Evaluation of Falls Risk in Community-Dwelling Older Adults Using Body-Worn Sensors
Evaluation of Falls Risk in Community-Dwelling Older Adults Using Body-Worn Sensors
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DOI：
10.1159/000337259
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发表时间：
2012-01-01
期刊：
影响因子：
3.5
通讯作者：
Kenny, Rose A.
中科院分区：
文献类型：
--
作者：
Greene, Barry R.;Doheny, Emer P.;Kenny, Rose A.
背景：跌倒是全世界老年人受伤和住院的最常见原因，也是导致老年人死亡和残疾的主要原因之一。这项研究的目的是确定一种基于身体佩戴的传感器数据的方法是否可以前瞻性地预测社区老年人的跌倒，并将其预测性能与同一数据集上的两种标准方法进行比较。方法：研究对象为226名社区老年人(平均年龄71.5±6.7岁，女性164人)，使用安装在左右小腿上的穿戴传感器获取数据，以量化步态和肢体活动，同时在一家老年研究诊所进行“计时起跑”(TUG)测试。参与者在最初评估两年后通过电话联系，以确定他们是否摔倒了。这些结果数据被用来创建统计模型来预测跌倒。结果：通过交叉验证获得的结果，在前瞻性地识别在随访期间跌倒的参与者方面，平均分类准确率为79.69%(平均95%可信区间：77.09-82.34)。结果显示(p<；0.0001)比两种标准的跌倒风险评估方法(手动定时拖船和Berg平衡评分)更准确，平均分类准确率分别为59.43%(95%可信区间：58.07~60.84)和64.30%(95%可信区间：62.56~66.09)。结论：结果表明，使用身体佩戴的传感器对拖船试验中的运动进行量化可能会导致一种可靠的方法来评估未来的跌倒风险。版权所有(C)2012 S.Karger AG，巴塞尔
Background: Falls are the most common cause of injury and hospitalization and one of the principal causes of death and disability in older adults worldwide. This study aimed to determine if a method based on body-worn sensor data can prospectively predict falls in community-dwelling older adults, and to compare its falls prediction performance to two standard methods on the same data set. Methods: Data were acquired using body-worn sensors, mounted on the left and right shanks, from 226 community-dwelling older adults (mean age 71.5 +/- 6.7 years, 164 female) to quantify gait and lower limb movement while performing the 'Timed Up and Go' (TUG) test in a geriatric research clinic. Participants were contacted by telephone 2 years following their initial assessment to determine if they had fallen. These outcome data were used to create statistical models to predict falls. Results: Results obtained through cross-validation yielded a mean classification accuracy of 79.69% (mean 95% CI: 77.09-82.34) in prospectively identifying participants that fell during the follow-up period. Results were significantly (p < 0.0001) more accurate than those obtained for falls risk estimation using two standard measures of falls risk (manually timed TUG and the Berg balance score, which yielded mean classification accuracies of 59.43% (95% CI: 58.07-60.84) and 64.30% (95% CI: 62.56-66.09), respectively). Conclusion: Results suggest that the quantification of movement during the TUG test using body-worn sensors could lead to a robust method for assessing future falls risk. Copyright (C) 2012 S. Karger AG, Basel