Online Program Home
My Program

Abstract Details

Activity Number: 470 - Biomarker Evaluation and Winning Student Papers on Medical Devices and Diagnostics
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Medical Devices and Diagnostics
Abstract #302882
Title: Analyzing Wearable Device Data Using Marked Point Processes
Author(s): Yuchen Yang* and Mei-Cheng Wang
Companies: Johns Hopkins University and Johns Hopkins University
Keywords: Discrete point process; Estimating equation; Rate function; Transition probability; Window censoring

We introduce two sets of measures as exploratory tools to study physical activity patterns: active-to-sedentary/sedentary-to-active rate function (ASRF/SARF), and active/sedentary rate function (ARF/SRF). These two sets of measures are complementary to each other and can be effectively used together. The specific features are illustrated by an analysis of wearable device data from National Health and Nutrition Examination Survey (NHANES). A two-level semiparametric regression model for ARF and the associated activity magnitude is developed under a unified framework using the marked point process formulation. The inactive and active states measured by accelerometers are treated as 0-1 point process, and the activity magnitude measured at each active state is defined as a marked variable. The commonly encountered missing data problem due to device non-wear is referred to as “window censoring,” which is handled by a proper estimation approach that adopts techniques from recurrent event data. Large sample property of the estimator and comparison between two regression models as measurement frequency increases are studied. Simulation and NHANES data analysis results are presented.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2019 program