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Functional data analysis reveals asymmetrical crank torque during cycling performed at different exercise intensities

机译:功能数据分析显示在不同运动强度下进行的循环期间的不对称曲柄扭矩

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摘要

Pedaling asymmetry is claimed as a factor of influence on injury and performance. However, the evidence is still controversial. Most previous studies determined peak torque asymmetries, which in our understanding does not consider the pattern of movement like torque profiles. Here we demonstrate that asymmetries in pedaling torque at different exercise intensities can be better described when the torque profiles are considered using functional analysis of variance than when only the peak values are analyzed. We compared peak torques and torque curves recorded while cyclists pedaled at submaximal intensities of 60%, 80%, and 95% of the maximal power output and compared data between the preferred and non preferred legs. ANOVA showed symmetry or rather no difference in the amount of peak torque between legs, regardless of pedaling intensity. FANOVA, on the other hand, revealed significant asymmetries between legs, regardless of cycling intensity, apparently for different sections of the cycle, however, not for peak torque, either. We conclude that pedaling asymmetry cannot be quantified solely by peak torques and considering the analysis of the entire movement cycle can more accurately reflect the biomechanical movement pattern. Therefore, FANOVA data analysis could be an alternative to identify asymmetries. A novel approach as described here might be useful when combining kinetics assessment with other approaches like EMG and kinematics and help to better understand the role of pedaling asymmetries for performance and injury risks.
机译:踏板不对称被认为是影响受伤和性能的一个因素。然而,证据仍然存在争议。大多数以前的研究确定峰值扭矩不对称,这在我们的理解不考虑运动模式,如扭矩分布。在这里,我们证明,与仅分析峰值相比,当使用方差函数分析来考虑扭矩曲线时,可以更好地描述不同运动强度下踏板扭矩的不对称性。我们比较了自行车运动员以60%、80%和95%的最大功率输出的次最大强度踩踏时记录的峰值扭矩和扭矩曲线,并比较了首选和非首选腿之间的数据。方差分析显示,无论蹬踏强度如何,两条腿之间的峰值扭矩大小都是对称的,或者说没有差异。另一方面,FANOVA揭示了腿之间的显著不对称性,无论循环强度如何,显然是在循环的不同部分,然而,峰值扭矩也不是如此。我们得出结论,踏板不对称性不能仅通过峰值扭矩来量化,考虑到对整个运动周期的分析可以更准确地反映生物力学运动模式。因此,FANOVA数据分析可以作为识别不对称性的替代方法。当将动力学评估与其他方法(如肌电图和运动学)相结合时,本文所述的新方法可能有用,并有助于更好地理解踏板不对称对性能和受伤风险的作用。

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