Information about project titled 'Not straightforward: modelling non-linearity in training load and injury research'
Not straightforward: modelling non-linearity in training load and injury research
|Details about the project - category||Details about the project - value|
|Project manager:||Lena Kristin Bache-Mathiesen|
|Supervisor(s):||Morten Wang Fagerland, Thor Einar Andersen|
|Coworker(s):||Torstein Dalen-Lorentsen, Ben Clarsen|
Background: Sports 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 may also be 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 to handle non-linearity have so-far not been compared for performance in training load-injury research.
Aim: To determine whether the relationship between training load and injury risk is non-linear and investigate ways of handling non-linearity.
Methods: We analysed daily training load and injury data from three cohorts: Norwegian elite U-19 football (n = 81, 55% male, mean age 17 years [SD 1]), Norwegian Premier League football (n = 36, 100% male, mean age 26 years [SD 4]), and elite youth handball (n = 205, 36% male, mean age 17 years [SD 1]). The relationship between training load measured as the session Rating of Perceived Exertion (sRPE) and probability of injury was estimated with restricted cubic splines in mixed-effects logistic regression models. Simulations were carried out to compare the ability of seven methods to model non-linear relationships, using visualizations, root-mean-squared error, and coverage of prediction intervals as performance metrics.
Results: No relationships were identified in the football cohorts; however, a J-shaped relationship was found between sRPE and the probability of injury on the same day for elite youth handball players (p < 0.001). In the simulations, the only methods capable of non-linear modelling relationships were the quadratic model, fractional polynomials, and restricted cubic splines.
Conclusion: The relationship between training load and injury risk should be assumed to be non-linear. Future research should apply appropriate methods to account for non-linearity, such as fractional polynomials or restricted cubic splines. We propose a guide for which method(s) to use in a range of different situations.