A growing number of studies have associated walkability and greenspace exposure with greater physical activity (PA) in women during pregnancy. However, most studies have focused on examining women’s residential environments and neglected exposure in locations outside the home neighborhood. Using 350 person-days (N = 55 participants) of smartphone global positioning system (GPS) location and accelerometer data collected during the first and third trimesters and 4–6 months postpartum from 55 Hispanic pregnant women from the Maternal and Developmental Risks from Environmental and Social Stressors (MADRES) study, we examined the day-level effect of women’s exposure to walkability and greenspace on their PA outcomes during pregnancy and in the early postpartum period. Moderate-to-vigorous physical activity [MVPA] minutes per day was assessed using accelerometers. Walkability and greenspace were measured using geographic information systems (GIS) within women’s daily activity spaces (i.e., places visited and routes taken) recorded using a smartphone GPS and weighted by time spent. We used a generalized linear mixed-effects model to estimate the effects of daily GPS-derived environmental exposures on day-level MVPA minutes. Results showed that women engaged in 23% more MVPA minutes on days when they had some versus no exposure to parks and open spaces in activity spaces (b = 1.23; 95%CI: 1.02–1.48). In addition, protective effects of daily greenspace and walkability exposure on MVPA were stronger in the first and third trimesters, among first-time mothers, and among women who had high pre-pregnancy body mass index (BMI) and lived in least-safe neighborhoods. Our results suggest that daily greenspace and walkability exposure are important for women’s PA and associated health outcomes during pregnancy and early postpartum.

Greenspace exposure is associated with lower depression risk. However, most studies have measured greenspace exposure using satellite-based vegetation indices, leading to potential exposure misclassification and limited policy relevance. We examined the association of street-view greenspace measures with incident depression in a prospective cohort of US women. We applied deep learning segmentation models to 350 million US street-view images nationwide (2007-2020) to derive ground-level greenspace metrics, including percentage of trees, grass, and other greenspace (plants/flowers/fields), and linked metrics to Nurses' Health Study II participants' residences (N = 33,490) within 500 m each year. Cox proportional hazards models estimated the relationship between street-view greenspace metrics and incident depression, assessed through self-report of clinician-diagnosed depression or regular antidepressant use and adjusted for individual- and area-level factors. In adjusted models, higher percentages of street-view trees were inversely associated with incident depression (HR per IQR, 0.98; 95%CI: 0.94-1.01) and specifically clinician-diagnosed depression (HR per IQR, 0.94; 95%CI: 0.90-0.99). Higher percentages of street-view grass were also inversely associated with incident depression, but only in areas with low particulate matter (PM_2.5) levels (HR per IQR, 0.79; 95%CI: 0.71-0.86). Results were consistent after adjusting for additional spatial and behavioral factors, and persisted after adjusting for traditional satellite-based vegetation indices. We observed participants who lived in areas with more trees visible in street-view images had a lower risk of depression. Our findings suggest tree-planting interventions may reduce depression risk.

Background: Previous studies investigating environmental and behavioral drivers of chronic disease have often had limited temporal and spatial data coverage. Smartphone-based digital phenotyping mitigates the limitations of these studies by using intensive data collection schemes that take advantage of the widespread use of smartphones while allowing for less burdensome data collection and longer follow-up periods. In addition, smartphone apps can be programmed to conduct daily or intraday surveys on health behaviors and psychological well-being. Objective: The aim of this study was to investigate the feasibility and scalability of embedding smartphone-based digital phenotyping in large epidemiological cohorts by examining participant adherence to a smartphone-based data collection protocol in 2 ongoing nationwide prospective cohort studies. Methods: Participants (N=2394) of the Beiwe Substudy of the Nurses’ Health Study 3 and Growing Up Today Study were followed over 1 year. During this time, they completed questionnaires every 10 days delivered via the Beiwe smartphone app covering topics such as emotions, stress and enjoyment, physical activity, access to green spaces, pets, diet (vegetables, meats, beverages, nuts and dairy, and fruits), sleep, and sitting. These questionnaires aimed to measure participants’ key health behaviors to combine them with objectively assessed high-resolution GPS and accelerometer data provided by participants during the same period. Results: Between July 2021 and June 2023, we received 11.1 TB of GPS and accelerometer data from 2394 participants and 23,682 survey responses. The average follow-up time for each participant was 214 (SD 148) days. During this period, participants provided an average of 14.8 (SD 5.9) valid hours of GPS data and 13.2 (SD 4.8) valid hours of accelerometer data. Using a 10-hour cutoff, we found that 51.46% (1232/2394) and 53.23% (1274/2394) of participants had >50% of valid data collection days for GPS and accelerometer data, respectively. In addition, each participant submitted an average of 10 (SD 11) surveys during the same period, with a mean response rate of 36% across all surveys (SD 17%; median 41%). After initial processing of GPS and accelerometer data, we also found that participants spent an average of 14.6 (SD 7.5) hours per day at home and 1.6 (SD 1.6) hours per day on trips. We also recorded an average of 1046 (SD 1029) steps per day. Conclusions: In this study, smartphone-based digital phenotyping was used to collect intensive longitudinal data on lifestyle and behavioral factors in 2 well-established prospective cohorts. Our assessment of adherence to smartphone-based data collection protocols over 1 year suggests that adherence in our study was either higher or similar to most previous studies with shorter follow-up periods and smaller sample sizes. Our efforts resulted in a large dataset on health behaviors that can be linked to spatial datasets to examine environmental and behavioral drivers of chronic disease.