Prediction of Injuries in CrossFit Training: A Machine Learning Perspective
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/15/3/77 |
| _version_ | 1797447384916033536 |
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| author | Serafeim Moustakidis Athanasios Siouras Konstantinos Vassis Ioannis Misiris Elpiniki Papageorgiou Dimitrios Tsaopoulos |
| author_facet | Serafeim Moustakidis Athanasios Siouras Konstantinos Vassis Ioannis Misiris Elpiniki Papageorgiou Dimitrios Tsaopoulos |
| author_sort | Serafeim Moustakidis |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T13:55:31Z |
| format | Article |
| id | doaj.art-e5ac52c4e4884d49985c8453c56cef5f |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-09T13:55:31Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-e5ac52c4e4884d49985c8453c56cef5f2023-11-30T20:44:11ZengMDPI AGAlgorithms1999-48932022-02-011537710.3390/a15030077Prediction of Injuries in CrossFit Training: A Machine Learning PerspectiveSerafeim Moustakidis0Athanasios Siouras1Konstantinos Vassis2Ioannis Misiris3Elpiniki Papageorgiou4Dimitrios Tsaopoulos5AIDEAS OÜ, Narva mnt 5, 10117 Tallinn, Harju Maakond, EstoniaAIDEAS OÜ, Narva mnt 5, 10117 Tallinn, Harju Maakond, EstoniaSchool of Health Sciences, University of Thessaly, Department of Physiotherapy, 35100 Lamia, Greece“Physio’clock” Advanced Physiotherapy Center, 41223 Larissa, GreeceDepartment of Energy Systems, University of Thessaly, Geopolis Campus, 41500 Larisa, GreeceInstitute for Bio-Economy & Agri-Technology, Center for Research and Technology Hellas, 60361 Volos, GreeceCrossFit 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.https://www.mdpi.com/1999-4893/15/3/77CrossFitpredictionensemble learningmachine learning |
| spellingShingle | Serafeim Moustakidis Athanasios Siouras Konstantinos Vassis Ioannis Misiris Elpiniki Papageorgiou Dimitrios Tsaopoulos Prediction of Injuries in CrossFit Training: A Machine Learning Perspective Algorithms CrossFit prediction ensemble learning machine learning |
| title | Prediction of Injuries in CrossFit Training: A Machine Learning Perspective |
| title_full | Prediction of Injuries in CrossFit Training: A Machine Learning Perspective |
| title_fullStr | Prediction of Injuries in CrossFit Training: A Machine Learning Perspective |
| title_full_unstemmed | Prediction of Injuries in CrossFit Training: A Machine Learning Perspective |
| title_short | Prediction of Injuries in CrossFit Training: A Machine Learning Perspective |
| title_sort | prediction of injuries in crossfit training a machine learning perspective |
| topic | CrossFit prediction ensemble learning machine learning |
| url | https://www.mdpi.com/1999-4893/15/3/77 |
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