Participants for this cross-sectional study (REGARDS Life Space) were recruited from the parent REGARDS study. The REGARDS study enrolled 30,239 Black and White participants aged ≥45 years from the continental United States between 2003 and 2007. The primary objective of the REGARDS study was to better understand the reasons for the excess stroke mortality among Black persons and among those residing in the southeastern United States [24]. Exclusion criteria included race other than African American or Black or White, active treatment for cancer, medical conditions that would prevent long-term participation, cognitive impairment judged by the telephone interviewer, residence in or inclusion on a waiting list for a nursing home, or inability to communicate in English [24]. Data for the parent REGARDS study were collected from participants during 2 in-home exams (baseline: 2003‐2007; exam 2: 2013‐2016) and ongoing telephone follow-up interviews every 6 months. Participants for the present study (REGARDS Life Space) were recruited during these phone calls in 2023 to 2024.

Additional eligibility criteria for the current study included the following: (1) having an iPhone model 8 or later, (2) usually bringing their iPhone with them when they left the house, (3) knowing how to download an app, and (4) not being homebound. REGARDS participants were screened for eligibility and then invited to participate in the study.

Participants were recruited for the current study in 2023 to 2024 using a convenience sampling method from the parent REGARDS study. They were initially screened for eligibility during the REGARDS 6-month follow-up calls. Participants who were eligible were then invited to participate in the current study by mail per the REGARDS ancillary study protocols. Individuals who responded to the mailer were included in the study. The target sample size was 100 participants for this feasibility study based on the available funding and staffing.

Data on age, gender (woman or man), race (Black or White), education (less than high school, high school, some college, or college and above), income (<US $35,000, US $35,000‐US $75,000, or >US $75,000 per year), employment status, and smoking status were collected by self-report from the parent REGARDS study. We conceptualize race in this study as a social construct, reflective of the different social conditions in which people live in the United States [24-26].

We evaluated characteristics of participants who were screened versus enrolled in the pilot study and tested for differences using a t test for age and a chi-square test for all other variables. We additionally described the distribution of the life-space metrics and self-reported variables. The within- and between-person variances were calculated using an intercept-only linear random effects model of the self-reported variables; this model included a random intercept for each person and accounted for the fact that multiple ratings from the same individual may be correlated [34]. Differences in summary life-space metrics across participant characteristics were tested using the Wilcoxon rank sum test. In the supplementary analyses, we used a nonparametric repeated measures test, the aligned rank transform ANOVA, to test for differences in day and environmental ratings between groups [35].

The street view metrics were standardized by subtracting the mean and dividing by the SD. We tested for differences in the street view metrics by participant characteristics using the Wilcoxon rank sum test. We evaluated the correlation of the street view metrics using pairwise Pearson correlation coefficients.

All geospatial and statistical analyses were conducted using R (version 4.2.3; R Foundation for Statistical Computing) [36].

This study was approved by the Stanford University Institutional Review Board (IRB-65276) and the University of Alabama at Birmingham Institutional Review Board (IRB-020925004). After inviting people to participate in the study, participants were informed of the study goals, risks, and benefits through the mobile app, and they also provided informed consent per the approved protocol. Consent forms were stored separately from study data, and all data were maintained in a deidentified format on a Research IT mHealth platform approved for protected health information. Participants could receive up to US $100 for participation by completing all 14 nightly surveys and the close-out survey. Those who completed fewer surveys received less. No identification of individual participants in any images of the manuscript or supplementary material is possible.

This paper was written following the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines (Checklist 1) for cross-sectional studies [37].

Recruitment and enrollment into the current study took place over 3 waves from June 2023 through January 2024. Among 1768 participants screened, 795 (45%) had an iPhone (Figure S1 in Multimedia Appendix 1). Among these participants, 538 (67.7%) said that they were possibly interested in a mobile phone study. There were 389 (72.3%) participants who met the eligibility criteria. Among the 389 eligible individuals, only 120 (30.8%) responded to our invitation letter, and 82 (68.3%) enrolled in the study.

