Predicting elbow load based on individual pelvis and trunk (inter)segmental rotations in fastball pitching.

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS ACS Applied Bio Materials Pub Date : 2024-02-14 DOI:10.1080/14763141.2024.2315230
Larisa Gomaz, Bart van Trigt, Frank van der Meulen, DirkJan Veeger
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Abstract

The baseball pitch is a repetitive, full-body throwing motion that exposes the elbow to significant loads, leading to a high incidence of elbow injuries. Elbow injuries in pitching are often attributed to high external valgus torques as these are generally considered to be a good proxy for the load on the Ulnar Collateral Ligament. The aim of the study is to contribute to elbow load monitoring by developing a prediction model based on the pelvis and trunk peak angular velocities and their separation time. Eleven male youth elite baseball pitchers (age 17 ± 2.2 years) threw 25 fastballs at full effort off a mound. Two-level varying-intercept, varying-slope Bayesian models were used to predict external valgus torque based on (inter)segmental rotation in fastball pitching with pitcher's weight and height added to strengthen the individualisation of the prediction. The results revealed the high predictive performance of the models including a set of kinematic parameters trunk peak angular velocity and the separation time between the pelvis and trunk peak angular velocities. Such an approach allows individualised prediction of the external valgus torque for each pitcher, which has a great practical advantage compared to group-based predictions in terms of injury assessment and injury prevention.

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根据快球投球时个人骨盆和躯干(节间)旋转情况预测肘部负荷。
棒球投球是一种重复性的全身投掷动作,会使肘部承受巨大的负荷,从而导致肘部受伤的高发率。投球运动中的肘部损伤通常归因于外翻力矩过大,因为外翻力矩通常被认为可以很好地代表尺侧副韧带所承受的负荷。本研究的目的是根据骨盆和躯干的峰值角速度及其分离时间建立一个预测模型,为肘部负荷监测做出贡献。11 名男性青年精英棒球投手(年龄为 17 ± 2.2 岁)在投手丘上全力投出 25 个快速球。采用两级变截距、变斜率贝叶斯模型,根据快球投球时的(节间)旋转来预测外翻力矩,并加入投手的体重和身高,以加强预测的个性化。结果表明,包括一组运动学参数躯干峰值角速度和骨盆与躯干峰值角速度之间的分离时间在内的模型具有很高的预测性能。这种方法可以对每个投手的外翻力矩进行个性化预测,与基于群体的预测相比,在损伤评估和损伤预防方面具有很大的实用优势。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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