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

How mobility pattern shapes the association between static green space and dynamic green space exposure

Greenspaces are crucial for enhancing mental and physical health. Recent research has shifted from static methods of assessing exposure to greenspaces, based on fixed locations, to dynamic approaches that account for individual mobility. These dynamic evaluations utilize advanced technologies like GPS tracking and remote sensing to provide more precise exposure estimates. However, little work has been conducted to compare dynamic and static exposure assessments and the effect of individual mobility on these evaluations. This study delves into how greenspaces around homes and workplaces, along with mobility patterns, affect dynamic greenspace exposure in Hong Kong. Data was collected from 787 participants in four communities in Hong Kong using GPS, portable sensors, and surveys. Using multiple statistical tests, our study revealed significant variations in participants' daily mobility patterns across socio-demographic and temporal factors. Further, using linear mixed-effects models, we identified complex and statistically significant interactions between participants' static greenspace exposure and their mobility patterns. Our findings suggest that individual mobility patterns significantly modify the relationship between static and dynamic greenspace exposure and play a critical role in explaining socio-demographic and temporal context differences in the relationship between static and dynamic greenspace exposure.

Proximity to public green spaces and depressive symptoms among South African residents: a population-based study

BACKGROUND

Exposure to green spaces has been suggested to improve mental health and may reduce the risk of depression. However, there is generally limited evidence on the association between green spaces and depression originating from low-and middle-income countries and Africa in particular. Here, we investigate the association between proximity to public green spaces and depressive symptoms among residents of Gauteng Province, South Africa.

METHODS

We used data from the 2017/2018 Gauteng quality of life survey. We included all individuals aged 18 years or older residing in the nine municipalities of Gauteng Province that completed the survey (n = 24,341). Depressive symptoms were assessed using the Patient Health Questionnaire-2. Proximity to public green spaces was defined as self-reported walking time (either less or greater than 15 min) from individuals' homes to the nearest public green space. To assess the association between access to public green spaces and depressive symptoms, we used mixed-effects models, adjusted for age, sex, population group (African, Indian/Asian, Coloured (mixed race), and White), educational attainment, and municipality. We additionally performed stratified analyses by age, sex, educational attainment, and population group to evaluate whether associations differed within subgroups. Associations are expressed as prevalence ratios (PR) and their 95% confidence intervals (95% CI).

RESULTS

We observed a 6% (PR = 0.94, 95%CI = 0.92-0.96) prevalence reduction in depressive symptoms for individuals who reported that the nearest public green space was less than 15 min from their homes as compared to those who reported > 15 min. After stratification, this inverse association was stronger among females, individuals aged 35-59 years,those with higher levels of educational attainment, and Coloured individuals as compared to their counterparts.

CONCLUSION

Our findings suggest that public green spaces close to residential homes may be associated with a reduction in the occurrence of depressive symptoms among urban populations in resource-constrained settings like South Africa.

GPS-derived environmental exposures during pregnancy and early postpartum - Evidence from the madres cohort

The built and natural environment factors (e.g., greenspace, walkability) are associated with maternal and infant health during and after pregnancy. Most pregnancy studies assess exposures to environmental factors via static methods (i.e., residential location at a single point in time, usually 3rd trimester). These do not capture dynamic exposures encountered in activity spaces (e.g., locations one visits and paths one travels) and their changes over time. In this study, we aimed to compare daily environmental exposure estimates using residential and global positioning systems (GPS)-measured activity space approaches and evaluated potential for exposure measurement error in the former. To do this, we collected four days of continuous geolocation monitoring during the 1st and 3rd trimesters of pregnancy and at 4-6 months postpartum in sixty-two pregnant Hispanic women enrolled in the MADRES cohort. We applied residential and GPS-based methods to assess daily exposures to greenspace, access to parks and transit, and walkability, respectively. We assessed potential for exposure measurement error in residential vs GPS-based estimates using Pearson correlations for each measure overall and by study period. We found residential and GPS-based estimates of daily exposure to total areas of parks and open spaces were weakly positively correlated (r = 0.31, P < .001) across pregnancy and postpartum periods. Residential estimates of %greenspace (r = 0.52, P < .001) and tree cover (r = 0.55, P < .001) along walkable roads were moderately correlated with GPS-based estimates. Residential and GPS-based estimates of public transit proximity, pedestrian-oriented intersection density, and walkability index score were all highly positively correlated (r > 0.70, P < .001). We also found associations between residential and GPS-based estimates decreased among participants with greater daily mobility. Our findings suggest the popular approach that assessing the built and natural environment exposures using residential methods at one time point may introduce exposure measurement error in pregnancy studies. GPS-based methods, to the extent feasible, are recommended for future studies.

