School of Electronics and Computer Science, University of Southampton, UK.
J Sports Sci. 2012;30(4):403-9. doi: 10.1080/02640414.2011.647047. Epub 2012 Jan 17.
The aim of this study was to investigate the effect of intra-limb variability on the calculation of asymmetry with the purpose of informing future analyses. Asymmetry has previously been quantified for discrete kinematic and kinetic variables; however, intra-limb variability has not been routinely included in these analyses. Synchronized lower-limb kinematic and kinetic data were collected from eight trained athletes (age 22 ± 5 years, mass 74.0 ± 8.7 kg, stature 1.79 ± 0.07 m) during maximal velocity sprint running. Asymmetry was quantified using a modified version of the symmetry angle for selected kinematic and kinetic variables. Significant differences (P < 0.05) between left and right values for each variable were calculated to indicate intra-limb variability relative to between-limb differences. Significant asymmetry was present in only 39% of kinematic variables and 23% of kinetic variables analysed. Large kinetic asymmetry values (>90%) were calculated for some athletes that were not significant, due to large intra-limb variability. Variables that displayed significant asymmetry were athlete-specific. Findings highlight the potential for misleading results if intra-limb variability is not included in asymmetry analyses. The exclusion of asymmetry scores for variables not displaying significant asymmetry will be useful when calculating overall asymmetry for different participants and could be applied to future running gait analyses.
本研究旨在探讨肢体内变异性对不对称性计算的影响,以期为未来的分析提供信息。不对称性先前已被用于离散运动学和动力学变量的量化;然而,肢体内变异性在这些分析中尚未被常规纳入。本研究从 8 名训练有素的运动员(年龄 22 ± 5 岁,体重 74.0 ± 8.7kg,身高 1.79 ± 0.07m)收集了同步的下肢运动学和动力学数据,这些运动员在最大速度冲刺跑中进行了运动。使用选定运动学和动力学变量的对称角的修改版本来量化不对称性。为了表示相对于肢体间差异的肢体内变异性,计算了每个变量的左右值之间的显著差异(P < 0.05)。在所分析的运动学变量中,只有 39%,动力学变量中只有 23%存在显著不对称性。由于肢体内变异性较大,一些运动员的动力学不对称性值较大(>90%),但并不显著。显示出显著不对称性的变量是运动员特异性的。研究结果强调,如果不对称性分析中不包括肢体内变异性,可能会导致结果产生误导。对于未显示出显著不对称性的变量,排除不对称性评分将有助于计算不同参与者的整体不对称性,并可应用于未来的跑步步态分析。