哪些筛查工具可预测团队运动中下肢损伤?系统评价。

Which screening tools can predict injury to the lower extremities in team sports?: a systematic review.

机构信息

Center for Human Movement Sciences, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.

出版信息

Sports Med. 2012 Sep 1;42(9):791-815. doi: 10.1007/BF03262295.

Abstract

BACKGROUND

Injuries to lower extremities are common in team sports such as soccer, basketball, volleyball, football and field hockey. Considering personal grief, disabling consequences and high costs caused by injuries to lower extremities, the importance for the prevention of these injuries is evident. From this point of view it is important to know which screening tools can identify athletes who are at risk of injury to their lower extremities.

OBJECTIVE

The aim of this article is to determine the predictive values of anthropometric and/or physical screening tests for injuries to the leg, anterior cruciate ligament (ACL), knee, hamstring, groin and ankle in team sports.

METHODS

A systematic review was conducted in MEDLINE (1966 to September 2011), EMBASE (1989 to September 2011) and CINAHL (1982 to September 2011). Based on inclusion criteria defined a priori, titles, abstracts and full texts were analysed to find relevant studies.

RESULTS

The analysis showed that different screening tools can be predictive for injuries to the knee, ACL, hamstring, groin and ankle. For injuries in general there is some support in the literature to suggest that general joint laxity is a predictive measure for leg injuries. The anterior right/left reach distance >4 cm and the composite reach distance <4.0% of limb length in girls measured with the star excursion balance test (SEBT) may predict leg injuries. Furthermore, an increasing age, a lower hamstring/quadriceps (H : Q) ratio and a decreased range of motion (ROM) of hip abduction may predict the occurrence of leg injuries. Hyperextension of the knee, side-to-side differences in anterior-posterior knee laxity and differences in knee abduction moment between both legs are suggested to be predictive tests for sustaining an ACL injury and height was a predictive screening tool for knee ligament injuries. There is some evidence that when age increases, the probability of sustaining a hamstring injury increases. Debate exists in the analysed literature regarding measurement of the flexibility of the hamstring as a predictive screening tool, as well as using the H : Q ratio. Hip-adduction-to-abduction strength is a predictive test for hip adductor muscle strain. Studies do not agree on whether ROM of the hamstring is a predictive screening tool for groin injury. Body mass index and the age of an athlete could contribute to an ankle sprain. There is support in the literature to suggest that greater strength of the plantar flexors may be a predictive measure for sustaining an ankle injury. Furthermore, there is some agreement that the measurement of postural sway is a predictive test for an ankle injury.

CONCLUSIONS

The screening tools mentioned above can be recommended to medical staff and coaches for screening their athletes. Future research should focus on prospective studies in larger groups and should follow athletes over several seasons.

摘要

背景

下肢损伤在足球、篮球、排球、足球和曲棍球等团体运动中较为常见。考虑到下肢损伤给个人带来的悲痛、致残后果和高昂的费用,预防这些损伤的重要性不言而喻。从这个角度来看,了解哪些筛查工具可以识别出下肢受伤风险较高的运动员非常重要。

目的

本文旨在确定人体测量学和/或物理筛查测试对团队运动中腿部、前交叉韧带(ACL)、膝盖、腿筋、腹股沟和脚踝损伤的预测值。

方法

在 MEDLINE(1966 年至 2011 年 9 月)、EMBASE(1989 年至 2011 年 9 月)和 CINAHL(1982 年至 2011 年 9 月)中进行了系统评价。根据事先定义的纳入标准,对标题、摘要和全文进行分析,以找到相关研究。

结果

分析表明,不同的筛查工具可以预测膝盖、ACL、腿筋、腹股沟和脚踝的损伤。一般来说,有一些文献支持关节过度活动度是腿部受伤的预测指标。女孩的星形偏移平衡测试(SEBT)的前右/左伸展距离>4 厘米和综合伸展距离<4.0%的肢体长度可能预示着腿部受伤。此外,年龄增加、腿筋/四头肌(H:Q)比值降低和髋关节外展活动范围减小可能预示着腿部受伤。膝关节过度伸展、前后侧膝关节松弛的侧-侧差异以及双腿之间的膝关节外展力矩差异被认为是 ACL 损伤的预测性测试,而身高是膝关节韧带损伤的预测性筛查工具。有证据表明,随着年龄的增长,发生腿筋损伤的概率增加。在分析文献中,关于作为预测性筛查工具测量腿筋柔韧性以及使用 H:Q 比值存在争议。髋关节内收-外展力量是髋关节内收肌拉伤的预测性测试。研究结果并不一致,即腿筋柔韧性是否是腹股沟损伤的预测性筛查工具。身体质量指数和运动员的年龄可能导致踝关节扭伤。有文献支持更大的足底屈肌力量可能是预测踝关节受伤的一个指标。此外,一些研究认为姿势摆动的测量是踝关节受伤的预测性测试。

结论

上述筛查工具可推荐给医务人员和教练,用于对运动员进行筛查。未来的研究应侧重于更大样本量的前瞻性研究,并应在多个赛季中跟踪运动员。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索