2SpamH: A Two-Stage Pre-Processing Algorithm for Passively Sensed mHealth Data.

Autor: Zhang H; Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medical College, New York, NY 10065, USA., Diaz JL; Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medical College, New York, NY 10065, USA., Kim S; Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medical College, New York, NY 10065, USA., Yu Z; Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medical College, New York, NY 10065, USA., Wu Y; Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medical College, New York, NY 10065, USA., Carter E; Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medical College, New York, NY 10065, USA., Banerjee S; Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medical College, New York, NY 10065, USA.; Weill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medical College, White Plains, NY 10605, USA.
Jazyk: angličtina
Zdroj: Sensors (Basel, Switzerland) [Sensors (Basel)] 2024 Oct 31; Vol. 24 (21). Date of Electronic Publication: 2024 Oct 31.
DOI: 10.3390/s24217053
Abstrakt: Recent advancements in mobile health (mHealth) technology and the ubiquity of wearable devices and smartphones have expanded a market for digital health and have emerged as innovative tools for data collection on individualized behavior. Heterogeneous levels of device usage across users and across days within a single user may result in different degrees of underestimation in passive sensing data, subsequently introducing biases if analyzed without addressing this issue. In this work, we propose an unsupervised 2-Stage Pre-processing Algorithm for Passively Sensed mHealth Data (2SpamH) algorithm that uses device usage variables to infer the quality of passive sensing data from mobile devices. This article provides a series of simulation studies to show the utility of the proposed algorithm compared to existing methods. Application to a real clinical dataset is also illustrated.
Databáze: MEDLINE
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