Lower limb sagittal gait kinematics can be predicted based on walking speed, gender, age and BMI
Lower limb sagittal gait kinematics can be predicted based on walking speed, gender, age and BMI
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DOI：
10.1038/s41598-019-45397-4
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发表时间：
2019-07-02
影响因子：
4.6
通讯作者：
Armand, Stephane
中科院分区：
文献类型：
--
作者：
Moissenet, Florent;Leboeuf, Fabien;Armand, Stephane
临床步态分析试图在病理背景下提供客观记录，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规范数据库的特征。特别是，患者特征与无症状人群数据库在步行速度、人口统计和人体测量参数方面的不匹配可能会导致临床过程中的误解。本研究的目的不是开发可能需要大量资源和时间的新规范数据存储库，而是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运动学的方法。通过这种方法，我们能够预测运动学，其误差在 54 名参与者记录的原始波形平均值的 1 个标准差以内。此外，所提出的方法使我们能够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。步行速度、年龄、性别和体重指数的不匹配可能会导致下肢矢状运动学误差高于 5 度，因此在任何临床解释之前都应该考虑到这一点。
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 degrees on lower limb sagittal kinematics and should thus be taken into account before any clinical interpretation.