Human Performance Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada.
Department of Human Health Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan.
J Biomech. 2021 Apr 15;119:110329. doi: 10.1016/j.jbiomech.2021.110329. Epub 2021 Feb 17.
Time continuous analyses, such as statistical parametric mapping (SPM), have been increasingly used in biomechanics research to determine differences between populations, interventions and methodologies. Currently, it is not known how sensitive time-continuous analyses are to timing variability that occur in gait data. We evaluated this sensitivity by examining the frequency of significant SPM outcomes between two walking speeds when lower limb kinematics and kinetics were segmented and aligned based on 40 repeatable gait events. These events, defined in the supplementary material, include a commonly used event like foot contact and other events that have been previously demonstrated to be repeatable. Repeatable gait events were determined from joint and segment kinematics, joint kinetics as well as ground reaction forces. We examined the frequency of statistical outcomes for a single subject with different numbers of strides analyzed and for a cohort of 10 subjects. Our findings demonstrate that gait interventions, such as changes in walking speed, can induce temporal shifts that affect time-continuous outcomes for both cohort- and subject-level analyses. As both timing and magnitude are important in gait data, researchers are encouraged to perform additional analyses to understand how both of these variables affect time-continuous analysis outcomes. Finally, we demonstrate that multiple SPM tests can be performed to determine if statistical outcomes are due to temporal shifting or differences in magnitude. It is important to understand how both timing and magnitude of biomechanical data influences time continuous analyses as these analyses inform injury prevention, device development and basic understanding of biomechanics.
时间连续分析，如统计参数映射（SPM），已越来越多地用于生物力学研究中，以确定人群、干预措施和方法之间的差异。目前尚不清楚时间连续分析对步态数据中出现的时间变化的敏感程度。我们通过检查在根据 40 个可重复的步态事件分段和对齐下肢运动学和动力学时，两种步行速度之间的 SPM 结果的出现频率来评估这种敏感性。这些事件在补充材料中定义，包括像足触地这样常用的事件，以及之前已经证明可重复的其他事件。可重复的步态事件是从关节和节段运动学、关节动力学以及地面反作用力来确定的。我们检查了单个受试者在分析不同数量的步伐时以及 10 个受试者队列时的统计结果出现频率。我们的研究结果表明，步态干预措施，如步行速度的变化，会引起时间转移，从而影响队列和个体水平分析的时间连续结果。由于时间和幅度在步态数据中都很重要，因此鼓励研究人员进行额外的分析，以了解这两个变量如何影响时间连续分析结果。最后，我们证明可以进行多次 SPM 测试，以确定统计结果是由于时间转移还是幅度差异引起的。了解生物力学数据的时间和幅度如何影响时间连续分析非常重要，因为这些分析为伤害预防、设备开发和生物力学的基本理解提供了信息。
