Research Group Lifestyle and Health, Utrecht University of Applied Sciences, 3584 JW Utrecht, The Netherlands.
J Rehabil Med. 2017 May 16;49(5):402-409. doi: 10.2340/16501977-2234.
This exploratory study investigated to what extent gait characteristics and clinical physical therapy assessments predict falls in chronic stroke survivors.
Prospective study.
Chronic fall-prone and non-fall-prone stroke survivors.
Steady-state gait characteristics were collected from 40 participants while walking on a treadmill with motion capture of spatio-temporal, variability, and stability measures. An accelerometer was used to collect daily-life gait characteristics during 7 days. Six physical and psychological assessments were administered. Fall events were determined using a "fall calendar" and monthly phone calls over a 6-month period. After data reduction through principal component analysis, the predictive capacity of each method was determined by logistic regression.
Thirty-eight percent of the participants were classified as fallers. Laboratory-based and daily-life gait characteristics predicted falls acceptably well, with an area under the curve of, 0.73 and 0.72, respectively, while fall predictions from clinical assessments were limited (0.64).
Independent of the type of gait assessment, qualitative gait characteristics are better fall predictors than clinical assessments. Clinicians should therefore consider gait analyses as an alternative for identifying fall-prone stroke survivors.
本探索性研究旨在调查步态特征和临床物理治疗评估在多大程度上可以预测慢性卒中幸存者的跌倒情况。
前瞻性研究。
慢性易跌倒和不易跌倒的卒中幸存者。
40 名参与者在跑步机上行走时，使用运动捕捉技术收集稳态步态特征，包括时空、变异性和稳定性测量。使用加速度计在 7 天内收集日常生活中的步态特征。进行了 6 项身体和心理评估。使用“跌倒日历”和 6 个月期间的每月电话来确定跌倒事件。通过主成分分析进行数据缩减后，通过逻辑回归确定每种方法的预测能力。
38%的参与者被归类为跌倒者。基于实验室和日常生活的步态特征可以很好地预测跌倒，曲线下面积分别为 0.73 和 0.72，而临床评估的跌倒预测则有限（0.64）。
无论步态评估的类型如何，定性步态特征都是比临床评估更好的跌倒预测指标。因此，临床医生应考虑步态分析作为识别易跌倒的卒中幸存者的替代方法。
