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不同步频条件下跑步机跑步时骨盆加速度的预测因素。

Predictors of pelvic acceleration during treadmill running across various stride frequency conditions.

作者信息

James Megan L, Stiles Victoria H, von Lieres Und Wilkau Hans C, Jones Alex L, Willy Richard W, Ashford Kelly J, Moore Isabel S

机构信息

School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, UK.

Department of Public Health and Sport Sciences, University of Exeter, Exeter, UK.

出版信息

Sports Biomech. 2025 Jan 20:1-15. doi: 10.1080/14763141.2024.2446179.

Abstract

Pelvic running injuries often require extensive rehabilitation and pelvic girdle pain is a barrier to running engagement in population sub-groups, such as perinatal women. However, exploration into how external pelvic loading may be altered during running is limited. This study assessed which biomechanical variables influence changes in external peak pelvic acceleration during treadmill running, across various stride frequency conditions. Twelve participants (7 female, 5 male) ran (9 km∙h) at their preferred stride frequency, and at ± 5% and ± 10% of their preferred stride frequency. Coordinate and acceleration data were collected using a motion capture system and inertial measurement units. Linear mixed models assessed peak tibial acceleration, displacement from hip to knee and ankle, contact time, and stride frequency as predictors of peak pelvic acceleration. Stride frequency and contact time interacted to predict peak vertical ( = .006) and resultant ( = .009) pelvic acceleration. When modelled, short contact times and low stride frequencies produced higher peak vertical ( = .007) and resultant ( = .016) pelvic accelerations than short contact times and average, or high stride frequencies. Increasing contact time, or increasing stride frequency at shorter contact times, may therefore be useful in reducing pelvic acceleration during treadmill running.

摘要

骨盆部跑步损伤通常需要长期康复治疗,而骨盆带疼痛是部分人群(如围产期女性)参与跑步运动的障碍。然而,关于跑步过程中外在骨盆负荷如何变化的研究却很有限。本研究评估了在不同步频条件下,跑步机跑步过程中哪些生物力学变量会影响外在骨盆峰值加速度的变化。12名参与者(7名女性,5名男性)以其偏好的步频跑步(9公里/小时),同时以其偏好步频的±5%和±10%进行跑步。使用动作捕捉系统和惯性测量单元收集坐标和加速度数据。线性混合模型评估了胫骨峰值加速度、从髋部到膝盖和脚踝的位移、接触时间以及步频作为骨盆峰值加速度的预测指标。步频和接触时间相互作用,可预测骨盆垂直峰值加速度(p = 0.006)和合成峰值加速度(p = 0.009)。建模结果显示,与短接触时间和平均步频或高步频相比,短接触时间和低步频会产生更高的骨盆垂直峰值加速度(p = 0.007)和合成峰值加速度(p = 0.016)。因此,增加接触时间,或在短接触时间内增加步频,可能有助于降低跑步机跑步过程中的骨盆加速度。

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