BACKGROUND: Inconsistent results have been found in the literature on associations of greenness, or vegetation quantity, and physical activity. However, few studies have assessed associations between mobility-based greenness and physical activity from mobile health data from smartphone and wearable devices with fine spatial and temporal resolution. METHODS: We assessed mobility-based greenness exposure and wearable accelerometer data from participants in the US-based prospective Nurses Health Study 3 cohort Mobile Health (mHealth) Substudy (2018-2020). We recruited 500 female participants with instructions to wear devices over four 7-day sampling periods equally spaced throughout the year. After restriction criteria there were 337 participants (mean age 36 years) with n = 639,364 unique observations. Normalized Difference Vegetation Index (NDVI) data were derived from 30 m x 30 m Landsat-8 imagery and spatially joined to GPS points recorded every 10 min. Fitbit proprietary algorithms provided physical activity summarized as mean number of steps per minute, which we averaged during the 10-min period following a GPS-based greenness exposure assessment. We utilized Generalized Additive Mixed Models to examine associations (every 10 min) between greenness and physical activity adjusting for neighborhood and individual socioeconomic status, Census region, season, neighborhood walkability, daily mean temperature and precipitation. We assessed effect modification through stratification and interaction models and conducted sensitivity analyses. RESULTS: Mean 10-min step count averaged 7.0 steps (SD 14.9) and greenness (NDVI) averaged 0.3 (SD 0.2). Contrary to our hypotheses, higher greenness exposure was associated non-linearly with lower mean steps per minute after adjusting for confounders. We observed statistically significant effect modification by Census region and season. DISCUSSION: We utilized objective physical activity data at fine temporal and spatial scales to present novel estimates of the association between mobility-based greenness and step count. We found higher levels of greenness were inversely associated with steps per minute.

BACKGROUND: Sleep is influenced by the environments that we experience while awake and while asleep. Neighborhood walkability has been linked with chronic disease and lifestyle factors, such as physical activity; however, evidence for the association between walkability and sleep is mixed. Extant studies assign walkability based on residential addresses, which does not account for mobility. We examined the association between walkability and sleep in the Nurses Health Study 3 (NHS3) Mobile Health Substudy (MHS). METHODS: From 2018 to 2020, individuals in the United States-based NHS3 prospective cohort participated in the MHS, in which minute-level global positioning systems (GPS) data and objective sleep duration and efficiency measures were collected via a custom smartphone application and Fitbit, respectively, for four 7-day periods across a year to capture seasonal variability. Census tract walkability was calculated by summing z-scores of population density (2015-2019 American Community Survey), business density (2018 Infogroup), and intersection density (2018 TIGER/Line road shapefiles). We ran generalized additive mixed models with penalized splines to estimate the association between walkability and sleep, adjusting for individual-level covariates as well as GPS-based exposure to environmental and contextual factors. RESULTS: The average main sleep period duration was 7.9 hours and the mean sleep efficiency was 93%. For both sleep duration and sleep efficiency, we did not observe an association with daily average walkability exposure. CONCLUSION: In this study of women across the United States, we found that daily GPS-based neighborhood walkability exposure during wake time was not associated with objective wearable-derived sleep duration or sleep efficiency.