Digital biomarkers of physical frailty and frailty phenotypes using sensor-based physical activity and machine learning

Catherine Park, Ramkinker Mishra, Jonathan Golledge, Bijan Najafi

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Remote monitoring of physical frailty is important to personalize care for slowing down the frailty process and/or for the healthy recovery of older adults following acute or chronic stressors. Taking the Fried frailty criteria as a reference to determine physical frailty and frailty phenotypes (slowness, weakness, exhaustion, inactivity), this study aimed to explore the benefit of machine learning to determine the least number of digital biomarkers of physical frailty measurable from a pendant sensor during activities of daily living. Two hundred and fifty-nine older adults were classified into robust or pre-frail/frail groups based on the physical frailty assessments by the Fried frailty criteria. All participants wore a pendant sensor at the sternum level for 48 h. Of seventeen sensor-derived features extracted from a pendant sensor, fourteen significant features were used for machine learning based on logistic regression modeling and a recursive feature elimination technique incorporating bootstrapping. The combination of percentage time standing, percentage time walking, walking cadence, and longest walking bout were identified as optimal digital biomarkers of physical frailty and frailty phenotypes. These findings suggest that a combination of sensor-measured exhaustion, inactivity, and speed have potential to screen and monitor people for physical frailty and frailty phenotypes.

Original languageEnglish (US)
Article number5289
JournalSensors
Volume21
Issue number16
DOIs
StatePublished - Aug 2 2021

Keywords

  • Digital biomarkers
  • Digital health
  • Digital twins
  • Frailty phenotype
  • Machine learning
  • Older adults
  • Physical activity
  • Physical frailty
  • Remote patient monitoring
  • Wearable

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

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