Chakraborty Saikat, Nandy Anup, Kesar Trisha M
Machine Intelligence and Bio-motion Research Lab., Department of Computer Science and Engineering, National Institute of Technology, Rourkela, India. Electronic address: saikat.scgmail.com.
Machine Intelligence and Bio-motion Research Lab., Department of Computer Science and Engineering, National Institute of Technology, Rourkela, India.
Clin Biomech (Bristol). 2020 Jan;71:11-23. doi: 10.1016/j.clinbiomech.2019.09.005. Epub 2019 Oct 18.
Studies have demonstrated that ambulatory children and adolescents with cerebral palsy demonstrate atypical gait patterns. Out of numerous gait variables, identification of the most deteriorated gait parameters is important for targeted and effective gait rehabilitation. Therefore, this study aimed to identify the gait parameters with the most discriminating nature to distinguish cerebral palsy gait from normal gait.
Multiple databases were searched to include studies on ambulatory children and adolescents with cerebral palsy that included gait (spatio-temporal, kinematic, and kinetic) and dynamic stability variables.
Of 68 studies that met the inclusion criteria, 35 studies were included in the meta analysis. Effect size was used to assess the discriminative strength of each variable. A large effect (≥ 0.8) of cerebral palsy on double limb support time (Standardized Mean Difference = 0.98), step length (Standardized Mean Difference = 1.65), step width (Standardized Mean Difference = 1.21), stride length (Standardized Mean Difference = 1.75), and velocity (Standardized Mean Difference = 1.42) was observed at preferred-walking speed. At fast-walking speed, some gait variables (i.e. velocity and stride length) exhibited larger effect size compared to preferred-walking speed. For some kinematic variables (e.g. range of motion of pelvis), the effect size varied across the body planes.
Our systematic review detects the most discriminative features of cerebral palsy gait. Non-uniform effects on joint kinematics across the anatomical planes support the importance of 3D gait analysis. Differential effects at fast versus preferred speeds emphasize the importance of measuring gait at a range of speeds.
研究表明,患有脑瘫的儿童和青少年在行走时表现出非典型的步态模式。在众多步态变量中,识别出最恶化的步态参数对于有针对性且有效的步态康复至关重要。因此,本研究旨在识别最具区分性的步态参数,以区分脑瘫步态和正常步态。
检索了多个数据库,纳入了关于患有脑瘫的儿童和青少年的研究,这些研究包括步态(时空、运动学和动力学)以及动态稳定性变量。
在符合纳入标准的68项研究中,35项研究被纳入荟萃分析。效应量用于评估每个变量的区分强度。在首选步行速度下,观察到脑瘫对双支撑时间(标准化均值差 = 0.98)、步长(标准化均值差 = 1.65)、步宽(标准化均值差 = 1.21)、步幅(标准化均值差 = 1.75)和速度(标准化均值差 = 1.42)有较大影响。在快走速度下,一些步态变量(即速度和步幅)与首选步行速度相比表现出更大的效应量。对于一些运动学变量(例如骨盆运动范围),效应量在身体各平面有所不同。
我们的系统评价检测到了脑瘫步态最具区分性的特征。跨解剖平面关节运动学的非均匀效应支持了三维步态分析的重要性。快走速度与首选速度下的差异效应强调了在一系列速度下测量步态的重要性。