Automated, ecologic assessment of frailty using a wrist-worn device

Abstract

The COVID-19 pandemic has considerably shifted the focus of scientific research, speeding up the process of digitizing medical monitoring. Wearable technology is already widely used in medical research, as it has the potential to monitor the user’s physical activity in daily life. This study aims to explore in-home collected wearable-derived signals for frailty status assessment. A sample of 35 subjects aged 70+, autonomous in basic activities of daily living and cognitively intact, was collected. After being clinically assessed for frailty according to Fried’s phenotype, participants wore a wrist device equipped with inertial motion sensors for 24 h, during which they led their usual life in their homes. Signal-derived traces were split into 10-s segments and labeled classified as gaits, other motor activities, or rests. Gait and other motor activity segments were used to calculate the Subject Activity Level (SAL), an index to quantify how users were active throughout the day. The SAL index was then combined with gait-derived features to design a novel frailty status assessment algorithm. In particular, subjects were classified as robust or non-robust, a category that includes both Fried’s frail and pre-frail phenotypes. For some users, activity levels alone enabled accurate frailty assessment, whereas, for others, a Gaussian Naive Bayes classifier based on the gait-derived features was required to assess frailty status. Overall, the proposed method showed extremely promising results, allowing discrimination of robust and non-robust subjects with an overall 91% accuracy, stemming from 95% sensitivity and 88% specificity. This study demonstrates the potential of unobtrusive, wearable devices in objectively assessing frailty through unsupervised monitoring in real-world settings.