Digital Biomarkers of Physical Frailty and Frailty Phenotypes Using Sensor-Based Physical Activity and Machine Learning.

Catherine Park,Bijan Najafi,Jonathan Golledge,Ramkinker Mishra

doi:10.3390/s21165289

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.

Highlights

Physical frailty is a clinical syndrome characterized by decreased physiological reserve and function [1]
This study examined the association between the physical activity features measurable remotely from a pendant sensor and physical frailty and its phenotypes based on machine learning and modeling of physical frailty and physical phenotypes
While reliable functional tests such as balance, gait, five-times sit-to-stand, and timed up-and-go recorded from wearable sensors have been reported to be associated with functional performance and physical frailty [22,23,24,27,28,38,39], the test results were based on supervised assessments of motor performance that are unsuitable for the remote monitoring of physical frailty under unsupervised conditions

Summary

Introduction

Physical frailty is a clinical syndrome characterized by decreased physiological reserve and function [1] It is prevalent in older adults [2,3] and is typically a chronic and progressive health condition related to the aging process [4,5,6]. Physical frailty increases the risk of adverse health outcomes, such as falls, disability, hospital admission, and mortality [3,7,8]. It is associated with increased requirement for medical care [9] and higher health care costs [10]. The health professional calculates the frailty index as a ratio between the number of identified deficits and the number assessed

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