Obesity is an established driver of cancer risk, and substantial evidence has linked obesity to inadequate physical activity and sleep. Geographic contextual measures, including neighborhood walkability and access to green space, have been demonstrated to affect physical activity, sleep patterns, and obesity. These factors are typically measured with questionnaires, which have substantial error. Novel mobile technologies, such as global positioning systems (GPS) enabled smartphones and consumer wearable accelerometry devices, can provide efficient, rigorous, and objective measures of geographic context, physical activity, and sleep with high spatio-temporal resolution. However, managing, processing, and analyzing streaming high-dimensional data presents significant logistical and analytical challenges, especially when linking these data to existing data from large prospective cohorts. My long term career goals are to assess the effect of dynamic measures of geographic context on objective measures of physical activity and sleep, as well as subsequent obesity and cancer risk within the full Nurses' Health Study 3 (NHS3). NHS3 is a web-based, nationwide, prospective open cohort with a current enrollment of ~40,000 male and female nurses aged 19-46 years old.
In Aims 1 and 2 of this proposal, I will measure the interdependent relationships between geographic context, physical activity, sleep, and obesity by deploying smartphone applications and wearable devices within a subsample (n=500) of the NHS3. I will use mobile technologies to collect streaming, high spatio-temporal resolution measures of geographic context (walkability and green space), physical activity, and sleep over a seven day monitoring period, four times over one year. I will then apply state-of-the-science statistical methods to examine the interrelationships between these high-dimensional, big data measures of context and behavior.
For Aim 3, I will apply statistical approaches for measurement error correction to examine the relationship between error-corrected measures of context/behavior and obesity in the full NHS3 cohort. I am well suited to perform this research based on 1) my past research experience in contextual measures and health, 2) the exceptional mentoring team I have assembled to ensure that this research is of the highest quality, and 3) the unique resources of NHS3. This study will enable me to rigorously quantify contextual exposures, physical activity and sleep, and to identify the influence of geographic contextual factors on these interdependent behavioral risk factors for cancer and obesity. I will be guided by a world-class team of mentors to expand my expertise in the objective measurement of geographic context, physical activity, and sleep through mobile technology; big data methods; and measurement error correction. The proposed research and training will provide me the skills to establish an independent career as a leader in the epidemiology of behavioral risk factors for cancer.
Geographic contextual exposures, physical activity, sleep, and obesity are important and interdependent behavioral risk factors for cancer; however, these factors are often measured poorly in epidemiologic research. I propose to integrate high spatio-temporal resolution objective measurements of these contextual and behavioral cancer risk factors into a nationwide prospective cohort study using mobile technology, big data methods, and measurement error correction. This research will enable unprecedented perspectives on exposures and behaviors that drive obesity and subsequent cancer risk, and will provide translational insights into potential interventions to optimize opportunities for physical activity and healthy sleep patterns.
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