Frailty status can be accurately assessed using inertial sensors and the TUG test
Frailty status can be accurately assessed using inertial sensors and the TUG test
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DOI：
10.1093/ageing/aft176
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发表时间：
2014-05-01
期刊：
影响因子：
6.7
通讯作者：
Kenny, Rose Anne
中科院分区：
文献类型：
--
作者：
Greene, Barry R.;Doheny, Emer P.;Kenny, Rose Anne
背景：虚弱是一种重要的老年综合征，与死亡率、发病率和跌倒风险增加有关。方法：采用Fry的虚弱表型和计时起跑(TUG)试验对399名社区老年人进行评估。使用安装在腿上的惯性传感器对测试进行量化。我们报告了一种基于回归的方法，利用拖轮过程中获得的惯性传感器数据来评估易损性。结果：使用惯性传感器数据将受试者分为虚弱和非虚弱两类，平均准确率为75.20%(按性别分层)。仅用拖轮时间对脆弱状态进行正确分类，平均分类正确率为71.82%。同样，仅用握力对脆性状态进行正确分类，平均分类准确率为77.65%。与同等的基于时间的手动拖轮模型相比，按性别对传感器数据进行分层显著提高了脆弱性分类的准确性。结论：结果表明，使用众所周知的机动性测试(Timed Up and Go(TUG))和惯性传感器进行评估的简单方案可以成为一种快速而有效的自动、非专家评估脆弱性的方法。
Background: frailty is an important geriatric syndrome linked to increased mortality, morbidity and falls risk.Methods: a total of 399 community-dwelling older adults were assessed using Fried's frailty phenotype and the timed up and go (TUG) test. Tests were quantified using shank-mounted inertial sensors. We report a regression-based method for assessment of frailty using inertial sensor data obtained during TUG. For comparison, frailty was also assessed using the same method based on grip strength and manual TUG time.Results: using inertial sensor data, participants were classified as frail or non-frail with mean accuracy of 75.20% (stratified by gender). Using TUG time alone, frailty status was classified correctly with mean classification accuracy of 71.82%. Similarly, using grip strength alone, the frailty status was classified correctly with mean classification accuracy of 77.65%. Stratifying sensor data by gender yielded significantly (p < 0.05) increased accuracy in classifying frailty when compared with equivalent manual TUG time-based models.Conclusion: results suggest that a simple protocol involving assessment using a well-known mobility test (Timed Up and Go (TUG)) and inertial sensors can be a fast and effective means of automatic, non-expert assessment of frailty.