{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T10:13:43Z","timestamp":1774260823631,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T00:00:00Z","timestamp":1645660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/a15030077","type":"journal-article","created":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T21:11:07Z","timestamp":1645737067000},"page":"77","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Prediction of Injuries in CrossFit Training: A Machine Learning Perspective"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1090-2177","authenticated-orcid":false,"given":"Serafeim","family":"Moustakidis","sequence":"first","affiliation":[{"name":"AIDEAS O\u00dc, Narva mnt 5, 10117 Tallinn, Harju Maakond, Estonia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7654-0543","authenticated-orcid":false,"given":"Athanasios","family":"Siouras","sequence":"additional","affiliation":[{"name":"AIDEAS O\u00dc, Narva mnt 5, 10117 Tallinn, Harju Maakond, Estonia"},{"name":"Department of Computer Science and Biomedical Informatics, School of Science, University of Thessaly, 35131 Lamia, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8810-4327","authenticated-orcid":false,"given":"Konstantinos","family":"Vassis","sequence":"additional","affiliation":[{"name":"School of Health Sciences, University of Thessaly, Department of Physiotherapy, 35100 Lamia, Greece"}]},{"given":"Ioannis","family":"Misiris","sequence":"additional","affiliation":[{"name":"\u201cPhysio\u2019clock\u201d Advanced Physiotherapy Center, 41223 Larissa, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2498-9661","authenticated-orcid":false,"given":"Elpiniki","family":"Papageorgiou","sequence":"additional","affiliation":[{"name":"Department of Energy Systems, University of Thessaly, Geopolis Campus, 41500 Larisa, Greece"},{"name":"Institute for Bio-Economy & Agri-Technology, Center for Research and Technology Hellas, 60361 Volos, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7355-0511","authenticated-orcid":false,"given":"Dimitrios","family":"Tsaopoulos","sequence":"additional","affiliation":[{"name":"Institute for Bio-Economy & Agri-Technology, Center for Research and Technology Hellas, 60361 Volos, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1515\/rns.2011.017","article-title":"The Positive Impact of Physical Activity on Cognition during Adulthood: A Review of Underlying Mechanisms, Evidence and Recommendations","volume":"22","author":"Ratey","year":"2011","journal-title":"Rev. Neurosci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sun, F., Norman, I.J., and While, A.E. (2013). Physical Activity in Older People: A Systematic Review. BMC Public Health, 13.","DOI":"10.1186\/1471-2458-13-449"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"103810","DOI":"10.1016\/j.ijnurstu.2020.103810","article-title":"Effects of Physical Exercise on Executive Function in Cognitively Healthy Older Adults: A Systematic Review and Meta-Analysis of Randomized Controlled Trials: Physical Exercise for Executive Function","volume":"114","author":"Xiong","year":"2020","journal-title":"Int. J. Nurs. Stud."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1136\/bjsm.37.5.384","article-title":"Risk Factors for Sports Injuries\u2014A Methodological Approach","volume":"37","author":"Bahr","year":"2003","journal-title":"Br. J. Sports Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1177\/03635465030310063901","article-title":"Clinical, Functional, and Radiologic Outcome in Team Handball Players 6 to 11 Years after Anterior Cruciate Ligament Injury: A Follow-up Study","volume":"31","author":"Myklebust","year":"2003","journal-title":"Am. J. Sports Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1179572719897069","DOI":"10.1177\/1179572719897069","article-title":"Identifying the Most Common CrossFit Injuries in a Variety of Athletes","volume":"9","author":"Alekseyev","year":"2020","journal-title":"Rehabil. Process Outcome"},{"key":"ref_7","first-page":"1213","article-title":"Musculoskeletal Injuries in Portuguese CrossFit Practitioners","volume":"59","author":"Minghelli","year":"2019","journal-title":"J. Sports Med. Phys. Fit."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2325967114531177","DOI":"10.1177\/2325967114531177","article-title":"Injury Rate and Patterns among CrossFit Athletes","volume":"2","author":"Weisenthal","year":"2014","journal-title":"Orthop. J. Sports Med."