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Sensors 2018, 18(9), 3056; https://doi.org/10.3390/s18093056

Quantile Coarsening Analysis of High-Volume Wearable Activity Data in a Longitudinal Observational Study

1
Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
2
IBM Watson Research Center, Yorktown Heights, NY 10598, USA
3
Center for Behavioral Cardiovascular Health, Department of Medicine, Columbia University Medical Center, New York, NY 10032, USA
4
Department of Neurology, Columbia University Medical Center, New York, NY 10032, USA
*
Author to whom correspondence should be addressed.
Received: 13 August 2018 / Revised: 4 September 2018 / Accepted: 6 September 2018 / Published: 12 September 2018
(This article belongs to the Special Issue Data Analytics and Applications of the Wearable Sensors in Healthcare)
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Abstract

Owing to advances in sensor technologies on wearable devices, it is feasible to measure physical activity of an individual continuously over a long period. These devices afford opportunities to understand individual behaviors, which may then provide a basis for tailored behavior interventions. The large volume of data however poses challenges in data management and analysis. We propose a novel quantile coarsening analysis (QCA) of daily physical activity data, with a goal to reduce the volume of data while preserving key information. We applied QCA to a longitudinal study of 79 healthy participants whose step counts were monitored for up to 1 year by a Fitbit device, performed cluster analysis of daily activity, and identified individual activity signature or pattern in terms of the clusters identified. Using 21,393 time series of daily physical activity, we identified eight clusters. Employment and partner status were each associated with 5 of the 8 clusters. Using less than 2% of the original data, QCA provides accurate approximation of the mean physical activity, forms meaningful activity patterns associated with individual characteristics, and is a versatile tool for dimension reduction of densely sampled data. View Full-Text
Keywords: citizen science; cluster analysis; physical activity; sedentary behavior; walking citizen science; cluster analysis; physical activity; sedentary behavior; walking
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Cheung, Y.K.; Hsueh, P.-Y.S.; Ensari, I.; Willey, J.Z.; Diaz, K.M. Quantile Coarsening Analysis of High-Volume Wearable Activity Data in a Longitudinal Observational Study. Sensors 2018, 18, 3056.

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