Zheng Xiaoping, Zeng Ziwei, S van Schooten Kimberley, Yang Yijian
Department of Sports Science and Physical Education, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong).
Neuroscience Research Australia, University of New South Wales, Sydney, Australia.
JMIR Aging. 2025 Sep 15;8:e77140. doi: 10.2196/77140.
Frailty affects over 50% of older adults in long-term care (LTC), and early detection is critical due to its potential reversibility. Wearable sensors enable continuous monitoring of gait and physical activity, and machine learning has shown promise in detecting frailty among community-dwelling older adults. However, its applicability in LTC remains underexplored. Furthermore, dynamic gait outcomes (eg, gait stability and symmetry) may offer more sensitive frailty indicators than traditional measures like gait speed, yet their potential remains largely untapped.
This study aimed to evaluate whether frailty in LTC facilities could be effectively identified using machine learning models trained on gait and daily physical activity data derived from a single accelerometer.
This study is a cross-sectional secondary analysis of baseline data from a 2-arm cluster randomized controlled trial. Of the 164 individuals initially enrolled, 51 participants (age: mean 85.0, SD 9.0 years; female: n=24, 47.1%) met the inclusion criteria of completing all assessments required for this study and were included in the final analysis. Frailty status was assessed using the fatigue, resistance, ambulation, incontinence, loss of weight, nutritional approach, and help with dressing (FRAIL-NH) scale. Participants completed a 5-meter walking task while wearing a 3D accelerometer. Following this task, the accelerometer was used to record daily physical activity over approximately 1 week. A total of 34 dynamic and spatial-temporal gait outcomes, 3 physical activity variables, and 6 demographic characteristics were extracted. Five conventional machine learning models were trained to classify frailty status using a leave-one-out cross-validation approach. Model performance was evaluated based on accuracy and the area under the receiver operating characteristic curve. To enhance model interpretability, explainable artificial intelligence techniques were used to identify the most influential predictive outcomes.
The extreme gradient boosting model demonstrated the optimal performance with an accuracy of 86.3% and an area under the curve of 0.92. Explainable artificial intelligence analysis revealed that older adults with frailty exhibited more variable, complex, and asymmetric gait patterns, which were characterized by higher stride length variability, increased sample entropy, and a higher gait symmetry score.
Our findings suggest that dynamic gait outcomes may serve as more sensitive indicators of frailty than spatial-temporal gait outcomes (eg, gait speed) in LTC settings, offering valuable insights for enhancing frailty detection and management.
衰弱影响了超过50%的长期护理（LTC）老年人，由于其具有潜在的可逆性，早期检测至关重要。可穿戴传感器能够持续监测步态和身体活动，机器学习在检测社区居住老年人的衰弱方面已显示出前景。然而，其在长期护理中的适用性仍未得到充分探索。此外，动态步态结果（如步态稳定性和对称性）可能比步态速度等传统指标提供更敏感的衰弱指标，但其潜力在很大程度上仍未被挖掘。
本研究旨在评估使用基于来自单个加速度计的步态和日常身体活动数据训练的机器学习模型，能否有效识别长期护理机构中的衰弱情况。
本研究是对一项双臂整群随机对照试验基线数据的横断面二次分析。最初纳入的164名个体中，51名参与者（年龄：平均85.0岁，标准差9.0岁；女性：n = 24，47.1%）符合完成本研究所需的所有评估的纳入标准，并被纳入最终分析。使用疲劳、阻力、行走、失禁、体重减轻、营养方法和穿衣帮助（FRAIL-NH）量表评估衰弱状态。参与者佩戴3D加速度计完成5米步行任务。在此任务之后，加速度计用于记录大约1周的日常身体活动。总共提取了34个动态和时空步态结果、3个身体活动变量和6个人口统计学特征。使用留一法交叉验证方法训练五个传统机器学习模型来对衰弱状态进行分类。基于准确性和受试者操作特征曲线下面积评估模型性能。为了提高模型的可解释性，使用可解释人工智能技术来识别最具影响力的预测结果。
极端梯度提升模型表现出最佳性能，准确率为86.3%，曲线下面积为0.92。可解释人工智能分析表明，衰弱的老年人表现出更多可变、复杂和不对称的步态模式，其特征为步长变异性更高、样本熵增加和步态对称得分更高。
我们的研究结果表明，在长期护理环境中，动态步态结果可能比时空步态结果（如步态速度）更敏感地反映衰弱情况，为加强衰弱检测和管理提供了有价值的见解。
