PROJECT SUMMARY
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.