},{"key":"ref_9","first-page":"53","article-title":"Retrospective Injury Epidemiology and Risk Factors for Injury in CrossFit","volume":"16","author":"Montalvo","year":"2017","journal-title":"J. Sports Sci. Med."},{"key":"ref_10","first-page":"1147","article-title":"Rates and Risk Factors of Injury in CrossFit: A Prospective Cohort Study","volume":"57","author":"Moran","year":"2017","journal-title":"J. Sports Med. Phys Fit."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2325967116663706","DOI":"10.1177\/2325967116663706","article-title":"An Epidemiological Profile of Crossfit Athletes in Brazil","volume":"4","author":"Sprey","year":"2016","journal-title":"Orthop. J. Sports Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1177\/2165079916685568","article-title":"The Benefits and Risks of CrossFit: A Systematic Review","volume":"65","author":"Meyer","year":"2017","journal-title":"Workplace Health Saf."},{"key":"ref_13","unstructured":"Da Silva, C. (2021, December 10). A Profile of Injuries among Participants at the 2013 CrossFit Games in Durban 2015. Available online: https:\/\/openscholar.dut.ac.za\/handle\/10321\/1415."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1177\/1941738116666073","article-title":"Shoulder Injuries in Individuals Who Participate in CrossFit Training","volume":"8","author":"Summitt","year":"2016","journal-title":"Sports Health"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2325967117745263","DOI":"10.1177\/2325967117745263","article-title":"Injury Incidence and Patterns among Dutch CrossFit Athletes","volume":"5","author":"Mehrab","year":"2017","journal-title":"Orthop. J. Sports Med."},{"key":"ref_16","unstructured":"Soares, M.R.A.D.R. (2017). An Epidemiological Profile of Crossfit Participants in Portugal 2017. [Master\u2019s Thesis, Universidade Lus\u00f3fona de Humanidades e Tecnologias]."},{"key":"ref_17","first-page":"1544","article-title":"The Risk of Injuries among CrossFit Athletes: An Italian Observational Retrospective Survey","volume":"59","author":"Tafuri","year":"2019","journal-title":"J. Sports Med. Phys. Fit."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2325967118803100","DOI":"10.1177\/2325967118803100","article-title":"A 4-Year Analysis of the Incidence of Injuries Among CrossFit-Trained Participants","volume":"6","author":"Feito","year":"2018","journal-title":"Orthop. J. Sports Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e1402","DOI":"10.6061\/clinics\/2019\/e1402","article-title":"CrossFit\u00ae: Injury Prevalence and Main Risk Factors","volume":"74","author":"Louzada","year":"2019","journal-title":"Clinics"},{"key":"ref_20","first-page":"11","article-title":"Incid\u00eancia de les\u00f5es musculoesquel\u00e9ticas em praticantes de CrossFit","volume":"1","author":"Pereira","year":"2019","journal-title":"Rev. Ci\u00eancias da Sa\u00fade-UNIPLAN"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2325967120908884","DOI":"10.1177\/2325967120908884","article-title":"CrossFit and the Epidemiology of Musculoskeletal Injuries: A Prospective 12-Week Cohort Study","volume":"8","author":"Szeles","year":"2020","journal-title":"Orthop. J. Sports Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.jts.2019.12.002","article-title":"\u00c9valuation Du Taux de Blessures Chez Les Pratiquants de CrossFit En France","volume":"37","author":"Gile","year":"2020","journal-title":"J. Traumatol. du Sport"},{"key":"ref_23","first-page":"3","article-title":"Injury in CrossFit\u00ae: A Systematic Review of Epidemiology and Risk Factors","volume":"50","author":"Terrados","year":"2021","journal-title":"Phys. Sportsmed."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2358","DOI":"10.1016\/j.arth.2018.02.067","article-title":"Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Computing: What Do These Terms Mean and How Will They Impact Health Care?","volume":"33","author":"Bini","year":"2018","journal-title":"J. Arthroplasty"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2325967117729798","DOI":"10.1177\/2325967117729798","article-title":"The Effect of Regular-Season Rest on Playoff Performance among Players in the National Basketball Association","volume":"5","author":"Belk","year":"2017","journal-title":"Orthop. J. Sports Med."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1080\/1091367X.2013.805137","article-title":"Data Mining in Elite Sports: A Review and a Framework","volume":"17","author":"Ofoghi","year":"2013","journal-title":"Meas. Phys. Educ. Exerc. