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单惯性传感器补充计时起立行走测试在评估老年疗养院居民跌倒风险方面具有高特异性。

High Specificity of Single Inertial Sensor-Supplemented Timed Up and Go Test for Assessing Fall Risk in Elderly Nursing Home Residents.

机构信息

Laboratoire d'Analyse du Mouvement et de la Posture (LAMP), Centre National de Rééducation Fonctionnelle et de Réadaptation-Rehazenter, Rue André Vésale 1, 2674 Luxembourg, Luxembourg.

Faculté des Sciences de la Motricité, Université Catholique de Louvain, Place Pierre de Coubertin 2, 1348 Ottignies-Louvain-la-Neuve, Belgium.

出版信息

Sensors (Basel). 2022 Mar 17;22(6):2339. doi: 10.3390/s22062339.

Abstract

The Timed Up and Go test (TUG) is commonly used to estimate the fall risk in the elderly. Several ways to improve the predictive accuracy of TUG (cameras, multiple sensors, other clinical tests) have already been proposed. Here, we added a single wearable inertial measurement unit (IMU) to capture the residents' body center-of-mass kinematics in view of improving TUG's predictive accuracy. The aim is to find out which kinematic variables and residents' characteristics are relevant for distinguishing faller from non-faller patients. Data were collected in 73 nursing home residents with the IMU placed on the lower back. Acceleration and angular velocity time series were analyzed during different subtasks of the TUG. Multiple logistic regressions showed that total time required, maximum angular velocity at the first half-turn, gender, and use of a walking aid were the parameters leading to the best predictive abilities of fall risk. The predictive accuracy of the proposed new test, called i + TUG, reached a value of 74.0%, with a specificity of 95.9% and a sensitivity of 29.2%. By adding a single wearable IMU to TUG, an accurate and highly specific test is therefore obtained. This method is quick, easy to perform and inexpensive. We recommend to integrate it into daily clinical practice in nursing homes.

摘要

计时起身行走测试(TUG)常用于评估老年人的跌倒风险。已经提出了几种方法来提高 TUG 的预测准确性(相机、多个传感器、其他临床测试)。在这里,我们在 TUG 中添加了单个可穿戴惯性测量单元(IMU),以捕捉居民的身体质心运动学,从而提高 TUG 的预测准确性。目的是找出哪些运动学变量和居民特征与区分跌倒者和非跌倒者患者有关。数据是在放置在背部的 IMU 下从 73 名疗养院居民中收集的。在 TUG 的不同子任务中分析了加速度和角速度时间序列。多项逻辑回归表明,所需的总时间、前半圈的最大角速度、性别和使用助行器是导致跌倒风险最佳预测能力的参数。称为 i + TUG 的新测试的预测准确性达到了 74.0%,特异性为 95.9%,灵敏度为 29.2%。通过在 TUG 中添加单个可穿戴 IMU,因此获得了准确且高度特异的测试。该方法快速、易于操作且价格低廉。我们建议将其整合到疗养院的日常临床实践中。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/981c/8950330/dae80ae1d4c6/sensors-22-02339-g001.jpg

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