Pritchard N Stewart, Brandt Kambrie M, Peluso Alexandra G, Kruse David W, Hart Elspeth, Carr Heather P, Bullock Garrett S, Miles Christopher M, Moore Justin B, Stitzel Joel D, Urban Jillian E
Department of Biomedical Engineering, Wake Forest University School of Medicine, Winston-Salem, NC,USA.
School of Biomedical Engineering and Sciences, Virginia Tech - Wake Forest University, Winston-Salem, NC, USA.
Sports Biomech. 2025 Mar 26:1-21. doi: 10.1080/14763141.2025.2481154.
This study aimed to evaluate head kinematics experienced during skill progression pathways in Women's Artistic Gymnastics to inform post-concussion return to gymnastics protocols. A return to gymnastics framework, consisting of seven skill progression pathways, was developed. Twelve gymnasts were instrumented with mouthpiece sensors and performed two trials of each skill, if able. Sensors recorded data at 100 Hz and skill segments were extracted using time-synchronised video. Peak resultant linear (PLA) and rotational acceleration (PRA), rotational velocity change index (ΔRV) and peak resultant rotational velocity (PRV) of 1 Hz low pass filtered data were computed from skills. A mixed effects model evaluated differences in kinematic metrics across skills within pathways while adjusting for random effects of the participant. Stepwise increases in kinematic metrics occurred along backward and forward tumbling (floor) pathways but did not occur in other pathways. For instance, gymnasts experienced greater PLA and PRV during clear hip and back hip circle compared to giant. Moreover, skills performed early along respective pathways (e.g, Yurchenko timer (to back), Tsukahara timer (to back), handstand forward roll) were among the skills with the greatest PRA and ΔRV. Head kinematics associated with skill performance should be considered when developing return to gymnastics protocols.
本研究旨在评估女子竞技体操技能进阶过程中的头部运动学,为脑震荡后重返体操训练方案提供依据。制定了一个由七条技能进阶路径组成的重返体操框架。12名体操运动员佩戴了咬嘴传感器,如有能力,对每项技能进行两次测试。传感器以100Hz记录数据,并使用时间同步视频提取技能片段。从技能中计算出1Hz低通滤波数据的峰值合成线性加速度(PLA)和旋转加速度(PRA)、旋转速度变化指数(ΔRV)和峰值合成旋转速度(PRV)。一个混合效应模型在调整参与者随机效应的同时,评估了各路径内不同技能运动学指标的差异。运动学指标在向后和向前翻腾(自由体操)路径上呈逐步增加,但在其他路径上未出现。例如,与大回环相比,体操运动员在直体后空翻和团身后空翻时经历了更大的PLA和PRV。在制定重返体操训练方案时,应考虑与技能表现相关的头部运动学。