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1023\/A:1022880431298","article-title":"Induction of Decision Trees and Bayesian Classification Applied to Diagnosis of Sport Injuries","volume":"21","author":"Kononenko","year":"1997","journal-title":"J. Med. Syst."},{"key":"ref_28","first-page":"A25","article-title":"Prediction of Physical Performance Using Data Mining.(Measurement)","volume":"74","author":"Fielitz","year":"2003","journal-title":"Res. Q. Exerc. Sport"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"326","DOI":"10.3171\/jns.2002.97.2.0326","article-title":"Predicting Recovery in Patients Suffering from Traumatic Brain Injury by Using Admission Variables and Physiological Data: A Comparison between Decision Tree Analysis and Logistic Regression","volume":"97","author":"Andrews","year":"2002","journal-title":"J. Neurosurg."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jbiomech.2017.10.025","article-title":"Supervised Learning Techniques and Their Ability to Classify a Change of Direction Task Strategy Using Kinematic and Kinetic Features","volume":"66","author":"Richter","year":"2018","journal-title":"J. Biomech."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2325967120S00360","DOI":"10.1177\/2325967120S00360","article-title":"Machine Learning Accurately Predicts Next Season NHL Player Injury Before It Occurs: Validation of 10,449 Player-Years from 2007-17","volume":"8","author":"Wright","year":"2020","journal-title":"Orthop. J. Sports Med."},{"key":"ref_32","first-page":"A5","article-title":"009 Big Data in Youth Elite Football: Could Machine Learning Help Us to Better Understand Injury Risk?","volume":"54","author":"Rommers","year":"2020","journal-title":"Br. J. Sports Med."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1123\/ijspp.2017-0299","article-title":"Relationships between the External and Internal Training Load in Professional Soccer: What Can We Learn from Machine Learning?","volume":"13","author":"Jaspers","year":"2018","journal-title":"Int. J. Sports Physiol. Perform."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1249\/MSS.0000000000001527","article-title":"Predictive Modeling of Hamstring Strain Injuries in Elite Australian Footballers","volume":"50","author":"Ruddy","year":"2018","journal-title":"Med. Sci. Sports Exerc."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2325967120963046","DOI":"10.1177\/2325967120963046","article-title":"Machine Learning Outperforms Regression Analysis to Predict Next-Season Major League Baseball Player Injuries: Epidemiology and Validation of 13,982 Player-Years from Performance and Injury Profile Trends, 2000\u20132017","volume":"8","author":"Karnuta","year":"2020","journal-title":"Orthop. J. Sports Med."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1055\/a-1231-5304","article-title":"New Machine Learning Approach for Detection of Injury Risk Factors in Young Team Sport Athletes","volume":"42","author":"Jauhiainen","year":"2021","journal-title":"Int. J. Sports Med."},{"key":"ref_37","first-page":"e89","article-title":"Introduction to Sample Size Calculation","volume":"2","author":"Arifin","year":"2013","journal-title":"Educ. Med. J."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3712","DOI":"10.1016\/j.patcog.2010.05.007","article-title":"SVM-FuzCoC: A Novel SVM-Based Feature Selection Method Using a Fuzzy Complementary Criterion","volume":"43","author":"Moustakidis","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1080\/10255842.2011.554408","article-title":"Feature Selection Based on a Fuzzy Complementary Criterion: Application to Gait Recognition Using Ground Reaction Forces","volume":"15","author":"Moustakidis","year":"2012","journal-title":"Comput. Methods Biomech. Biomed. Eng."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Witten, I., Frank, E., and Hall, M. (2011). Introduction to Data Mining. Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann-Elsevier. [3rd ed.].","DOI":"10.1016\/B978-0-12-374856-0.00010-9"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Atkeson, C.G., Moore, A.W., and Schaal, S. (1997). Locally Weighted Learning. Lazy Learning, Springer.","DOI":"10.1007\/978-94-017-2053-3_2"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-Vector Networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A Decision-Theoretic Generalization of on-Line Learning and an Application to Boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Le, N.Q.K., Kha, Q.H., Nguyen, V.H., Chen, Y.