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利用前交叉韧带损伤生物力学和神经肌肉风险概况分析来确定神经肌肉训练的有效性。

Utilization of ACL Injury Biomechanical and Neuromuscular Risk Profile Analysis to Determine the Effectiveness of Neuromuscular Training.

作者信息

Hewett Timothy E, Ford Kevin R, Xu Yingying Y, Khoury Jane, Myer Gregory D

机构信息

Mayo Clinic, Rochester, Minnesota, USA

High Point University, High Point, North Carolina, USA.

出版信息

Am J Sports Med. 2016 Dec;44(12):3146-3151. doi: 10.1177/0363546516656373. Epub 2016 Jul 29.

DOI:10.1177/0363546516656373
PMID:27474385
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5513480/
Abstract

BACKGROUND

The widespread use of anterior cruciate ligament (ACL) injury prevention interventions has not been effective in reducing the injury incidence among female athletes who participate in high-risk sports.

PURPOSE/HYPOTHESIS: The purpose of this study was to determine if biomechanical and neuromuscular factors that contribute to the knee abduction moment (KAM), a predictor of future ACL injuries, could be used to characterize athletes by a distinct factor. Specifically, we hypothesized that a priori selected biomechanical and neuromuscular factors would characterize participants into distinct at-risk profiles.

STUDY DESIGN

Controlled laboratory study.

METHODS

A total of 624 female athletes who participated in jumping, cutting, and pivoting sports underwent testing before their competitive season. During testing, athletes performed drop-jump tasks from which biomechanical measures were captured. Using data from these tasks, latent profile analysis (LPA) was conducted to identify distinct profiles based on preintervention biomechanical and neuromuscular measures. As a validation, we examined whether the profile membership was a significant predictor of the KAM.

RESULTS

LPA using 6 preintervention biomechanical measures selected a priori resulted in 3 distinct profiles, including a low (profile 1), moderate (profile 2), and high (profile 3) risk for ACL injuries. Athletes with profiles 2 and 3 had a significantly higher KAM compared with those with profile 1 (P < .05).

CONCLUSION

This is the first study to use LPA of biomechanical landing data to create ACL injury risk profiles. Three distinct risk groups were identified based on differences in the peak KAM.

CLINICAL RELEVANCE

These findings demonstrate the existence of discernable groups of athletes that may benefit from injury prevention interventions.

STUDY REGISTRATION

ClinicalTrials.gov NCT identifier: NCT01034527.

摘要

背景

前交叉韧带(ACL)损伤预防干预措施的广泛应用未能有效降低参加高风险运动的女性运动员的损伤发生率。

目的/假设:本研究的目的是确定导致膝关节外展力矩(KAM,未来ACL损伤的预测指标)的生物力学和神经肌肉因素是否可用于通过一个独特因素对运动员进行特征描述。具体而言,我们假设预先选定的生物力学和神经肌肉因素将把参与者分为不同的高危特征组。

研究设计

对照实验室研究。

方法

共有624名参加跳跃、切入和转身运动的女性运动员在其赛季前接受了测试。在测试过程中,运动员进行了下落跳任务,从中获取生物力学测量数据。利用这些任务的数据,进行了潜在特征分析(LPA),以根据干预前的生物力学和神经肌肉测量指标确定不同的特征组。作为验证,我们检查了特征组成员身份是否是KAM的显著预测指标。

结果

使用预先选定的6项干预前生物力学测量指标进行的LPA产生了3个不同的特征组,包括ACL损伤低风险(特征组1)、中度风险(特征组2)和高风险(特征组3)。与特征组1的运动员相比,特征组2和3的运动员的KAM显著更高(P <.05)。

结论

这是第一项使用生物力学着陆数据的LPA来创建ACL损伤风险特征组的研究。根据峰值KAM的差异确定了3个不同的风险组。

临床意义

这些发现表明存在可识别的运动员群体,他们可能从损伤预防干预措施中受益。

研究注册

ClinicalTrials.gov NCT标识符:NCT01034527。

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