Schmitt Abigail C, Baudendistel Sidney T, Fallon Michaela S, Roper Jaimie A, Hass Chris J
Applied Neuromechanics Laboratory, Department of Applied Physiology and Kinesiology, University of Florida, 1864 Stadium Road, Gainesville, FL 32611, USA.
Locomotor and Movement Control Laboratory, School of Kinesiology, Auburn University, 301 Wire Road, Auburn, AL 36849, USA.
Parkinsons Dis. 2020 Feb 7;2020:5813049. doi: 10.1155/2020/5813049. eCollection 2020.
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 ( < 0.001, = 0.389). Although the regression using the combined spatiotemporal measures to predict fall frequency was significant (=0.002, = 0.264), none of the components reached significance (gait velocity: =0.640, step length: =0.900, step time: =0.525, stance time: =0.532, single support time: =0.480). 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.
步态障碍和步态变异性增加在帕金森病(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,即将步态的多个方面汇总为一个单一指标。增强步态变异性指数(EGVI)的开发是为了将个体的步态变异性与健康人群的变异性进行比较,然而EGVI的各个部分可能也提供了在这种转换中可能会丢失的重要信息。我们试图对比个体步态测量指标作为跌倒频率的预测因素,以及EGVI作为PD患者跌倒频率的单一预测因素。273例特发性PD患者(189例男性,84例女性;年龄68±10岁)在装有仪器的通道上行走,并报告三个月内的跌倒频率(从不、很少、每月、每周或每天)。使用回归技术评估步态速度、步长、步时、站立时间、单支撑时间和EGVI预测跌倒频率的能力。EGVI解释了跌倒频率方差的15.1%(P<0.001,R²=0.389)。尽管使用时空综合测量指标预测跌倒频率的回归分析具有显著性(P=0.002,R²=0.264),但没有一个组成部分具有显著性(步态速度:P=0.640,步长:P=0.900,步时:P=0.525,站立时间:P=0.532,单支撑时间:P=0.480)。对于PD患者,EGVI比其个体时空组成部分更能预测跌倒频率。根据EGVI的解释,跌倒更频繁的患者步态变异性更大。虽然EGVI提供了一种客观的步态变异性测量方法,具有一定的预测跌倒频率的能力,但目前其完整的临床解释和应用尚不清楚。