-C., Cheng, S.-J., and Chen, C.-Y. (2021). Machine Learning-Based Radiomics Signatures for EGFR and KRAS Mutations Prediction in Non-Small-Cell Lung Cancer. Int. J. Mol. Sci., 22.","DOI":"10.3390\/ijms22179254"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Hung, T.N.K., Le, N.Q.K., Le, N.H., Van Tuan, L., Nguyen, T.P., Thi, C., and Kang, J.-H. (2022). An AI-based Prediction Model for Drug-drug Interactions in Osteoporosis and Paget\u2019s Diseases from SMILES. Mol. Inform., 2100264.","DOI":"10.1002\/minf.202100264"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Kokkotis, C., Ntakolia, C., Moustakidis, S., Giakas, G., and Tsaopoulos, D. (2022). Explainable Machine Learning for Knee Osteoarthritis Diagnosis Based on a Novel Fuzzy Feature Selection Methodology. Phys. Eng. Sci. Med., 1\u201311.","DOI":"10.21203\/rs.3.rs-777000\/v1"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"e14388","DOI":"10.1111\/ctr.14388","article-title":"State-of-the-art Machine Learning Algorithms for the Prediction of Outcomes after Contemporary Heart Transplantation: Results from the UNOS Database","volume":"35","author":"Kampaktsis","year":"2021","journal-title":"Clin. Transplant."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1186\/1471-2288-13-91","article-title":"The McNemar Test for Binary Matched-Pairs Data: Mid-p and Asymptotic Are Better than Exact Conditional","volume":"13","author":"Fagerland","year":"2013","journal-title":"BMC Med. Res. Methodol."},{"key":"ref_50","first-page":"46","article-title":"A Description of Training Characteristics and Its Association with Previous Musculoskeletal Injuries in Recreational Runners: A Cross-Sectional Study","volume":"16","author":"Junior","year":"2012","journal-title":"Braz. J. Phys. Ther."},{"key":"ref_51","first-page":"328","article-title":"ACSM\u2019s Guidelines for Exercise Testing and Prescription 9th Ed. 2014","volume":"58","author":"Ferguson","year":"2014","journal-title":"J. Can. Chiropr. Assoc."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2325967119825557","DOI":"10.1177\/2325967119825557","article-title":"Risk Factors for Baseball-Related Arm Injuries: A Systematic Review","volume":"7","author":"Agresta","year":"2019","journal-title":"Orthop. J. Sports Med."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1007\/s11547-006-0005-8","article-title":"Supraspinatus Tendon US Morphology in Basketball Players: Correlation with Main Pathologic Models of Secondary Impingement Syndrome in Young Overhead Athletes. Preliminary Report","volume":"111","author":"Girometti","year":"2006","journal-title":"Radiol. Med."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1111\/sms.12636","article-title":"Incidence and Risk Factors of Injuries in Brazilian Elite Handball Players: A Prospective Cohort Study","volume":"27","author":"Giroto","year":"2017","journal-title":"Scand. J. Med. Sci. Sports"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1007\/s40279-014-0194-6","article-title":"What Are the Main Risk Factors for Running-Related Injuries?","volume":"44","author":"Saragiotto","year":"2014","journal-title":"Sports Med."},{"key":"ref_56","first-page":"36","article-title":"Extreme Conditioning Programs and Injury Risk in a US Army Brigade Combat Team","volume":"11","author":"Grier","year":"2013","journal-title":"US Army Med. Dep. J."},{"key":"ref_57","first-page":"672","article-title":"Retrospective Injury Epidemiology of One Hundred One Competitive Oceania Power Lifters: The Effects of Age, Body Mass, Competitive Standard, and Gender","volume":"20","author":"Keogh","year":"2006","journal-title":"J. Strength Cond. Res."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2061","DOI":"10.1519\/JSC.0b013e3181b86cb9","article-title":"Sex Differences in \u201cWeightlifting\u201d Injuries Presenting to United States Emergency Rooms","volume":"23","author":"Quatman","year":"2009","journal-title":"J. Strength Cond. Res. Strength Cond. Assoc."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/15\/3\/77\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:27:03Z","timestamp":1760135223000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/15\/3\/77"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,24]]},"references-count":58,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["a15030077"],"URL":"https:\/\/doi.org\/10.3390\/a15030077","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,24]]}}}