Making Sense of Sensors

• Activity Number: 334
• Type: Invited
• Date/Time: Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM
• Sponsor: Health Policy Statistics Section
• Author(s): Vadim Zipunnikov* and Debangan Dey and Andrew Leroux and Jacek Urbanek and Junrui Di
• Companies: Johns Hopkins Bloomberg School of Public Health and Indian Statistical Institute and Johns Hopkins Bloomberg School of Public Health and Johns Hopkins Bloomberg School of Public Health and Johns Hopkins Bloomberg School of Public Health
• Keywords: wearables ; multilevel models ; functional data analysis ; time-series ; mortality

Abstract

The crashing wave of activity tracking "wearables" opens up an opportunity to unveil previously hidden but pivotal signatures of disability and disease. To achieve this promise, the understanding, interpretation and analysis of complex multimodal and multilevel data produced by such devices becomes crucial. The first part of my talk will provide an overview of the instruments that are available for real-time measurement of physical activity as well as a quick review of the strengths and limitations of current analytical approaches for modeling physical activity data. In the second part, I will talk about analysis of physical activity data collected on 13K+ subjects in the National Health and Nutrition Examination Survey (NHANES), a nationally representative sample of the US population. I will present recent multilevel functional data approaches that separate and quantify the systematic and random circadian patterns of physical activity, model them as functions of age, gender, and dominant comorbidities and demonstrate that these patterns are powerful predictors of mortality.