Federal University of ABC, Neuroscience and Biomedical Engineering programs, São Bernardo do Campo, SP, Brazil.
Federal University of ABC, Neuroscience and Biomedical Engineering programs, São Bernardo do Campo, SP, Brazil.
Gait Posture. 2019 Mar;69:40-45. doi: 10.1016/j.gaitpost.2019.01.018. Epub 2019 Jan 14.
The Gait Profile Score (GPS) measures the quality of an individual's walking by calculating the difference between the kinematic pattern and the average walking pattern of healthy individuals.
The purposes of this study were to quantify the effect of speed on the GPS and to determine whether the prediction of gait patterns at a specific speed would make the GPS outcome insensitive to gait speed in the evaluation of post-stroke individuals.
The GPS was calculated for able-bodied individuals walking at different speeds and for the comparison of post-stroke individuals with able-bodied individuals using the original experimental data (standard GPS) and the predicted gait patterns at a given speed (GPS velocity, GPS). We employed standard gait analysis for data collection of the subjects. Sixteen participants with a stroke history were recruited for the post-stroke group, and 15 age-matched, able-bodied participants formed the control group.
Gait speed significantly affects the GPS and the method to predict the gait patterns at any speed is able to mitigate the effects of gait speed on the GPS. Overall, the gap between the GPS and GPS values across the post-stroke individuals was small (0.5° on average, range from 0.0° to 1.4°) and not statistically significant. However, there was a significant negative linear relationship in the absolute difference between the GPS and GPS values for the participants of the post-stroke group with gait speed, indicating that a larger difference between the speeds of the post-stroke participant and the reference dataset resulted in a larger difference between the GPS and GPS.
The modified version of the GPS, the GPS, is effective in reducing the impact of gait speed on GPS; however, the observed difference between the two methods was only around 1° for the slowest individuals in comparison to the reference dataset.
步态廓线评分（GPS）通过计算个体运动学模式与健康个体平均行走模式之间的差异来衡量个体行走的质量。
本研究旨在量化速度对 GPS 的影响，并确定在特定速度下预测步态模式是否会使 GPS 结果在评估中风后个体时对步态速度不敏感。
使用原始实验数据（标准 GPS）和给定速度下预测的步态模式（GPS 速度，GPS），计算不同速度下的健康个体和中风后个体的 GPS。我们采用标准步态分析进行受试者数据采集。招募了 16 名有中风病史的患者作为中风后组，15 名年龄匹配的健康个体作为对照组。
步态速度对 GPS 有显著影响，预测任何速度下的步态模式的方法能够减轻步态速度对 GPS 的影响。总体而言，中风后个体的 GPS 和 GPS 值之间的差距较小（平均为 0.5°，范围为 0.0°至 1.4°），且无统计学意义。然而，中风后组参与者的 GPS 和 GPS 值之间的绝对差值与步态速度之间存在显著的负线性关系，表明中风后参与者的速度与参考数据集之间的差异越大，GPS 和 GPS 值之间的差异就越大。
修改后的 GPS（GPS）有效降低了步态速度对 GPS 的影响；然而，与参考数据集相比，两种方法之间的差异仅在最慢的个体中约为 1°。
