Detecting Frailty Using Wearable Device-Measured 24-Hour Movement Behaviors in Older Adults

Abstract

Frailty is a common geriatric syndrome associated with an increased risk of adverse health outcomes, yet its assessment in clinical and research settings remains limited due to reliance on subjective measures and time constraints. The goal of this dissertation was to improve frailty assessment in older adults by leveraging wearable device–measured 24-hour movement behaviors through two approaches: (1) detecting frailty using machine learning models based on accelerometer-derived movement behaviors, and (2) refining the Fried Frailty Phenotype (FFP) by replacing the self-reported low physical activity criterion with accelerometer-measured physical activity. Study 1 was a cross-sectional study involving 44 older adults who wore a thigh-worn accelerometer (ActivPAL) for 10 consecutive days. Frailty was assessed using both the FFP and the Comprehensive Geriatric Assessment–Frailty Index (CGA-FI). Machine learning models were developed to classify frailty status, detect individual FFP components, and estimate CGA-FI scores based on 24-hour movement behavior features. Results showed that the Random Forest model had higher AUC for FFP-defined frailty, while the K-Nearest Neighbors model performed best for CGA-FI-defined frailty. The Support Vector Machine showed higher accuracy for predicting CGA-FI continuous scores. Key predictors for detecting frailty included mean steps/day and variability of stepping and standing time, and time in stepping cadence ≥100 steps/min. Study 2 was a prospective cohort study using data from 38,429 UK Biobank participants aged ≥60 years with valid wrist-worn accelerometer data, modified FFP data (FFP-Mod), and mortality follow-up. Two revised FFP definitions were created by substituting the self-reported low physical activity criterion with accelerometer-measured physical activity including the lowest quintile of: 1) overall mean acceleration (FFP-Acc) and 2) time in MVPA (FFP-MVPA). Results revealed that while individuals classified as frail or prefrail by all three FFP definitions had significantly higher all-cause mortality risk compared to robust individuals, these associations were stronger for FFP-Acc and FFP-MVPA than for FFP-Mod. In conclusion, this dissertation supports the feasibility and utility of integrating wearable-derived movement behavior metrics into frailty assessments. Machine learning models using 24-hour movement behaviors can detect frailty, and replacing self-reported physical activity with accelerometer-measured physical activity improves the predictive validity of FFP. This work contributes to the development of objective and clinically relevant frailty assessments for use in both research and public health applications.Study 1 was supported by the National Institute on Aging (P30AG073107) through the MassAITC pilot project. Study 2 was supported by the Mutual Mentoring Grant from the Office of Faculty Development at the University of Massachusetts Amherst.Doctor of Philosophy (Ph.D.