The characteristics of those who did and did not participate are reported in Table 1. Overall, participants were younger, more likely to be female, and to identify as White. Participants had higher education, greater income, and were more likely to be employed compared with nonparticipants.

Among 82 enrolled participants, 76 (92.7%) had 14 days of GPS data collected. Since all participants had at least 5 days with GPS data recorded, we did not exclude any participants from the analysis, and there were no missing data on covariates. The median number of days of response to the nightly questionnaires was 14 (IQR 11-14; range 2-14). There was a high degree of variability across participants in the life-space metrics (Table 2). For example, the total distance traveled over 2 weeks ranged from 39 km to 31,743 km. Participants rated the overall quality of their day as “very good” (median 4) and their environment as “excellent” (median 5), and the within-person variability over 14 days approximated the between-person variability of these measures.

On average, participants younger than 70 years, female participants, and Black participants had larger life space measured by the life-space ellipse than those aged 70 years and older, male participants, and White participants (154.5 vs 58.1 km², P=.04; 102.9 vs 46.0 km², P=.07; 179.3 vs 62.8 km², P=.03; and 145.5 vs 25.5 km², P=.001), although not all of these comparisons met the α=.05 level of statistical significance based on the Wilcoxon rank sum test (Table 3). Female participants were likely to travel a farther maximum distance from home than male participants (31.3 km vs 23.6 km, P=.02), and younger participants had a greater total distance traveled compared with older participants (561.7 vs 410.5 km, P=.08), although this difference did not meet the α=.05 level of statistical significance based on the Wilcoxon rank sum test. Of the socioeconomic variables, higher income (>US $75,000 per year) was associated with higher life space, maximum distance from home, and a greater total distance traveled compared with <US $75,000 per year (145.5 vs 25.5 km², P=.001; 40.3 vs 18.0 km, P=.008; and 576.5 vs 320.0 km, P=.004, respectively). The daily ratings and environmental ratings did not differ by age, gender, or race (P>.05 for all comparisons; Table S1 in Multimedia Appendix 1).

The street view metrics were strongly intercorrelated. Greenness was associated with lower levels of sidewalk (ρ=−0.32; P=.003) and higher levels of streetlight (ρ=0.50; P<.001) and house (ρ=0.48; P<.001). The sidewalk variable was associated with greater levels of streetlights (ρ=0.62; P<.001) and benches (ρ=0.89; P<.001) but lower measured occurrences of houses (ρ=−0.49; P<.001). The bench and streetlight variables were positively correlated (ρ=0.70; P<.001), while houses and benches were negatively correlated (ρ=−0.60; P<.001; Table S2 in Multimedia Appendix 1). Street view metrics were characterized by demographic groups (Table S3 in Multimedia Appendix 1). Overall, metrics were similar across age, gender, race, education, income, and employment categories, although women had slightly higher exposure to green space than men, those with a college education had slightly lower bench and greater house values than those without a college education, and those currently employed had slightly lower streetlight and bench values than those not currently employed.

In this feasibility study, we demonstrated that participants’ personal mobile phones can be used to passively track their life space. Among 82 older adult participants, we found that participants younger than 70 years, female participants, Black participants, and participants with higher income had a larger life space compared with those 70 years and older, male participants, White participants, and those with lower income. We additionally were able to capture features of the physical environment from GSV imagery. The participants in our study were a selected population of REGARDS participants, and future studies should aim to streamline enrollment to lower the bar to participation.

