Prediction of Injuries in CrossFit Training: A Machine Learning Perspective

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.

An Overview of Machine Learning Applications in Sports Injury Prediction

“…Notably, only one recent paper was found to use AdaBoost, a 2022 study predicting injury in CrossFit practitioners. AdaBoost was found to perform better overall than comparison algorithms with an AUC of 77.93% [ 36 ].…”

Special Issue on Ensemble Learning and/or Explainability

“…The fifth paper is entitled "Prediction of Injuries in CrossFit Training: A Machine Learning Perspective". It was authored by Moustakidis et al [8]. The main scope of this paper was the identification of risk factors, as well as the development of ML models using ensemblelearning techniques to predict CrossFit injuries.…”

Epidemiological Profile among Greek CrossFit Practitioners

“…As far as the risk factors are concerned, in the majority of the studies (as in this study), typical statistical techniques were used to analyze the data, including multiple regression, Pearson correlation coefficients, and general linear models with partial correlation coefficients [55]. Regression analysis does not automatically "learn" from complicated data relationships because it is static and not predictive, especially when more data inputs are introduced [55].…”

“…As far as the risk factors are concerned, in the majority of the studies (as in this study), typical statistical techniques were used to analyze the data, including multiple regression, Pearson correlation coefficients, and general linear models with partial correlation coefficients [55]. Regression analysis does not automatically "learn" from complicated data relationships because it is static and not predictive, especially when more data inputs are introduced [55]. Nevertheless, using this data set, Moustakidis et al [55] made the first foray into the development of models capable of predicting CF injuries using cutting-edge machine learning (ML) algorithms, taking into account the massive proliferation of data in sports and addressing the growing needs for reducing the health, performance, and financial consequences of injuries in athletes.…”

“…Regression analysis does not automatically "learn" from complicated data relationships because it is static and not predictive, especially when more data inputs are introduced [55]. Nevertheless, using this data set, Moustakidis et al [55] made the first foray into the development of models capable of predicting CF injuries using cutting-edge machine learning (ML) algorithms, taking into account the massive proliferation of data in sports and addressing the growing needs for reducing the health, performance, and financial consequences of injuries in athletes. ML refers to the process by which a computer system utilizes data to train itself to make better decisions with significant potential for use in sports medicine research.…”