Gait impairment and increased gait variability are common among individuals with Parkinson’s disease (PD) and have been associated with increased risk for falls. The development of composite scores has gained interest to aggregate multiple aspects of gait into a single metric. The Enhanced Gait Variability Index (EGVI) was developed to compare an individual’s gait variability to the amount of variability in a healthy population, yet the EGVI’s individual parts may also provide important information that may be lost in this conversion. We sought to contrast individual gait measures as predictors of fall frequency and the EGVI as a single predictor of fall frequency in individuals with PD. 273 patients (189M, 84F; 68 ± 10 yrs) with idiopathic PD walked over an instrumented walkway and reported fall frequency over three months (never, rarely, monthly, weekly, or daily). The predictive ability of gait velocity, step length, step time, stance time, and single support time and the EGVI was assessed using regression techniques to predict fall frequency. The EGVI explained 15.1% of the variance in fall frequency (, r = 0.389). Although the regression using the combined spatiotemporal measures to predict fall frequency was significant (, r = 0.264), none of the components reached significance (gait velocity: , step length: , step time: , stance time: , single support time: ). The EGVI is a better predictor of fall frequency in persons with PD than its individual spatiotemporal components. Patients who fall more frequently have more variable gait, based on the interpretation of the EGVI. While the EGVI provides an objective measure of gait variability with some ability to predict fall frequency, full clinical interpretations and applications are currently unknown.

中文翻译：

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评估步态变异性指数增强与帕金森氏病患者跌倒之间的关系。

步态障碍和步态变异性增加在帕金森氏病（PD）患者中很常见，并且与跌倒风险增加有关。综合评分的发展引起了人们的兴趣，他们将步态的多个方面汇总为一个指标。增强步态变异性指数（EGVI）的开发目的是将个人的步态变异性与健康人群的变异量进行比较，但EGVI的各个部分也可能提供此转换中可能丢失的重要信息。我们试图将个体的步态测量作为跌倒频率的预测因子，将EGVI作为PD个体的下降频率的单个预测因子进行对比。273名（189M，84F； 68±10岁）特发性PD患者走过仪器通道，并报告了三个月内的跌倒频率（从不，很少，每月，每周或每天）。使用回归技术评估跌倒频率来评估步态速度，步长，步长，站立时间和单身支撑时间以及EGVI的预测能力。EGVI解释了下降频率变化的15.1％（， r  ＝ 0.389）。尽管使用时空组合测量来预测跌倒频率的回归显着（，r  = 0.264），但没有任何一个分量达到显着水平（步态速度：，步长：，步长时间：，站立时间：，单支撑时间：）。 与个体时空成分相比，EGVI可以更好地预测PD患者的跌倒频率。根据EGVI的解释，经常摔倒的患者的步态变化更大。尽管EGVI提供了一种预测步态变异性的客观方法，并具有一定的预测跌倒频率的能力，但目前尚不清楚完整的临床解释和应用。