Predicting physical functioning status in older adults: insights from wrist accelerometer sensors and derived digital biomarkers of physical activity.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 39178361

Abstract

OBJECTIVE: Conventional physical activity (PA) metrics derived from wearable sensors may not capture the cumulative, transitions from sedentary to active, and multidimensional patterns of PA, limiting the ability to predict physical function impairment (PFI) in older adults. This study aims to identify unique temporal patterns and develop novel digital biomarkers from wrist accelerometer data for predicting PFI and its subtypes using explainable artificial intelligence techniques.

Authors

  • Lingjie Fan
    College of Computer Science, Sichuan University, Chengdu, Sichuan 610000, China.
  • Junhan Zhao
    Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02114, United States.
  • Yao Hu
    Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China.
  • Junjie Zhang
    Department of Immunology, School of Basic Medical Sciences, Anhui Medical University, Hefei, 230032, PR China.
  • Xiyue Wang
    College of Electrical Engineering and Information Technology, Sichuan University, 610065, China. Electronic address: xiyue.wang.scu@gmail.com.
  • Fengyi Wang
    Department of Rehabilitation Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610000, China.
  • Mengyi Wu
    School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400000, China.
  • Tao Lin

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