Faculty of Engineering, Nara Women's University, Nara, Japan.
Department of Cooperative Major in Human Centered Engineering, Graduate School of Humanities and Sciences, Ochanomizu University, Tokyo, Japan.
BMC Geriatr. 2022 Sep 12;22(1):746. doi: 10.1186/s12877-022-03425-5.
Frailty and falls are two adverse characteristics of aging that impair the quality of life of senior people and increase the burden on the healthcare system. Various methods exist to evaluate frailty, but none of them are considered the gold standard. Technological methods have also been proposed to assess the risk of falling in seniors. This study aims to propose an objective method for complementing existing methods used to identify the frail state and risk of falling in older adults.
A total of 712 subjects (age: 71.3 ± 8.2 years, including 505 women and 207 men) were recruited from two Japanese cities. Two hundred and three people were classified as frail according to the Kihon Checklist. One hundred and forty-two people presented with a history of falling during the previous 12 months. The subjects performed a 45 s standing balance test and a 20 m round walking trial. The plantar pressure data were collected using a 7-sensor insole. One hundred and eighty-four data features were extracted. Automatic learning random forest algorithms were used to build the frailty and faller classifiers. The discrimination capabilities of the features in the classification models were explored.
The overall balanced accuracy for the recognition of frail subjects was 0.75 ± 0.04 (F1-score: 0.77 ± 0.03). One sub-analysis using data collected for men aged > 65 years only revealed accuracies as high as 0.78 ± 0.07 (F1-score: 0.79 ± 0.05). The overall balanced accuracy for classifying subjects with a recent history of falling was 0.57 ± 0.05 (F1-score: 0.62 ± 0.04). The classification of subjects relative to their frailty state primarily relied on features extracted from the plantar pressure series collected during the walking test.
In the future, plantar pressures measured with smart insoles inserted in the shoes of senior people may be used to evaluate aspects of frailty related to the physical dimension (e.g., gait and balance alterations), thus allowing assisting clinicians in the early identification of frail individuals.
衰弱和跌倒都是与衰老相关的两个负面特征，它们降低了老年人的生活质量，并增加了医疗保健系统的负担。目前已经有多种评估衰弱的方法，但没有一种方法被认为是金标准。也已经提出了一些技术方法来评估老年人跌倒的风险。本研究旨在提出一种客观的方法来补充现有的用于识别老年人衰弱状态和跌倒风险的方法。
从日本的两个城市招募了 712 名受试者（年龄：71.3±8.2 岁，包括 505 名女性和 207 名男性）。203 人根据 Kihon Checklist 被分类为衰弱。142 人在过去 12 个月内有跌倒史。受试者进行了 45 秒站立平衡测试和 20 米往返行走试验。使用 7 传感器鞋垫采集足底压力数据。提取了 184 个数据特征。使用自动学习随机森林算法构建衰弱和跌倒分类器。探索了分类模型中特征的判别能力。
识别衰弱受试者的整体平衡准确率为 0.75±0.04（F1 评分：0.77±0.03）。仅对年龄 >65 岁的男性进行的一项子分析显示，准确率高达 0.78±0.07（F1 评分：0.79±0.05）。最近有跌倒史的受试者的整体平衡准确率为 0.57±0.05（F1 评分：0.62±0.04）。对衰弱状态的分类主要依赖于从行走试验中采集的足底压力序列中提取的特征。
未来，插入老年人鞋子中的智能鞋垫测量的足底压力可能用于评估与身体维度相关的衰弱方面（例如，步态和平衡改变），从而帮助临床医生早期识别衰弱个体。
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