Our study built upon other recent work using GPS technologies to capture life-space and mobility patterns among older adults [12]. In a 2021 systematic review of 29 studies using GPS tracking technology to measure mobility, only one of the studies used the participants’ smartphone devices [12]. Although location tracking is common among commercial smartphone apps, the uptake in research settings appears to lag behind, with only a small number of investigations using GPS tracking in commercial devices in older adults [10,11]. This may be due to time and technology barriers to app development. We used an open-source framework for digital health apps and research that was developed to accelerate the prototyping of digital health apps [38]. This set of tools enabled us to develop a relevant app from the ground up in a reasonably short time while ensuring that the app met our standards for security and accessibility, as opposed to starting from scratch, which would have slowed development.

Consistent with the literature summarized in the 2021 systematic review, we found that life-space mobility was smaller among older adults [12]. In contrast to prior research, we found that women traveled a greater distance from home relative to men [12,39]. The reason for this difference is unclear and could be due to chance. In our population, Black participants had a greater life space compared with White participants. This may be due to the higher prevalence of employment among Black participants in our study or other factors including social connections and leisure activities. We found that participants with higher income had higher life space, greater maximum distance traveled, and greater total distance traveled, and this was consistent with prior literature demonstrating that low-income adults spend a greater proportion of time at home and have smaller life-space measures [12,29]. This may be due to the greater available resources that higher income affords, including access to transportation and money to pay for gas, food, and activities outside the home.

Our findings demonstrate that passive smartphone-based life-space monitoring is feasible and well-accepted in research studies. Additionally, this technology could support the care of older adults by enabling early detection of restricted life space without patient burden, potentially triggering follow-up to investigate and address the root causes [6]. The observed differences by income and race highlight how this technology could identify populations who may benefit from targeted interventions such as improved transportation access or community resources [6]. Furthermore, linking mobility patterns with environmental data could inform age-friendly urban planning and identify modifiable built environment features that promote or restrict older adults’ real-world movement and independence [9].

As a feasibility study, our study was necessarily limited in scope due to time and resource constraints, and several limitations should be considered when interpreting the findings. Our study population represented a selected sample of REGARDS participants who responded to a mailed recruitment letter, limiting generalizability to the broader REGARDS cohort and other community-dwelling adults. Specifically, our participants were younger and had greater education and income, which may correlate with greater technological literacy, health engagement, and economic resources. Following REGARDS ancillary study protocols, eligible participants were invited to participate by a letter. Email-based recruitment could enable faster response times and communication. Moreover, in-person enrollment during existing study visits would substantially lower barriers to participation, particularly for individuals with limited technological literacy or confidence. We recommend that future studies use this approach, when feasible, to improve generalizability. Additionally, we recommend having dedicated study personnel, ideally at an in-person visit, to enroll study participants and facilitate downloading and installing the mobile app. When in-person support is not feasible, participants should have access to study staff via telephone or email during extended business hours. Due to funding constraints, we were unable to staff our helpline continuously and thus needed to return phone calls from participants asynchronously, which slowed down recruitment and disadvantaged those with limited flexibility. We anticipate that technological support needs will attenuate over time as digital literacy increases among older adults. Nonetheless, ensuring accessible technical assistance remains critical for equitable participation across diverse populations, particularly among those with limited prior smartphone experience, lower educational attainment, or cognitive impairment. Finally, our app was designed to maximize battery efficiency by sampling GPS coordinates only every 500 m, but this constraint limited our ability to capture microscale environmental variations, particularly in dense urban environments.

This first-generation study is an important demonstration of the feasibility of integrating life-space assessment and mobility measures into older adults’ daily routine through the use of a smartphone app. Unlike traditional questionnaire-based methods to assess life space, this approach can offer real-world continuous assessment and is not susceptible to recall bias. An additional strength of this approach is that it adds virtually no additional measurement burden to the participant, and thus could be used to capture life space over an extended period of weeks, months, or years. Integration of a life-space assessment into older adults’ daily lives may allow for the use of life space as an integrative health metric, and changes in life space could be studied for their prognostic value. New open-source platforms can accelerate time to development for digital health apps, thus supporting the feasibility of smartphone apps in population health research.