Oslo Sports Trauma Research Center

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Information about project titled 'Exploring the relationship between workload and risk of injury'

Exploring the relationship between workload and risk of injury

Details about the project - category Details about the project - value
Project status: Ongoing
Project manager: Lena Kristin Bache-Mathiesen
Supervisor(s): Morten Fagerland, Thor Einar Andersen, Ben Clarsen, Jon Michael Gran
Coworker(s): Torstein Dalen-Lorentsen

Description

Background: Injuries are a considerable economic cost for the professional athlete and for sports institutions. According to the annual Football Injury Index English Premier League Review (available here), Premier League spent 221 million GBP on player injuries during the 2018/2019 season. Injuries caused by overtraining are considered highly preventable, and therefore, there is an increasing demand for knowledge on how training load affects injury risk. However, there is little consensus in the field on how to measure training load—both how it is defined, and how it should be modified before analysis.

While overtraining is associated with increased injury risk from fatigue, too little training is also associated with increased risk, as it reduces fitness. This suggests a non-linear relationship between training load and injury risk. Different methods for modeling training load as a continuous variable have so-far not been compared for performance in training load-injury research.

Aim: The main goal for this project is to determine how training load should be modeled when assessing injury risk.

Methods: In this project, we will attempt to acquire access to sports data from several sources. As of 2019-12-30, we have confirmed the access and permission to use the same soccer-data purposely obtained for use in Torstein Dalen-Lorensten’s project “Methodological differences in analysing the relationship between the Acute:Chronic Workload Ratio and injury” (described here). First, we will determine which quantifications of training load in the sports data should be used. Second, we will simulate data based on the real-world data and ascertain the shape of the relationship between training load and injury in our study populations, using the measures of training load determined beforehand. Then, we will compare different methods for handling training load as a non-linear risk factor to injury with commonly used modeling methods in the field, in addition to less represented, previously proposed suitable models.