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"Computational Challenges in Environmental Epidemiology: Embedding Spatial Data Science, Mobile Health Technology, and Deep Learning into Prospective Cohort Studies" with Peter James, HSPH.
"Computational Challenges in Environmental Epidemiology: Embedding Spatial Data Science, Mobile Health Technology, and Deep Learning into Prospective Cohort Studies" with Peter James, HSPH.
The places in which we live, work, play, and age influence our health behaviors, our mental health, our cognitive function, and our chronic disease risk. However, the majority of research on spatial factors and health has relied on residential addresses to assign exposure, questionnaire data to measure health behaviors and health outcomes, and coarse and nonspecific indices to estimate exposure to spatial factors. Recent technological advances have provided opportunities to overcome these limitations. Mobile health technologies—including GPS-enabled smartphones and consumer wearables like Fitbits—have opened new doorways to track personalized exposure and granular data on mental health, cognitive function, and health behaviors from minute to minute. Deep learning algorithms applied to Google Street View images empower us to estimate exposure to specific features of the built and natural environment from an on the ground perspective to quantify their impact on health. In this talk, I will speak about my experience integrating mobile health technologies, consumer wearables, and Google Street View imagery into several prospective cohorts, including the Nurses' Health Studies and Project Viva.
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Contact: CRCS@seas.harvard.edu