Chehab E F, Andriacchi T P, Favre J
Department of Mechanical Engineering, Stanford University, Stanford, CA, United States; Department of Bioengineering, Stanford University, Stanford, CA, United States; Palo Alto Veterans Affairs, Palo Alto, CA, United States.
Department of Mechanical Engineering, Stanford University, Stanford, CA, United States; Palo Alto Veterans Affairs, Palo Alto, CA, United States; Department of Orthopaedic Surgery, Stanford University Medical Center, Stanford, CA, United States.
J Biomech. 2017 Jun 14;58:11-20. doi: 10.1016/j.jbiomech.2017.04.014. Epub 2017 Apr 20.
The increased use of gait analysis has raised the need for a better understanding of how walking speed and demographic variations influence asymptomatic gait. Previous analyses mainly reported relationships between subsets of gait features and demographic measures, rendering it difficult to assess whether gait features are affected by walking speed or other demographic measures. The purpose of this study was to conduct a comprehensive analysis of the kinematic and kinetic profiles during ambulation that tests for the effect of walking speed in parallel to the effects of age, sex, and body mass index. This was accomplished by recruiting a population of 121 asymptomatic subjects and analyzing characteristic 3-dimensional kinematic and kinetic features at the ankle, knee, hip, and pelvis during walking trials at slow, normal, and fast speeds. Mixed effects linear regression models were used to identify how each of 78 discrete gait features is affected by variations in walking speed, age, sex, and body mass index. As expected, nearly every feature was associated with variations in walking speed. Several features were also affected by variations in demographic measures, including age affecting sagittal-plane knee kinematics, body mass index affecting sagittal-plane pelvis and hip kinematics, body mass index affecting frontal-plane knee kinematics and kinetics, and sex affecting frontal-plane kinematics at the pelvis, hip, and knee. These results could aid in the design of future studies, as well as clarify how walking speed, age, sex, and body mass index may act as potential confounders in studies with small populations or in populations with insufficient demographic variations for thorough statistical analyses.
步态分析使用的增加，使得人们更需要深入了解步行速度和人口统计学差异如何影响无症状步态。以往的分析主要报告了步态特征子集与人口统计学指标之间的关系，因此难以评估步态特征是否受到步行速度或其他人口统计学指标的影响。本研究的目的是对步行过程中的运动学和动力学特征进行全面分析，测试步行速度的影响，并与年龄、性别和体重指数的影响进行对比。通过招募121名无症状受试者，并在慢速、正常速度和快速步行试验期间分析踝关节、膝关节、髋关节和骨盆的三维运动学和动力学特征来实现这一目的。使用混合效应线性回归模型来确定78种离散步态特征中的每一种如何受到步行速度、年龄、性别和体重指数变化的影响。正如预期的那样，几乎每个特征都与步行速度的变化相关。一些特征也受到人口统计学指标变化的影响，包括年龄影响矢状面膝关节运动学、体重指数影响矢状面骨盆和髋关节运动学、体重指数影响额状面膝关节运动学和动力学，以及性别影响骨盆、髋关节和膝关节的额状面运动学。这些结果有助于未来研究的设计，也有助于阐明步行速度、年龄、性别和体重指数在小样本研究或人口统计学差异不足以进行全面统计分析的人群研究中可能如何作为潜在的混杂因素。
