Objectively Measured Life Space Using Participants’ Mobile Phones: Cross-Sectional Study

Abstract Background Measurement of life space characterizes people’s engagement with their environment. Several studies have used GPS devices to capture life space. A challenge with device-based measures is that participants may forget to bring the device with them. Life-space measures that leverage the GPS data from participants’ mobile phones may address this challenge, but few studies have leveraged this approach to date. Objective Our objective was to assess the feasibility of implementing a mobile phone life-space assessment and characterize life space using Google Street View (GSV) imagery. We then aimed to evaluate the association of demographic data with life-space size and environmental features. Methods We developed a mobile phone app to passively capture life space in older adults and sampled GSV images from participants’ life space to characterize the environments where they spent their time. For this cross-sectional study, we recruited 82 participants (n=34, 41.5% men) aged 65 years and older (mean 74.5, SD 6.5 years) in 2023 to 2024 from the REGARDS (Reasons for Geographic and Racial Differences in Stroke) study. They were followed for 2 weeks and invited to complete nightly questionnaires on their phones. We measured features of the built environment that captured walkability (sidewalks, benches, and streetlights), neighborhoods (houses), and green space based on an image segmentation algorithm. Differences in life-space size and environmental features across participant characteristics were tested using the Wilcoxon rank sum test. Results Among 82 participants, 76 (92.7%) completed all 14 days of data collection. Median daily maximum distance from home was 6.6 (IQR 1.3-15.5) km. Median distance traveled over 2 weeks was 477.1 (IQR 234.4-817.13) km. Adults younger than 70 years, women, Black participants, and those with income more than US $75,000 per year had larger life space than those aged 70 years and older, men, White participants, and those with income less than US $75,000 (154.5 vs 58.1 km 2 , P =.04; 102.9 vs 46.0 km 2 , P =.07; 179.3 vs 62.8 km 2 , P =.03; and 145.5 vs 25.5 km 2 , P =.001, respectively), although not all of these comparisons met the α=.05 level of statistical significance based on the Wilcoxon rank sum test. We found no association between demographic characteristics and features of the life-space built environment ( P >.05 for all comparisons). Conclusions In this feasibility study, we demonstrated that personal mobile phones could be used to passively track older adults’ life space based on a digital health app developed on an open-source platform. There was wide variation in objectively measured life space, and we found sociodemographic patterning of life-space movement. Objective measurement of life space with participants’ mobile phones is feasible and should be evaluated in larger samples to better capture the relationship between health and time spent in different environments. Integration of a life-space assessment into older adults’ daily lives may allow for the use of life space as a health measure.