Mechanistic Insights into Health Disparities: Leveraging Longitudinal Wearable Data to Assess the Impact of Built Environment on Physical Activity and Health Outcomes

Despite the known benefits of regular physical activity (PA), adherence to recommended PA guidelines remains alarmingly low, with marked variation observed across the population. PA behavior is influenced by genetic, behavioral, environmental, and social factors. In particular, the built environment—where people are born, live, learn, work, play, worship, and age—has become an increasingly important focus for interventions aimed at promoting healthy PA behaviors. However, understanding the role of the built environment in shaping PA behaviors has been limited due to incomprehensive measures of PA and the infeasibility of randomized controlled trials. Our project aims to address this gap by leveraging the large, longitudinal wearable device data from the All of Us Research Program (AoURP). We propose a novel approach to represent PA behaviors and a machine learning framework to uncover the mechanisms through which the built environment factors influence PA and, by extension, health outcomes. We will utilize minute-level intraday step data from the AoURP to generate PA behavior profiles—a data-driven representation for quantifying daily patterns of PA. This high-frequency data from wearable devices offers detailed insights into each individual’s daily interactions with the built environment, offering greater temporal specificity than conventional summary metrics, such as average daily steps or minutes of moderate-to-vigorous activity. We hypothesize that PA will be strongly associated with a variety of factors, including the built environment, and that these associations will be more pronounced using PA behavior profiles than traditional summary measures. We also aim to study the indirect effects of the built environment on health outcomes through a machine learning framework that incorporates counterfactual analysis. Through this framework, we will examine how hypothetical modifications in the built environment could "flip" health outcomes by altering an individual's PA behavior. In addition, stratified post-hoc analyses will be conducted to explore the differential impacts of environmental changes across various subpopulations. This proposed project will advance our scientific understanding of population-level health outcomes through the development of a new approach for representing PA behaviors from wearable device data. By proposing a novel framework for counterfactual analysis, the project will demonstrate how observational data can be used to understand the underlying etiologic factors and mechanisms that contribute to variation in health outcomes. Project Number: 1F31HL179990-01 | Fiscal Year: 2025 | NIH Institute/Center: National Heart Lung and Blood Institute (NHLBI) | Principal Investigator: Hayoung Jeong | Institution: DUKE UNIVERSITY, DURHAM, NC | Award Amount: $42,941 | Activity Code: F31 | Study Section: Special Emphasis Panel[ZRG1 F18-E (20)] View on NIH RePORTER: https://reporter.nih.gov/project-details/1F31HL17999001