Can we predict injury in male football players based on the functional movement screen and other tests of injury risk? (PhD Academy Award)

Philp, Fraser

doi:10.1136/bjsports-2019-100764

Cited by 1 publication

(3 citation statements)

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“…Injury characteristics relating to severity have been outlined in table 1. Further information regarding injury reporting and recording methods and participant characteristics have been reported previously 8…”

Section: Methodsmentioning

confidence: 99%

“…The dataset contained a total of 34 variables (table 2). Further information regarding the methods of recording of the independent variables have been reported previously 8…”

Section: Methodsmentioning

confidence: 99%

“…These models have limited clinical applicability for the following reasons: (1) the models are often ‘black-boxes’ that provide no physiological explanation for the predictor variables, and sports and exercise medicine practitioners may have an inherent distrust of complex models in which the results cannot be explained8 9; (2) instability in model performance, stemming from small sample sizes combined with large numbers of correlated independent variables; (3) a lack of external validation. There are therefore two gaps that need to be addressed: Firstly, to explore if traditional predictor selection methods can be replaced with modern methods.…”

Section: Introductionmentioning

confidence: 99%

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Improving predictor selection for injury modelling methods in male footballers

Philp

Al-shallawi

Kyriacou

et al. 2020

BMJ Open Sport Exerc Med

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ObjectivesThis objective of this study was to evaluate whether combining existing methods of elastic net for zero-inflated Poisson and zero-inflated Poisson regression methods could improve real-life applicability of injury prediction models in football.MethodsPredictor selection and model development was conducted on a pre-existing dataset of 24 male participants from a single English football team’s 2015/2016 season.ResultsThe elastic net for zero-inflated Poisson penalty method was successful in shrinking the total number of predictors in the presence of high levels of multicollinearity. It was additionally identified that easily measurable data, that is, mass and body fat content, training type, duration and surface, fitness levels, normalised period of ‘no-play’ and time in competition could contribute to the probability of acquiring a time-loss injury. Furthermore, prolonged series of match-play and increased in-season injury reduced the probability of not sustaining an injury.ConclusionFor predictor selection, the elastic net for zero-inflated Poisson penalised method in combination with the use of ZIP regression modelling for predicting time-loss injuries have been identified appropriate methods for improving real-life applicability of injury prediction models. These methods are more appropriate for datasets subject to multicollinearity, smaller sample sizes and zero-inflation known to affect the performance of traditional statistical methods. Further validation work is now required.

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Section: Methodsmentioning

confidence: 99%

“…The dataset contained a total of 34 variables (table 2). Further information regarding the methods of recording of the independent variables have been reported previously 8…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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