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Title: A two-stage model for wearable device data Authors:  Jiawei Bai - Johns Hopkins University (United States)
Yifei Sun - Columbia University (United States)
Jennifer Schrack - Johns Hopkins University (United States)
Ciprian Crainiceanu - Johns Hopkins University (United States)
Mei-Cheng Wang - Johns Hopkins University (United States)
Jiawei Bai - Johns Hopkins University (United States) [presenting]
Abstract: Recent advances in wearable computing technology have allowed continuous health monitoring in large observational studies and clinical trials. Examples of data collected by wearable devices include minute-by-minute physical activity proxies measured by accelerometers or heart rate. The analysis of data generated by wearable devices has so far been quite limited to crude summaries, for example, the mean activity count over the day. To better utilize the full data and account for the dynamics of activity level in the time domain, we introduce a two-stage regression model for the minute-by-minute physical activity proxy data. The model allows for both time-varying parameters and time-invariant parameters, which helps capture both the transition dynamics between active/inactive periods (Stage 1) and the activity intensity dynamics during active periods (Stage 2). The approach extends methods developed for zero-inflated Poisson data to account for the high dimensionality and time-dependence of the high-density data generated by wearable devices.