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