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长时间跑步中的跑步损伤和步时变异性。

Running injury and stride time variability over a prolonged run.

机构信息

Department of Health Professions, Physical Therapy Program, University of La Crosse-Wisconsin, 1725 State Street, 4054 Health Sciences Center, La Crosse, WI 54601, USA.

出版信息

Gait Posture. 2011 Jan;33(1):36-40. doi: 10.1016/j.gaitpost.2010.09.020. Epub 2010 Oct 29.

Abstract

Locomotor variability is inherent to movement and, in healthy systems, contains a predictable structure. In this study, detrended fluctuation analysis (DFA) was used to quantify the structure of variability in locomotion. Using DFA, long-range correlations (α) are calculated in over ground running and the influence of injury and fatigue on α is examined. An accelerometer was mounted to the tibia of 18 runners (9 with a history of injury) to quantify stride time. Participants ran at their preferred 5k pace±5% on an indoor track to fatigue. The complete time series data were divided into three consecutive intervals (beginning, middle, and end). Mean, standard deviation (SD), coefficient of variation (CV) and α of stride times were calculated for each interval. Averages for all variables were calculated per group for statistical analysis. No significant interval, group or interval×group effects were found for mean, SD or CV of stride time. A significant linear trend in α for interval occurred with a reduction in α over the course of the run (p=0.01) indicating that over the run, stride times of runners became more unpredictable. This was likely due to movement errors associated with fatigue necessitating frequent corrections. The injured group exhibited lower α (M=0.79, CI(95)=0.70, 0.88) than the non-injured group (p=0.01) (M=0.96, CI(95)=0.88, 1.05); a reduction hypothesized to be associated with altered complexity. Overall, these findings suggest injury and fatigue influence neuromuscular output during running.

摘要

运动的变异性是固有存在的,在健康的系统中,它包含可预测的结构。在这项研究中,去趋势波动分析(DFA)被用于量化运动变异性的结构。使用 DFA,在地面跑步中计算出长程相关性(α),并检查损伤和疲劳对α的影响。将加速度计安装在 18 名跑步者(9 名有受伤史)的胫骨上,以量化步时。参与者在室内跑道上以他们喜欢的 5k 速度±5%跑步至疲劳。完整的时间序列数据被分为三个连续的间隔(开始、中间和结束)。计算每个间隔的步时的平均值、标准差(SD)、变异系数(CV)和α。为了进行统计分析,按组计算所有变量的平均值。对于步时的平均值、SD 或 CV,没有发现间隔、组或间隔×组的显著影响。α的显著线性趋势随着跑步过程中α的降低而发生(p=0.01),这表明随着跑步的进行,跑步者的步时变得更加不可预测。这可能是由于与疲劳相关的运动错误需要频繁纠正。受伤组的α值较低(M=0.79,CI(95)=0.70,0.88),而非受伤组的α值较高(M=0.96,CI(95)=0.88,1.05)(p=0.01);这种减少被假设与复杂程度的改变有关。总的来说,这些发现表明损伤和疲劳会影响跑步时的神经肌肉输出。

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