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

Park, Catherine, Mishra, Ramkinker, Golledge, Jonathan, and Najafi, Bijan (2021) Digital biomarkers of physical frailty and frailty phenotypes using sensor-based physical activity and machine learning. Sensors, 21 (16). 5289.

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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.

Item ID: 70196
Item Type: Article (Research - C1)
ISSN: 1424-8220
Keywords: Digital biomarkers, Digital health, Digital twins, Frailty phenotype, Machine learning, Older adults, Physical activity, Physical frailty, Remote patient monitoring, Wearable
Copyright Information: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Funders: National Health and Medical Research Council of Australia (NHMRC)
Projects and Grants: NHMRC 117061, NHMRC 1180736
Date Deposited: 05 Apr 2022 04:43
FoR Codes: 32 BIOMEDICAL AND CLINICAL SCIENCES > 3201 Cardiovascular medicine and haematology > 320199 Cardiovascular medicine and haematology not elsewhere classified @ 100%
SEO Codes: 20 HEALTH > 2001 Clinical health > 200105 Treatment of human diseases and conditions @ 100%
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