Kelly D, Condell J, Gillespie J, Munoz Esquivel K, Barton J, Tedesco S, Nordstrom A, Åkerlund Larsson M, Alamäki A
Ulster University, Northern Ireland, United Kingdom.
Ulster University, Northern Ireland, United Kingdom.
J Biomed Inform. 2022 Jul;131:104116. doi: 10.1016/j.jbi.2022.104116. Epub 2022 Jun 8.
Falls are one of the most costly population health issues. Screening of older adults for fall risks can allow for earlier interventions and ultimately lead to better outcomes and reduced public health spending. This work proposes a solution to limitations in existing fall screening techniques by utilizing a hip-based accelerometer worn in free-living conditions. The work proposes techniques to extract fall risk features from periods of free-living ambulatory activity. Analysis of the proposed techniques is conducted and compared with existing screening methods using Functional Tests and Lab-based Gait Analysis. 1705 Older Adults from Umea (Sweden) were assessed. Data consisted of 1 Week of hip worn accelerometer data, gait measurements and performance metrics for 3 functional tests. Retrospective and Prospective fall data were also recorded based on the incidence of falls occurring 12 months before and after the study commencing respectively. Machine learning based experiments show accelerometer based measures perform best when predicting falls. Prospective falls had a sensitivity and specificity of 0.61 and 0.66 respectively while retrospective falls had a sensitivity and specificity of 0.61 and 0.68 respectively.
跌倒属于成本最高的人群健康问题之一。对老年人进行跌倒风险筛查有助于更早地进行干预，并最终带来更好的结果，降低公共卫生支出。这项研究提出了一种解决方案，通过在日常生活环境中佩戴基于髋部的加速度计来克服现有跌倒筛查技术的局限性。该研究提出了从日常动态活动期间提取跌倒风险特征的技术。对所提出的技术进行了分析，并与使用功能测试和基于实验室的步态分析的现有筛查方法进行了比较。对来自瑞典于默奥市的1705名老年人进行了评估。数据包括一周佩戴在髋部的加速度计数据、步态测量数据以及三项功能测试的性能指标。还分别根据研究开始前12个月和开始后跌倒的发生率记录了回顾性和前瞻性跌倒数据。基于机器学习的实验表明，基于加速度计的测量方法在预测跌倒时表现最佳。前瞻性跌倒的灵敏度和特异性分别为0.61和0.66，而回顾性跌倒的灵敏度和特异性分别为0.…61和0.68。（原文中retrospective falls had a sensitivity and specificity of 0.61 and 0.68 respectively这里0.68前少了个数字0，译文保留原文情况）