Revisiting the impact of public spaces on the mental health of rural migrants in Wuhan: an integrated multi-source data analysis

Current research on public spaces and mental health often focuses on the independent relationship of one or more social mediators, neglecting the nuanced implications and serial mechanisms inherent in the progressive social process. Using Wuhan city, China, as a study case with multi-source data, this research applies Multilevel Generalized Structural Equation Modeling and deep learning techniques to explore the differential effects of public spaces with varying degrees of publicness (i.e., typical, semi-, and privately owned) on rural migrants' mental health. Crucially, this study scrutinizes both explicit (social interaction) and implicit (perceived integration) social mechanisms to revisit the relationships. The findings reveal that not all public spaces equally influence mental health, with typical and privately owned public spaces conferring profound benefits. Notably, public spaces impact mental health chiefly through perceived integration instead of through direct social interaction. Social interaction improves mental health primarily by enhancing perceived integration, suggesting that meaningful connections beyond superficial encounters are critical. In particular, we observed significant social effects in typical and privately owned public spaces but limited social functionality in semi-public spaces. This evidence contributes to the knowledge required to create supportive social environments within public spaces, integral to nurturing inclusive urban development.

Dynamic greenspace exposure, individual mental health status and momentary stress level: A study using multiple greenspace measurements

Previous research on the relationship between greenspace exposure and mental health has largely taken a residence-based approach to exposure assessment, ignoring the dynamic nature of people's daily movements. Moreover, most studies evaluated greenspace from an overhead perspective, whereas an eye-level perspective could potentially offer a more comprehensive understanding of individuals' encounters with greenspaces. Based on our survey in two communities in Hong Kong (Sham Shui Po and Tin Shui Wai), we captured people's eye-level greenspace exposure based on their travel routes and visited places using GPS trajectories, streetscape images, and deep learning methods. We then compared the results with those obtained with an overhead greenness exposure measure (the normalized difference vegetation index [NDVI]). The results indicate that these two greenspace measurements are not associated with each other, implying that they encompass distinct facets of greenspace, which may have different effects on mental health. Further, we examined the associations between various greenspace exposure measures and mental health using GPS trajectories and ecological momentary assessment data. The results reveal a negative association between eye-level greenspace exposure and momentary stress, while no similar association was observed when using the top-down NDVI as an indicator of greenspace exposure. Moreover, compared to the total volume of greenspace exposure, the distance-weighted average of greenspace exposure based on dynamic mobility contexts has a stronger association with individual overall mental health. Lastly, the relationship between greenspace exposure and mental health varies between the two communities with different socio-economic attributes. The study indicates that policymakers should focus not only on residential neighborhoods and overhead greenspace but also consider the dynamic environments and socio-economic contexts that people are embedded in.

