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dc.contributor.authorRuddy, Joshua D
dc.contributor.authorShield, Anthony J
dc.contributor.authorManiar, Nirav
dc.contributor.authorWilliams, Morgan D
dc.contributor.authorDuhig, Steven
dc.contributor.authorTimmins, Ryan G
dc.contributor.authorHickey, Jack
dc.contributor.authorBourne, Matthew N
dc.contributor.authorOpar, David A
dc.date.accessioned2018-03-23T12:30:59Z
dc.date.available2018-03-23T12:30:59Z
dc.date.issued2018
dc.identifier.issn0195-9131
dc.identifier.doi10.1249/MSS.0000000000001527
dc.identifier.urihttp://hdl.handle.net/10072/369178
dc.description.abstractPurpose: Three of the most commonly identified hamstring strain injury (HSI) risk factors are age, previous HSI and low levels of eccentric hamstring strength. However, no study has investigated the ability of these risk factors to predict the incidence of HSI in elite Australian footballers. Accordingly, the purpose of this prospective cohort study was to investigate the predictive ability of HSI risk factors using machine learning techniques. Methods: Eccentric hamstring strength, demographic and injury history data were collected at the start of pre-season for 186 and 176 elite Australian footballers in 2013 and 2015 respectively. Any prospectively occurring HSIs were reported to the research team. Using various machine learning techniques, predictive models were built for 2013 and 2015 within-year HSI prediction and between-year HSI prediction (2013 to 2015). The calculated probabilities of HSI were compared to the injury outcomes and area under the curve (AUC) was determined and used to assess the predictive performance of each model. Results: The minimum, maximum and median AUC values for the 2013 models were 0.26, 0.91 and 0.58 respectively. For the 2015 models, the minimum, maximum and median AUC values were, correspondingly, 0.24, 0.92 and 0.57. For the between-year predictive models the minimum, maximum and median AUC values were 0.37, 0.73 and 0.52 respectively. Conclusion: While some iterations of the models achieved near perfect prediction, the large ranges in AUC highlight the fragility of the data. The 2013 models performed slightly better than the 2015 models. The predictive performance of between-year HSI models was poor however. In conclusion, risk factor data cannot be used to identify athletes at an increased risk of HSI with any consistency.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherLippincott Williams & Wilkins
dc.relation.ispartofpagefrom1
dc.relation.ispartofpageto29
dc.relation.ispartofjournalMedicine & Science in Sports & Exercise
dc.subject.fieldofresearchSports Medicine
dc.subject.fieldofresearchHuman Movement and Sports Sciences
dc.subject.fieldofresearchMedical Physiology
dc.subject.fieldofresearchPublic Health and Health Services
dc.subject.fieldofresearchcode110604
dc.subject.fieldofresearchcode1106
dc.subject.fieldofresearchcode1116
dc.subject.fieldofresearchcode1117
dc.titlePredictive Modeling of Hamstring Strain Injuries in Elite Australian Footballers
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dc.description.versionPost-print
gro.description.notepublicThis publication has been entered into Griffith Research Online as an Advanced Online Version.
gro.rights.copyright© 2017 LWW. This is a non-final version of an article published in final form in Medicine and Science in Sports and Exercise, pp. 1-30, 2017. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal link for access to the definitive, published version.
gro.hasfulltextFull Text
gro.griffith.authorBourne, Matthew
gro.griffith.authorDuhig, Steven


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