Wednesday, February 5, 2025 2/5/2025

Built Environment Assessment through Computer visiON (BEACON): Applying Deep Learning to Street-Level and Satellite Images to Estimate Built Environment Effects on Cardiovascular Health

Award Number: R01HL150119
ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

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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.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2025 ( Subtotal = $0 )
2025	2024	UNIVERSITY OF CALIFORNIA, DAVIS	1850 RESEARCH PARK DR STE 300	DAVIS	CA	95618	YOLO	USA	Cardiovascular Diseases Research	001	5	1/10/2025	CHANGE OF GRANTEE / TRAINING INSTITUTION / AWARDING INSTITUTION	$494,932
2025	2023	HARVARD PILGRIM HEALTH CARE INC	1 WELLNESS WAY	CANTON	MA	02021	NORFOLK	USA	Cardiovascular Diseases Research	000	4	1/10/2025	NON-COMPETING CONTINUATION	-$494,932
														Subtotal = $0

Issue Date FY: 2024 ( Subtotal = $759,104 )
2024	2024	UNIVERSITY OF CALIFORNIA, DAVIS	1850 RESEARCH PARK DR STE 300	DAVIS	CA	95618	YOLO	USA	Cardiovascular Diseases Research	001	5	5/31/2024	CHANGE OF GRANTEE / TRAINING INSTITUTION / AWARDING INSTITUTION	$759,104
2024	2023	HARVARD PILGRIM HEALTH CARE INC	1 WELLNESS WAY	CANTON	MA	02021	NORFOLK	USA	Cardiovascular Diseases Research	000	4	5/30/2024	NON-COMPETING CONTINUATION	$0
														Subtotal = $759,104

Issue Date FY: 2023 ( Subtotal = $757,428 )
2023	2023	HARVARD PILGRIM HEALTH CARE INC	1 WELLNESS WAY	CANTON	MA	02021	NORFOLK	USA	Cardiovascular Diseases Research	000	4	8/15/2023	NON-COMPETING CONTINUATION	$757,428
														Subtotal = $757,428

Issue Date FY: 2022 ( Subtotal = $772,031 )
2022	2022	HARVARD PILGRIM HEALTH CARE INC	93 WORCESTER ST	WELLESLEY	MA	02481	NORFOLK	USA	Cardiovascular Diseases Research	000	3	8/2/2022	NON-COMPETING CONTINUATION	$772,031
														Subtotal = $772,031

Issue Date FY: 2021 ( Subtotal = $785,476 )
2021	2021	HARVARD PILGRIM HEALTH CARE INC	93 WORCESTER ST	WELLESLEY	MA	02481	NORFOLK	USA	Cardiovascular Diseases Research	000	2	7/3/2021	NON-COMPETING CONTINUATION	$785,476
														Subtotal = $785,476

Issue Date FY: 2020 ( Subtotal = $863,771 )
2020	2020	HARVARD PILGRIM HEALTH CARE INC	93 WORCESTER ST	WELLESLEY	MA	02481	NORFOLK	USA	Cardiovascular Diseases Research	000	1	6/15/2020	NEW	$863,771
														Subtotal = $863,771

Grand Total All Awards = $3,937,810

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Built Environment Assessment through Computer visiON (BEACON): Applying Deep Learning to Street-Level and Satellite Images to Estimate Built Environment Effects on Cardiovascular Health

Award Number: R01HL150119

ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

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