neighborhoods were identified as exceptions to this: low-income people who have low levels of daily mobility and limited travel outside their residential neighborhoods, blue-collar workers who spend long hours at low-end workplaces, and elderly people who face many household constraints. Public policies thus need to focus on the hidden environmental injustice revealed by the NEAP in order to improve the well-being of these environmentally vulnerable groups.

Contraband Cigarette Purchasing from First Nation reserves in Ontario and Quebec: Findings from the 2002-2014 ITC Canada Survey

Backround: The availability of contraband cigarettes provides incentives for price-sensitive smokers to reduce their monetary costs of smoking. The objectives of this study were to examine whether Canadian smokers' geographic proximity to First Nations reserves and attempts to quit smoking influenced the likelihood of purchasing lower-cost cigarettes from reserves.

METHODS

Data were from the International Tobacco Control (ITC) Canada Survey, a prospective survey of Canadian adult smokers conducted from 2002 to 2014 using telephone and online interviewing methods. Analysis was restricted to smokers from Ontario (n=2105) and Quebec (n=1427) participating in at least one survey wave. Smokers' postal codes were used to calculate distance to the nearest reserve. Weighted logistic generalised estimating equations (GEE) regression examined the linear relationship between distance and the log odds of last purchasing cigarettes on reserve in each province. GEE models also examined the relationship between past-year quit attempts and the log odds of on-reserve purchasing.

RESULTS

Controlling for other factors, from 2002-2014, smokers from Ontario who lived 10 km closer to reserves than otherwise similar smokers had significantly higher odds of last purchasing on reserve (OR ranged from 1.16 to 1.65). Distance had little effect on smokers' purchasing behaviours in Quebec. Moreover, in Ontario, for every 10 km increase in distance, smokers who did not try to quit had significantly greater odds of purchasing from a reserve than smokers who tried to quit (p=0.002).

CONCLUSION

In order for tobacco taxation policies to achieve their maximal benefit, governments must limit potential sources of lower-cost cigarettes. Collaborative governance arrangements can ensure tobacco products sold on reserve to non-Indigenous people are appropriately taxed while allowing First Nations communities to keep the revenue generated by such taxes.

The Effects of GPS-Based Buffer Size on the Association between Travel Modes and Environmental Contexts

To investigate the association between physical activity (including active travel modes) and environmental factors, much research has estimated contextual influences based on zones or areas delineated with buffer analysis. However, few studies to date have examined the effects of different buffer sizes on estimates of individuals’ dynamic exposures along their daily trips recorded as GPS trajectories. Thus, using a 7-day GPS dataset collected in the Chicago Regional Household Travel Inventory (CRHTI) Survey, this study addresses the methodological issue of how the associations between environmental contexts and active travel modes (ATMs) as a subset of physical activity vary with GPS-based buffer size. The results indicate that buffer size influences such associations and the significance levels of the seven environmental factors selected as predictors. Further, the findings on the effects of buffer size on such associations and the significance levels are clearly different between the ATMs of walking and biking. Such evidence of the existence of buffer-size effects for multiple environmental factors not only confirms the importance of the uncertain geographic context problem (UGCoP) but provides a resounding cautionary note to all future research on human mobility involving individuals’ GPS trajectories, including studies on physical activity and travel behaviors, especially on the reliable estimation of individual exposures to environmental factors and their health outcomes.