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.

中文翻译：

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使用基于传感器的身体活动和机器学习的身体虚弱和虚弱表型的数字生物标志物

身体虚弱的远程监测对于个性化护理很重要，以减缓虚弱过程和/或老年人在急性或慢性压力源后的健康康复。以 Fried 虚弱标准为参考，确定身体虚弱和虚弱表型（缓慢、虚弱、疲惫、不活动），本研究旨在探索机器学习的好处，以确定可从吊坠测量的身体虚弱数字生物标志物的最少数量日常生活活动中的传感器。根据 Fried 虚弱标准的身体虚弱评估，259 名老年人被分为健壮组或虚弱前/虚弱组。所有参与者在胸骨水平佩戴吊坠传感器 48 小时。在从悬垂传感器中提取的 17 个传感器衍生特征中，十四个重要特征被用于基于逻辑回归建模和结合自举的递归特征消除技术的机器学习。站立时间百分比、步行时间百分比、步行节奏和最长步行回合的组合被确定为身体虚弱和虚弱表型的最佳数字生物标志物。这些发现表明，传感器测量的疲劳、不活动和速度的组合有可能筛查和监测人们的身体虚弱和虚弱表型。和最长的步行回合被确定为身体虚弱和虚弱表型的最佳数字生物标志物。这些发现表明，传感器测量的疲劳、不活动和速度的组合有可能筛查和监测人们的身体虚弱和虚弱表型。和最长的步行回合被确定为身体虚弱和虚弱表型的最佳数字生物标志物。这些发现表明，传感器测量的疲劳、不活动和速度的组合有可能筛查和监测人们的身体虚弱和虚弱表型。