Centre National de Rééducation Fonctionnelle et de Réadaptation - Rehazenter, Laboratoire d'Analyse du Mouvement et de la Posture (LAMP), Luxembourg, Luxembourg.
College of Health and Society, The University of Salford, Salford, UK.
Sci Rep. 2019 Jul 2;9(1):9510. doi: 10.1038/s41598-019-45397-4.
Clinical gait analysis attempts to provide, in a pathological context, an objective record that quantifies the magnitude of deviations from normal gait. However, the identification of deviations is highly dependent with the characteristics of the normative database used. In particular, a mismatch between patient characteristics and an asymptomatic population database in terms of walking speed, demographic and anthropometric parameters may lead to misinterpretation during the clinical process. Rather than developing a new normative data repository that may require considerable of resources and time, this study aims to assess a method for predicting lower limb sagittal kinematics using multiple regression models based on walking speed, gender, age and BMI as predictors. With this approach, we were able to predict kinematics with an error within 1 standard deviation of the mean of the original waveforms recorded on fifty-four participants. Furthermore, the proposed approach allowed us to estimate the relative contribution to angular variations of each predictor, independently from the others. It appeared that a mismatch in walking speed, but also age, sex and BMI may lead to errors higher than 5° on lower limb sagittal kinematics and should thus be taken into account before any clinical interpretation.
临床步态分析试图在病理环境下提供客观记录，量化与正常步态的偏差程度。然而，偏差的识别高度依赖于所使用的正常数据库的特征。特别是，在步行速度、人口统计学和人体测量学参数方面，患者特征与无症状人群数据库之间的不匹配可能会导致临床过程中的误解。本研究旨在评估一种使用基于步行速度、性别、年龄和 BMI 作为预测因子的多元回归模型来预测下肢矢状面运动学的方法，而不是开发可能需要大量资源和时间的新的正常数据库。通过这种方法，我们能够以原始记录的运动学数据的平均值的 1 个标准差范围内的误差来预测运动学。此外，所提出的方法允许我们独立于其他预测因子来估计每个预测因子对角度变化的相对贡献。似乎步行速度的不匹配，以及年龄、性别和 BMI 的不匹配，可能会导致下肢矢状面运动学上的误差超过 5°，因此在进行任何临床解释之前都应该考虑到这些因素。
