Centre for Sport Research, School of Exercise and Nutrition Sciences, Deakin University, Geelong, Victoria, Australia.
University of Canberra Research Institute of Sport & Exercise (UCRISE), University of Canberra, Canberra, Australia.
PeerJ. 2023 Mar 17;11:e14921. doi: 10.7717/peerj.14921. eCollection 2023.
A common approach in the biomechanical analysis of running technique is to average data from several gait cycles to compute a 'representative mean.' However, the impact of the quantity and selection of gait cycles on biomechanical measures is not well understood. We examined the effects of gait cycle selection on kinematic data by: (i) comparing representative means calculated from varying numbers of gait cycles to 'global' means from the entire capture period; and (ii) comparing representative means from varying numbers of gait cycles sampled from different parts of the capture period. We used a public dataset ( = 28) of lower limb kinematics captured during a 30-second period of treadmill running at three speeds (2.5 m s, 3.5 m s and 4.5 m s). 'Ground truth' values were determined by averaging data across all collected strides and compared to representative means calculated from random samples (1,000 samples) of (range = 5-30) consecutive gait cycles. We also compared representative means calculated from n (range = 5-15) consecutive gait cycles randomly sampled (1,000 samples) from within the same data capture period. The mean, variance and range of the absolute error of the representative mean compared to the 'ground truth' mean progressively reduced across all speeds as the number of gait cycles used increased. Similar magnitudes of 'error' were observed between the 2.5 m s and 3.5 m s speeds at comparable gait cycle numbers -where the maximum errors were < 1.5 degrees even with a small number of gait cycles (, 5-10). At the 4.5 m s speed, maximum errors typically exceeded 2-4 degrees when a lower number of gait cycles were used. Subsequently, a higher number of gait cycles (i.e., 25-30) was required to achieve low errors (i.e., 1-2 degrees) at the 4.5 m s speed. The mean, variance and range of absolute error of representative means calculated from different parts of the capture period was consistent irrespective of the number of gait cycles used. The error between representative means was low (i.e., < 1.5 degrees) and consistent across the different number of gait cycles at the 2.5 m s and 3.5 m s speeds, and consistent but larger (, up to 2-4 degrees) at the 4.5 m s speed. Our findings suggest that selecting as many gait cycles as possible from a treadmill running bout will minimise potential 'error.' Analysing a small sample (i.e., 5-10 cycles) will typically result in minimal 'error' (i.e., < 2 degrees), particularly at lower speeds (i.e., 2.5 m s and 3.5 m s). Researchers and clinicians should consider the balance between practicalities of collecting and analysing a smaller number of gait cycles against the potential 'error' when determining their methodological approach. Irrespective of the number of gait cycles used, we recommend that the potential 'error' introduced by the choice of gait cycle number be considered when interpreting the magnitude of effects in treadmill-based running studies.
在跑步技术的生物力学分析中,一种常见的方法是平均几个步态周期的数据,以计算“代表性均值”。然而,步态周期数量和选择对生物力学测量的影响尚不清楚。我们通过以下两种方法来检查步态周期选择对运动学数据的影响:(i)将来自不同数量步态周期的代表性均值与整个采集期间的“全局”均值进行比较;(ii)将来自不同采集期不同部分的不同数量步态周期的代表性均值进行比较。我们使用了一个公共数据集(n = 28),该数据集包括在跑步机上以 3 种速度(2.5 m/s、3.5 m/s 和 4.5 m/s)跑步 30 秒期间下肢的运动学数据。“真实值”通过对所有收集的步进行平均确定,并与从随机样本(1000 个样本)中计算出的代表性均值(范围为 5-30)进行比较。我们还比较了从相同数据采集期内随机抽取的 n(范围为 5-15)个连续步态周期中计算出的代表性均值(1000 个样本)。随着使用的步态周期数量的增加,代表均值与“真实值”均值的绝对误差的平均值、方差和范围逐渐减小,所有速度均如此。在可比的步态周期数量下,2.5 m/s 和 3.5 m/s 速度之间观察到相似幅度的“误差” - 即使使用少量步态周期(5-10),最大误差也小于 1.5 度。在 4.5 m/s 速度下,最大误差通常超过 2-4 度,此时使用的步态周期数量较少。随后,在 4.5 m/s 速度下,需要更多的步态周期(即 25-30)才能达到低误差(即 1-2 度)。无论使用的步态周期数量如何,从采集期的不同部分计算出的代表性均值的平均值、方差和范围都是一致的。代表均值之间的误差较小(即小于 1.5 度),在 2.5 m/s 和 3.5 m/s 速度下,无论步态周期数量多少,都保持一致,但在 4.5 m/s 速度下,误差更大(即 2-4 度)。我们的研究结果表明,从跑步机跑步过程中选择尽可能多的步态周期将最大限度地减少潜在的“误差”。分析小样本(即 5-10 个周期)通常会导致最小的“误差”(即小于 2 度),尤其是在较低的速度(即 2.5 m/s 和 3.5 m/s)下。研究人员和临床医生在确定其方法学方法时,应考虑在收集和分析较少步态周期的实际情况与确定潜在“误差”之间取得平衡。无论使用的步态周期数量如何,我们建议在解释基于跑步机跑步研究中效应的幅度时,应考虑步态周期数量选择引起的潜在“误差”。