How to research training load and injury risk?

Injuries caused by overtraining are highly preventable, and there is an increasing demand for knowledge on how training load affects injury risk. However, there is little consensus on how to perform the analyses. In this project we will identify which methods are most suitable for this type of research.

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Formal title

Improving the Methodology of Training Load and Injury Research: An Analysis of Analyses

Formal

The overall aim of this PhD project is to identify and recommend suitable methods for research on the relationship between training load and injury risk

Description

The PhD project consists of four subprojects.

Subproject I: Previous research have shown that not only can high training loads increase risk of injury, but possibly also too little training. In this subproject we will explore the non-linear relationship between training load and risk of injury.

Subproject II: Researchers in the field have used varying methods for dealing with missing data values. We will consider how missing values in sports data should be handled.

Subproject III: In this subproject we will identify which statistical methods are capable of estimating the cumulative effect of long-term training load on injury risk.

Subproject IV: Athletes can sustain multiple injuries during a competitive season. We will determine how to account for so-called recurrent events in injury risk research.

Result

Subproject I
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. 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.

Bache-Mathiesen, L; Andersen, T. E.; Dalen-Lorentsen, T; Clarsen, B; Fagerland, M. W. (2021). Not straightforward: modelling non-linearity in training load and injury research. BMJ Open Sports & Exercise Medicine 7(3):e001119. DOI: 10.1136/bmjsem-2021-001119

Subproject II
Only 37 (34%) of 108 studies reported whether training load had any missing observations. Multiple Imputation using Predicted Mean Matching was the best method of handling missing data across multiple scenarios. Studies of training load and injury risk should report the extent of missing data, and how they are handled. Multiple Imputation with Predicted Mean Matching should be used when imputing sRPE and GPS variables.

Bache-Mathiesen, L; Andersen, T. E.; Clarsen, B; Fagerland, M. W. (2021). Handling and reporting missing data in training load and injury risk research. Science and Medicine in Football 1–13. DOI: 10.1080/24733938.2021.1998587