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评估长期训练负荷对团队运动中受伤风险的累积影响。

Assessing the cumulative effect of long-term training load on the risk of injury in team sports.

作者信息

Bache-Mathiesen Lena Kristin, Andersen Thor Einar, Dalen-Lorentsen Torstein, Clarsen Benjamin, Fagerland Morten Wang

机构信息

Department of Sports Medicine, Norwegian School of Sports Sciences, Oslo, Norway.

Department of Sports Medicine, Oslo Sports Trauma Research Centre, Norwegian School of Sports Sciences, Oslo, Norway.

出版信息

BMJ Open Sport Exerc Med. 2022 May 30;8(2):e001342. doi: 10.1136/bmjsem-2022-001342. eCollection 2022.

DOI:10.1136/bmjsem-2022-001342
PMID:35722043
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9152939/
Abstract

OBJECTIVES

Determine how to assess the cumulative effect of training load on the risk of injury or health problems in team sports.

METHODS

First, we performed a simulation based on a Norwegian Premier League male football dataset (n players=36). Training load was sampled from daily session rating of perceived exertion (sRPE). Different scenarios of the effect of sRPE on injury risk and the effect of relative sRPE on injury risk were simulated. These scenarios assumed that the probability of injury was the result of training load exposures over the previous 4 weeks. We compared seven different methods of modelling training load in their ability to model the simulated relationship. We then used the most accurate method, the distributed lag non-linear model (DLNM), to analyse data from Norwegian youth elite handball players (no. of players=205, no. of health problems=471) to illustrate how assessing the cumulative effect of training load can be done in practice.

RESULTS

DLNM was the only method that accurately modelled the simulated relationships between training load and injury risk. In the handball example, DLNM could show the cumulative effect of training load and how much training load affected health problem risk depending on the distance in time since the training load exposure.

CONCLUSION

DLNM can be used to assess the cumulative effect of training load on injury risk.

摘要

目的

确定如何评估团队运动中训练负荷对受伤风险或健康问题的累积影响。

方法

首先,我们基于挪威超级联赛男子足球数据集(n = 36名球员)进行了模拟。训练负荷取自每日主观用力程度评分(sRPE)。模拟了sRPE对受伤风险的不同影响情景以及相对sRPE对受伤风险的影响情景。这些情景假设受伤概率是过去4周训练负荷暴露的结果。我们比较了七种不同的训练负荷建模方法模拟这种关系的能力。然后,我们使用最准确的方法,即分布滞后非线性模型(DLNM),来分析挪威青少年精英手球运动员(球员数量 = 205,健康问题数量 = 471)的数据,以说明在实际中如何评估训练负荷的累积影响。

结果

DLNM是唯一能够准确模拟训练负荷与受伤风险之间模拟关系的方法。在手球示例中,DLNM可以显示训练负荷的累积影响,以及训练负荷暴露后经过的时间距离如何影响健康问题风险。

结论

DLNM可用于评估训练负荷对受伤风险的累积影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c9a/9152939/1eda25c81ed1/bmjsem-2022-001342f05.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c9a/9152939/d657e339d922/bmjsem-2022-001342f01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c9a/9152939/21258507841c/bmjsem-2022-001342f02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c9a/9152939/c72e9f76fc79/bmjsem-2022-001342f03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c9a/9152939/dffaa88e2dc1/bmjsem-2022-001342f04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c9a/9152939/1eda25c81ed1/bmjsem-2022-001342f05.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c9a/9152939/d657e339d922/bmjsem-2022-001342f01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c9a/9152939/21258507841c/bmjsem-2022-001342f02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c9a/9152939/c72e9f76fc79/bmjsem-2022-001342f03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c9a/9152939/dffaa88e2dc1/bmjsem-2022-001342f04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7c9a/9152939/1eda25c81ed1/bmjsem-2022-001342f05.jpg

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