Unsupervised Assessment of Frailty Status Using Wearable Sensors: A Feasibility Study among Community-Dwelling Older Adults.

0 REHABILITATION

Advances in rehabilitation science and practice Pub Date : 2025-02-15 eCollection Date: 2025-01-01 DOI:10.1177/27536351241311845

Oonagh Mary Giggins, Grainne Vavasour, Julie Doyle

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Abstract

Objectives: This study examined whether community-dwelling older adults can independently capture wearable sensor data that can be used to classify frailty status.

Methods: Fifty-one older adults (age 77.5 ± 8.4 years, height 163.6 77.5 ± 8.4, weight 72.0 ± 13.5 kg, female 76%) took part in this investigation. Participants independently captured physical activity and physical function data at home using a smartwatch and a research-grade inertial sensor system for 48-hours. Machine learning classifiers were used to determine whether the data obtained can discriminate between frailty levels.

Results: Models incorporating variables from both the smartwatch and inertial sensor system were successful in the prediction of frailty status.

Discussion: This study has demonstrated the ability of older adults to collect data which can be used to indicate their frailty risk. This may enable earlier intervention and lessen the impact of frailty on the individual and society as a whole.

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使用可穿戴传感器的无监督衰弱状态评估：在社区居住老年人中的可行性研究。

目的：本研究考察了居住在社区的老年人是否可以独立捕获可穿戴传感器数据，这些数据可用于对虚弱状态进行分类。方法：51例老年人（年龄77.5±8.4岁，身高163.6 77.5±8.4，体重72.0±13.5 kg，女性76%）参加调查。参与者在家中使用智能手表和研究级惯性传感器系统独立捕获48小时的身体活动和身体功能数据。使用机器学习分类器来确定获得的数据是否可以区分脆弱程度。结果：结合智能手表和惯性传感器系统变量的模型在预测虚弱状态方面是成功的。讨论：这项研究证明了老年人收集数据的能力，这些数据可以用来表明他们的衰弱风险。这可能使早期干预成为可能，并减轻虚弱对个人和整个社会的影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Advances in rehabilitation science and practice

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