Abstract
CrossFit has gained recognition and interest among physically active populations being one of the most popular and rapidly growing exercise regimens worldwide. Due to the intense and repetitive nature of CrossFit, concerns have been raised over the potential injury risks that are associated with its training including rhabdomyolysis and musculoskeletal injuries. However, identification of risk factors for predicting injuries in CrossFit athletes has been limited by the absence of relevant big epidemiological studies. The main purpose of this paper is the identification of risk factors and the development of machine learning-based models using ensemble learning that can predict CrossFit injuries. To accomplish the aforementioned targets, a survey-based epidemiological study was conducted in Greece to collect data on musculoskeletal injuries in CrossFit practitioners. A Machine Learning (ML) pipeline was then implemented that involved data pre-processing, feature selection and well-known ML models. The performance of the proposed ML models was assessed using a comprehensive cross validation mechanism whereas a discussion on the nature of the selected features is also provided. An area under the curve (AUC) of 77.93% was achieved by the best ML model using ensemble learning (Adaboost) on the group of six selected risk factors. The effectiveness of the proposed approach was evaluated in a comparative analysis with respect to numerous performance metrics including accuracy, sensitivity, specificity, AUC and confusion matrices to confirm its clinical relevance. The results are the basis for the development of reliable tools for the prediction of injuries in CrossFit.
Highlights
An increase on the classification performance of all Machine Learning (ML) algorithms was observed for the first 6 selected features, whereas the inclusion of additional features had no significant effect on the classification accuracies
The present study showed that referring medical history to the coach is a risk factor for future injuries in CF
This paper focuses on the development of an ML-based methodology capable of identifying important risk factors which are strongly associated with CF injuries
Summary
It is widely accepted that a physically active lifestyle and sports participation are important for all age groups with positive impact [1,2,3]. Sports participation carries a risk for injuries, which may in some cases lead to permanent disability [4]. Sports injuries are very common across different sports among both elite and recreational athletes, affect health and performance and may even cause prolonged problems in a person’s life [5]. Sports injuries can lead to pain, loss of playing or working time, as well as decreased motility and stability [5]
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