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

Unlocking the night: Exploring the health impacts of night-time walking environments on health outcomes of older adults

Walking remains the primary form of physical activity for many older adults in China, and the quality of the walking environment at night may determine the frequency and duration of evening outings. This study reveals how night-time environmental features influence the health outcomes of older adults. Using the medical check-up records of 87,578 older adults from a public health service in the Beilin district of Xi'an city, China, the role of the night-time walking environment in managing chronic conditions was examined. A favorable night-time walking environment reduced the prevalence of chronic conditions and comorbidities among older adults. However, the health effects stemming from the night-time walking environment exhibited heterogeneity, with significant impacts only on metabolic conditions, such as hypertension and diabetes, while the effects on other conditions were not significant. Our findings supplement the theory of healthy aging by highlighting the potential value of the environment in managing chronic conditions, which may serve as a cost-effective health intervention for aging societies.

Neighborhood Factors as Correlates of Alcohol Use in the N2 Cohort Study of Black Sexual Minority Men and Transgender Women

Sexually minoritized men (SMM), transgender women (TW), and particularly Black SMM and Black TW may be disproportionately impacted by alcohol-related problems. Few studies have empirically examined neighborhood factors that may contribute to alcohol use, specifically among these populations. Using data from the N2 longitudinal cohort study in Chicago, IL, survey data from the second wave of longitudinal assessment (n = 126), and GPS mobility data collected during study enrollment were used to evaluate neighborhood alcohol outlet availability, neighborhood disorder, and neighborhood poverty as correlates for individual alcohol use. Neighborhood exposures were measured using 200-m derived activity space areas, created from GPS data, and with publicly accessible geospatial contextual data. Separate multi-variable quasi-poison regression models tested for association between neighborhood alcohol outlet density (AOD), measured separately for on-premise (e.g. bars) and off-premise consumption outlets (e.g. liquor stores), neighborhood poverty (defined as the percentage of neighborhood areas at 150% or greater of the U.S. poverty line), exposure to vacant buildings, and neighborhood violent crime density. Separate analytical models found no significant effect between alcohol use and on-premise neighborhood AOD (IRR = 0.99, p = 0.35), off-premise consumption AOD (IRR = 0.92, p = 0.33), or neighborhood violent crime (IRR = 1.00, p = 0.65). Vacant buildings (IRR = 1.03, p = 0.05) and levels of neighborhood poverty (1.05, p = 0.01) were found to be significantly associated with increased alcohol use. Among this population, opposed to geospatial access, neighborhood measurements indicative of disorder and poverty may have greater influence on shaping alcohol use.

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.

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.