Assessment of air pollution and air quality perception mismatch using mobility-based real-time exposure

Air pollution poses a threat to human health. Public perceptions of air pollution are important for individual self-protection and policy-making. Given the uncertainty faced by residence-based exposure (RB) measurements, this study measures individuals' real-time mobility-based (MB) exposures and perceptions of air pollution by considering people's daily movement. It explores how contextual uncertainties may influence the disparities in perceived air quality by taking into account RB and MB environmental factors. In addition, we explore factors that are related to the mismatch between people's perceived air quality and actual air pollution exposure. Using K-means clustering to divide the PM2.5 values into two groups, a mismatch happens when the perceived air quality is poor but the air pollution level is lower than 15.536μg/m3 and when the perceived air quality is good but the air pollution level is higher than 15.608μg/m3. The results show that there is a mismatch between air pollution exposure and perception of air pollution. People with low income are exposed to higher air pollution. Unemployed people and people with more serious mental health symptoms (e.g., depression) have a higher chance of accurately assessing air pollution (e.g., perceiving air quality as poor when air pollution levels are high). Older people and those with a higher MB open space density tend to underestimate air pollution. Students tend to perceive air quality as good. People who are surrounded by higher MB transportation land-use density and green space density tend to perceive air quality as poor. The results can help policymakers to increase public awareness of high air pollution areas, and consider the health effects of landscapes during planning.

Social media insights into spatio-temporal emotional responses to COVID-19 crisis

The Coronavirus pandemic has presented multifaceted challenges in urban emotional well-being and mental health management. Our study presents a spatio-temporal sentiment mining (STSM) framework to address these challenges, focusing on the space-time geography and environmental psychology. This framework analyzes the distribution and trends of 6 categories of public sentiments in Shanghai during the COVID-19 crisis, considering the potential urban spatial influencing factors. The research specifically draws on social media data temporally coinciding with the spread of COVID-19 and the pre-trained language model RoBERTa-wwm-ext to classify public sentiment, in order to characterize the distribution and trends of dominant urban sentiment under the influence of epidemic at different phases. The interactions between urban geospatial features and sentiments are further modelled and explained using LightGBM algorithm and SHapley Additive exPlanations (SHAP) technique. The experimental findings reveal the subtle yet dynamic impact of the urban environment on the long-term spatial variation and trends of public sentiment under the epidemic, with green spaces and socio-economic status emerging as significant factors. Regions with higher permanent population consumption demonstrated more positive sentiments, underscoring the significance of socio-economic factors in urban planning and public health policy. This research offers the most extensive analysis to date on the influence of urban characteristics on public sentiment during Shanghai's epidemic life cycle also lays the groundwork for applying the STSM framework in future crises beyond COVID-19.

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.

Inside and outside the neighborhood: Short-term and long-term subjective well-being by geographical context

The value of linking urban environment and subjective well-being (SWB) is now well recognized. But whether the geographical context inside and outside the neighborhood has differential influence on long- and short-term SWB remains unclear. Based on the activity perspective, we used survey data from Guangzhou, China, integrating GPS data, portable environmental sensors data to analyze time-weighted and real-time geographical context inside and outside the neighborhood on long- and short-term SWB. The results show that SWB is not only influenced by the neighborhood environment, but also the geographical context outside the neighborhood. Time-weighted geographical environment inside the neighborhood has a higher impact and explanatory ability on long-term SWB, while real-time geographical environment outside the neighborhood has a higher impact and explanatory ability on short-term SWB. This study provides a new understanding for geographies of SWB through the extension of time and space, and also provides reference for more refined urban planning and governance in the future.

Cyclists' exposure to air pollution, noise, and greenery: a population-level spatial analysis approach

Urban travel exposes people to a range of environmental qualities with significant health and wellbeing impacts. Nevertheless, the understanding of travel-related environmental exposure has remained limited. Here, we present a novel approach for population-level assessment of multiple environmental exposure for active travel. It enables analyses of (1) urban scale exposure variation, (2) alternative routes' potential to improve exposure levels per exposure type, and (3) by combining multiple exposures. We demonstrate the approach's feasibility by analysing cyclists' air pollution, noise, and greenery exposure in Helsinki, Finland. We apply an in-house developed route-planning and exposure assessment software and integrate to the analysis 3.1 million cycling trips from the local bike-sharing system. We show that especially noise exposure from cycling exceeds healthy thresholds, but that cyclists can influence their exposure by route choice. The proposed approach enables planners and individual citizens to identify (un)healthy travel environments from the exposure perspective, and to compare areas in respect to how well their environmental quality supports active travel. Transferable open tools and data further support the implementation of the approach in other cities.

