Information about project titled 'Assessing the cumulative effect of long-term training load on the risk of sports injury'
Assessing the cumulative effect of long-term training load on the risk of sports injury
| Details about the project - category | Details about the project - value |
|---|---|
| Project status: | Published |
| Project manager: | Lena Kristin Bache-Mathiesen |
| Supervisor(s): | Morten Wang Fagerland, Thor Einar Andersen |
| Coworker(s): | Ben Clarsen, Torstein Dalen-Lorentsen |
Description
Background: In the last decades,researchers have worked to identify risk factors for sports injuries. One such potential risk factor is training load. Modelling training load has proven challenging, however, because of its multi-dimensional nature, and likely complex relationship with injury risk. Training load is also a time-varying exposure. It is thought to have different effects depending on distance in time from the current day, and the effects have been hypothesized to be non-linear. Not only the amount of training load, but the change in training load, is considered to affect injury risk. While some methods have been recommended to handle challenges in training load and injury risk in isolation, it is unclear how to deal with these issues in symphony.
Aim: To determine how to model the cumulative effect of training load when assessing its effect on the risk of sports injury or health problems.
Methods: Based on a Norwegian Premier League male football dataset (n players=36), we simulated relationships between daily session rating of perceived exertion (sRPE) and injury risk with varying degrees of complexity. Seven modelling methods were compared in their ability to uncover the cumulative effect of training load on the probability of injury in these experimental relationships. We implemented the most accurate method, the distributed lag non-linear model (DLNM), on a dataset of Norwegian youth elite handball players (205 players, 471 health problems) to illustrate how assessing the cumulative effect of training load can be done in practice.
Results: Of the seven compared methods, only DLNM accurately modelled the simulated relationships between training load and injury risk. The Exponentially Weighted Moving Average (EWMA) had acceptable performance under the assumption that the effect of training load decayed exponentially for each day back in time. In the handball example, DLNM could show the cumulative effect of training load on health problem risk.
Conclusion: DLNM is an appropriate method for assessing the cumulative effect of training load on injury or health problem risk.