 A two†stage model for wearable device dataJiawei Bai, Yifei Sun, Jennifer A. Schrack, Ciprian M. Crainiceanu and Mei†Cheng Wang Biometrics, 2018, vol. 74, issue 2, 744-752 Abstract: Recent advances of 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. Methods are motivated by and applied to the Baltimore Longitudinal Study of Aging. Date: 2018 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:biomet:v:74:y:2018:i:2:p:744-752 Ordering information: This journal article can be ordered from Access Statistics for this article More articles in Biometrics from The International Biometric Society |
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