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The prevalence of overweight and obesity are increasing worldwide. In the U.S., approximately 85,000 new cancer cases per year are related to obesity. Understanding lifestyle behaviors, their causes, and relations to cancer are critical. Where people spend their time during the day may be related to their risk of getting cancer. This project will assess behaviors in different locations across the day and relate exposure to different environments to biological outcomes.
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The purpose of this study is to advance methods of cancer risk exposure assessment by measuring both neighborhood access and total environment exposure to healthy environments by dynamically integrating Global Positioning System (GPS) data with Geographical Information System (GIS) data. We hypothesize that dynamic GPS based measures of environmental exposure will be more strongly related to behavior and insulin and inflammation biomarkers than static address based GIS measures of access.
Primary Aim:
To compare Dynamic vs Static GIS measures of physical activity (PA) supportivity and their associations with PA behaviors, body mass index (BMI), and biological markers.
Secondary Aim:
To compare Dynamic vs Static GIS measures of access to healthy food and their associations with dietary intake, BMI, and biological markers.
Exploratory aim:
Social environment (e.g. eating with others) has been identified as an important determinant of health, but assessment of this has been limited to self-report through surveys or text prompts. Images from a person worn camera (the SenseCam) provide an objective and continuous assessment of social environment e.g. number of social interactions per day, eating alone or with others. In a subsample of participants (N=30), we propose to explore whether portable SenseCam measures of social interactions & social behaviors from person view images are more strongly related to breast and colon cancer risk factors than self-reported social environment measures. Behaviors and built environments (especially food locations) can also be coded to validate the accuracy of the GIS measures and the machine learned behavior categories employed in the full sample.
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