The temporality of geographic contexts: Individual environmental exposure has time-related effects on mood

Research on environmental exposure and its impacts on people's mood has attracted increasing attention. Most studies focus on the spatiality of geographic contexts, but they neglect the influence of temporality in the relationships between environments and mood. To this end, a survey was conducted in January 2019 in Guangzhou, China, and measured data (micro-environments, built environments, EMA records, GPS trajectories, and activity logs) covering a weekday were collected from 125 participants. Then, multiple linear regression models are employed to examine and compare the associations between environments and mood based on three possible types of temporal responses (cumulative response, momentary response, and time-lagged response). The results indicate that there are great differences in environmental mood effects based on different types of temporal responses. Specifically, (i) for three types of temporal responses, exposure to PM_2.5 and noise have mood-blunting effects, whereas exposure to green spaces has mood-augmenting effects. (ii) For two types of temporal responses, higher temperature (in winter) may positively influence individual mood based on cumulative and time-lagged response, and higher POI density can positively affect mood based on cumulative and momentary response. (iii) Relative humidity may not have time-related effects on mood. Although all three types of temporal responses are observed in this study, the most significant manifestation is momentary response. These findings not only enrich theoretical research on environmental mood effects and temporality, but also inform the practice of more refined and humanistic urban planning, environmental governance, and public services.

Nonlinear Effects of the Neighborhood Environments on Residents' Mental Health

In the context of rapid urbanization and the "Healthy China" strategy, neighborhood environments play an important role in improving mental health among urban residents. While an increasing number of studies have explored the linear relationships between neighborhood environments and mental health, much remains to be revealed about the nonlinear health effects of neighborhood environments, the thresholds of various environmental factors, and the optimal environmental exposure levels for residents. To fill these gaps, this paper collected survey data from 1003 adult residents in Guangzhou, China, and measured the built and social environments within the neighborhoods. The random forest model was then employed to examine the nonlinear effects of neighborhood environments on mental health, evaluate the importance of each environmental variable, as well as identify the thresholds and optimal levels of various environmental factors. The results indicated that there are differences in the importance of diverse neighborhood environmental factors affecting mental health, and the more critical environmental factors included greenness, neighborhood communication, and fitness facility density. The nonlinear effects were shown to be universal and varied among neighborhood environmental factors, which could be classified into two categories: (i) higher exposure levels of some environmental factors (e.g., greenness, neighborhood communication, and neighborhood safety) were associated with better mental health; (ii) appropriate exposure levels of some environmental factors (e.g., medical, fitness, and entertainment facilities, and public transport stations) had positive effects on mental health, whereas a much higher or lower exposure level exerted a negative impact. Additionally, this study identified the exact thresholds and optimal exposure levels of neighborhood environmental factors, such as the threshold (22.00%) and optimal exposure level (>22.00%) of greenness and the threshold (3.80 number/km²) and optimal exposure level (3.80 number/km²) of fitness facility density.

Neighborhood disadvantage moderates the effect of a mobile health intervention on adolescent depression

This study leverages data from a pilot randomized controlled trial to investigate whether the effectiveness of a text-delivered mHealth intervention targeting adolescent depression and anxiety differs according to residential- and activity space-based measures of exposure to community-level socioeconomic disadvantage. For depression, we find that intervention efficacy is significantly stronger for youth residing in more disadvantaged neighborhoods, even after controlling for individual level socioeconomic status, as well as marginal moderating effects of activity space-based neighborhood disadvantage on treatment efficacy. We do not find evidence of treatment efficacy moderation by neighborhood disadvantage regarding anxiety. While the generalizability of our findings is restricted to this sample and for this intervention, this research serves as a motivating example and initial evidence for how mHealth intervention efficacy can vary by characteristics of the environment, in particular community-level disadvantage. Future clinical research should investigate whether the effectiveness of mHealth interventions may be enhanced by personalization based on an individual's contextual environmental exposures.