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

Catherine Park, Ramkinker Mishra, Jonathan Golledge, Bijan Najafi, Catherine Park, Ramkinker Mishra, Jonathan Golledge, Bijan Najafi

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.

Keywords: digital biomarkers; digital health; digital twins; frailty phenotype; machine learning; older adults; physical activity; physical frailty; remote patient monitoring; wearable.

Conflict of interest statement

While the overlap with this study is minimal, using activity monitoring to determine frailty is protected by a patent pending (US20150272511 A1). The patent is owned by University of Arizona, and B.N. is listed as a co-inventor on this patent pending. B.N. served as a consultant for BioSensics LLC, which is the manufacturer of the PAMSys used in this study. However, his consultation is not related to the scope of this study and he was not involved in data analysis. Other author(s) declare no potential conflict of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1
Figure 1
The PAMSys wearable sensor and its placement.
Figure 2
Figure 2
Results of optimal feature selection using machine learning. Error bars indicate 95% confidence intervals. AUC, PPV, and NPV indicate area under the receiver operating characteristic curve, positive predictive value, and negative predictive value, respectively.

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