Tyndall National Institute, University College Cork, Lee Maltings, Prospect Row, Cork, T12R5CP, Ireland.
Aging Clin Exp Res. 2024 May 8;36(1):108. doi: 10.1007/s40520-024-02757-z.
Wrist-worn activity monitors have seen widespread adoption in recent times, particularly in young and sport-oriented cohorts, while their usage among older adults has remained relatively low. The main limitations are in regards to the lack of medical insights that current mainstream activity trackers can provide to older subjects. One of the most important research areas under investigation currently is the possibility of extrapolating clinical information from these wearable devices.
The research question of this study is understanding whether accelerometry data collected for 7-days in free-living environments using a consumer-based wristband device, in conjunction with data-driven machine learning algorithms, is able to predict hand grip strength and possible conditions categorized by hand grip strength in a general population consisting of middle-aged and older adults.
The results of the regression analysis reveal that the performance of the developed models is notably superior to a simple mean-predicting dummy regressor. While the improvement in absolute terms may appear modest, the mean absolute error (6.32 kg for males and 4.53 kg for females) falls within the range considered sufficiently accurate for grip strength estimation. The classification models, instead, excel in categorizing individuals as frail/pre-frail, or healthy, depending on the T-score levels applied for frailty/pre-frailty definition. While cut-off values for frailty vary, the results suggest that the models can moderately detect characteristics associated with frailty (AUC-ROC: 0.70 for males, and 0.76 for females) and viably detect characteristics associated with frailty/pre-frailty (AUC-ROC: 0.86 for males, and 0.87 for females).
The results of this study can enable the adoption of wearable devices as an efficient tool for clinical assessment in older adults with multimorbidities, improving and advancing integrated care, diagnosis and early screening of a number of widespread diseases.
腕戴式活动监测器近年来得到了广泛应用，尤其是在年轻人和运动群体中，而在老年人中的使用仍然相对较低。目前主流的活动追踪器主要存在缺乏能为老年人群体提供医学见解的局限性。目前正在研究的一个最重要的研究领域是能否从这些可穿戴设备中推断出临床信息。
本研究的研究问题是了解使用基于消费者的腕带设备在自由生活环境中收集 7 天的加速度计数据，结合数据驱动的机器学习算法，是否能够预测手握力以及一般中年和老年人人群中按手握力分类的可能状况。
回归分析的结果表明，开发的模型性能明显优于简单的均值预测哑回归器。虽然在绝对值上的提高可能看起来微不足道，但平均绝对误差（男性为 6.32 公斤，女性为 4.53 公斤）在考虑到握力估计足够准确的范围内。分类模型则擅长根据应用于虚弱/衰弱定义的 T 分数水平将个体分类为虚弱/衰弱前或健康。虽然虚弱的截止值有所不同，但结果表明，该模型可以适度检测与虚弱相关的特征（男性的 AUC-ROC：0.70，女性的 AUC-ROC：0.76），并且可以有效地检测与虚弱/衰弱前相关的特征（男性的 AUC-ROC：0.86，女性的 AUC-ROC：0.87）。
本研究的结果可以使可穿戴设备成为患有多种合并症的老年人临床评估的有效工具，改善和推进综合护理、诊断和广泛疾病的早期筛查。
