TY - JOUR
T1 - A semiparametric model for wearable sensor-based physical activity monitoring data with informative device wear
AU - Song, Jaejoon
AU - Swartz, Michael D.
AU - Gabriel, Kelley Pettee
AU - Basen-Engquist, Karen
N1 - Publisher Copyright:
© The Author 2018. Published by Oxford University Press. All rights reserved.
PY - 2019/4/1
Y1 - 2019/4/1
N2 - Wearable sensors provide an exceptional opportunity in collecting real-Time behavioral data in free living conditions. However, wearable sensor data from observational studies often suffer from information bias, since participants' willingness to wear the monitoring devices may be associated with the underlying behavior of interest. The aim of this study was to introduce a semiparametric statistical approach for modeling wearable sensor-based physical activity monitoring data with informative device wear. Our simulation study indicated that estimates from the generalized estimating equations showed ignorable bias when device wear patterns were independent of the participants physical activity process, but incrementally more biased when the patterns of device non-wear times were increasingly associated with the physical activity process. The estimates from the proposed semiparametric modeling approach were unbiased both when the device wear patterns were (i) independent or (ii) dependent to the underlying physical activity process. We demonstrate an application of this method using data from the 2003-2004 National Health and Nutrition Examination Survey (N=4518), to examine gender differences in physical activity measured using accelerometers. The semiparametric model can be implemented using our R package acc, free software developed for reading, processing, simulating, visualizing, and analyzing accelerometer data, publicly available at the Comprehensive R Archive Network.
AB - Wearable sensors provide an exceptional opportunity in collecting real-Time behavioral data in free living conditions. However, wearable sensor data from observational studies often suffer from information bias, since participants' willingness to wear the monitoring devices may be associated with the underlying behavior of interest. The aim of this study was to introduce a semiparametric statistical approach for modeling wearable sensor-based physical activity monitoring data with informative device wear. Our simulation study indicated that estimates from the generalized estimating equations showed ignorable bias when device wear patterns were independent of the participants physical activity process, but incrementally more biased when the patterns of device non-wear times were increasingly associated with the physical activity process. The estimates from the proposed semiparametric modeling approach were unbiased both when the device wear patterns were (i) independent or (ii) dependent to the underlying physical activity process. We demonstrate an application of this method using data from the 2003-2004 National Health and Nutrition Examination Survey (N=4518), to examine gender differences in physical activity measured using accelerometers. The semiparametric model can be implemented using our R package acc, free software developed for reading, processing, simulating, visualizing, and analyzing accelerometer data, publicly available at the Comprehensive R Archive Network.
KW - Accelerometry
KW - Augmented estimating equations
KW - Information bias
KW - Semiparametric regression model
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U2 - 10.1093/biostatistics/kxx073
DO - 10.1093/biostatistics/kxx073
M3 - Article
C2 - 29415194
AN - SCOPUS:85060093562
SN - 1465-4644
VL - 20
SP - 287
EP - 298
JO - Biostatistics
JF - Biostatistics
IS - 2
ER -