Year: 2024
Type: article
Abstract: 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.
Source: Sensors
DOI: 10.3390/s24217053
Authors: Hongzhe Zhang, Jihui L. Diaz, Soohyun Kim, Zilong Yu, Yiyuan Wu, Emily Carter, Samprit Banerjee
Institutions: Weill Cornell Medicine, Cornell University
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,955
Awards: G5128213490, G6914398972
OpenAlex ID: W4403978200