Over 80% of the US population resides in urban areas, and the built environment?the buildings, streets, and green spaces in which we live?may drive cardiovascular disease (CVD) by promoting or limiting physical activity and weight gain, and by influencing exposures to environmental factors, such as air pollution, extreme temperatures, and noise. Evidence for the built environment and CVD has been dominated by cross-sectional studies with nonspecific exposure assessment. Developing precise, time-varying, and personalized exposure metrics is necessary to establish causal relationships between the built environment and CVD, which are crucial to informing policy-relevant, actionable interventions. It is now possible to estimate such exposure metrics at scale in prospective cohort studies using deep learning computer vision methods, a class of machine learning algorithms that can accurately process images, combined with time-varying nationwide street-level imagery, high resolution satellite data, and novel mobile health technologies. We propose to identify the influence of the built environment on CVD health behaviors and CVD incidence by developing built environment exposure measures from deep learning algorithms, and to apply these exposure measures to time-activity data in participants with global positioning systems (GPS) data from the Nurses? Health Study 3 (N=500), and to geocoded residential addresses from nationwide Nurses? Health Study, Nurses? Health Study II, and Health Professionals Follow-up Study prospective cohorts (N=288,000). We will create built environment exposure measures by leveraging deep learning algorithms applied to nationwide Google Street View imagery (2007-2020) and high-resolution Landsat satellite data (1986-2020) to create fine-scale, time-varying built environment metrics of the natural environment (e.g., trees), physical environment (e.g., sidewalks), perceptions (e.g., safety), and urban form (e.g., compact high-rise). We will use a mix of innovative analytical approaches to determine the effect of the built environment on CVD-related health behaviors and CVD incidence across different time horizons. First, we will append these metrics to time-activity patterns of participants who have collected minute-level data on GPS and physical activity from smartphones and consumer wearable devices to quantify how minute-level exposure to the built environment is related to CVD health behaviors. Next, we will apply novel built environment metrics to residential address histories of participants to estimate how self-reported CVD health behaviors change after their residential built environment changes. Last, we will examine the association between long-term cumulative residential exposure to the built environment and CVD incidence over 34 years of follow-up. Our work will enable us to measure built environment exposure from unprecedented perspectives in large prospective cohorts, to elucidate potential causal relationships between the built environment and CVD health behaviors, and to better specify pathways to CVD incidence. Ultimately, our work will yield actionable insights to guide land use policy and urban planning strategies to design cities that optimize cardiovascular health.
The built environment?the buildings, streets, and green spaces in which we live?may influence cardiovascular disease (CVD) by promoting or limiting physical activity and weight gain and by shaping exposures to environmental hazards (e.g., air pollution, extreme temperatures, noise); however, developing accurate, time- varying, and personalized exposure metrics is necessary to build causal relationships between the built environment and CVD and to inform policy-relevant, actionable interventions. We propose to identify the influence of the built environment on CVD-related health behaviors and CVD incidence by developing longitudinal built environment exposure measures from street-view and satellite imagery using deep learning algorithms, and to apply these exposure measures to time-activity data from a) 500 participants with global positioning systems (GPS) data from the Nurses? Health Study 3 (NHS3) and b) over 34 years of geocoded residential addresses from three large nationwide cohorts of over 288,000 US adult women and men from the Nurses? Health Study, Nurses? Health Study II, and Health Professionals Follow-up Study prospective cohorts. Our work will enable us to measure time-varying exposure to the built environment with unprecedented precision in large prospective cohorts, to elucidate potential relationships between the built environment and CVD health behaviors, to better specify pathways to CVD incidence, and ultimately, to yield actionable insights to guide land use policy and urban planning strategies to design cities that optimize cardiovascular health.
