Lai Ka-Ming, Fong Kenneth N K
Hong Kong Red Cross, Hong Kong SAR, China.
Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China.
Sensors (Basel). 2025 Apr 16;25(8):2516. doi: 10.3390/s25082516.
Falls pose a significant health risk for older people, necessitating accurate predictive tools for fall prevention. This study evaluated the sensitivity of a wearable waist-belt sensor, the Booguu Aspire system, in predicting prospective fall incidents among 37 community-dwelling older people in Hong Kong. A prospective cohort design was employed, involving two analytical groups: the overall cohort and a subset with cognitive performance data available, measured using the Montreal Cognitive Assessment (MoCA). Participants were categorized into moderate- or high-risk groups for falls using the sensor and further assessed with physical function tests, including the Single Leg Stand Test (SLST), 6 Meter Walk Test (6MWT), and Five Times Sit to Stand Test (5STS). Fall incidents were monitored for 12 months through quarterly follow-up phone calls. Statistical analyses showed no significant differences in physical performance between high- and moderate-risk groups and no significant correlations between sensor-based fall risk ratings and physical function test outcomes. The SLST, 6MWT, 5STS, and MoCA tests classified sensor-determined fall risk ratings with accuracies of 51.4%, 64.9%, 59.5%, and 50%. The sensor showed low sensitivity, with 13.51% true positives for fallers and a 20% sensitivity for high-risk individuals. ROC analysis yielded an Area Under the Curve of 0.688. Our findings indicate that the wearable waist-belt Sensor System may not be a sensitive tool in predicting prospective fall incidents. The algorithm for fall risk classification in the wearable sensor merits further exploration.
跌倒对老年人构成重大健康风险，因此需要准确的预测工具来预防跌倒。本研究评估了一种可穿戴式腰带传感器——Booguu Aspire系统，在预测香港37名社区居住老年人未来跌倒事件方面的敏感性。采用前瞻性队列设计，包括两个分析组：总体队列和有认知表现数据的子集，认知表现数据使用蒙特利尔认知评估量表（MoCA）进行测量。使用该传感器将参与者分为跌倒中度或高度风险组，并进一步通过身体功能测试进行评估，包括单腿站立测试（SLST）、6米步行测试（6MWT）和五次坐立测试（5STS）。通过每季度的随访电话监测跌倒事件12个月。统计分析表明，高风险组和中度风险组在身体表现上没有显著差异，基于传感器的跌倒风险评级与身体功能测试结果之间也没有显著相关性。SLST、6MWT、5STS和MoCA测试对传感器确定的跌倒风险评级进行分类的准确率分别为51.4%、64.9%、59.5%和50%。该传感器显示出低敏感性，跌倒者的真阳性率为13.51%，高风险个体的敏感性为20%。ROC分析得出曲线下面积为0.688。我们的研究结果表明，可穿戴式腰带传感器系统可能不是预测未来跌倒事件的敏感工具。可穿戴传感器中跌倒风险分类算法值得进一步探索。
