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Systematic review
Predictive models of injury risk in male professional football players: a systematic review
  1. Francisco Martins1,2,3,
  2. Krzysztof Przednowek4,
  3. Francisco Santos1,2,3,
  4. Cíntia França2,3,5,
  5. Diogo Martinho1,3,
  6. Élvio Rúbio Gouveia2,3,6,
  7. Adilson Marques7,8,
  8. Hugo Sarmento1
  1. 1 Research Unit for Sport and Physical Activity (CIDAF), Faculty of Sport Sciences and Physical Education, University of Coimbra, 3004-504 Coimbra, Portugal
  2. 2 Department of Physical Education and Sport, University of Madeira, 9020-105 Funchal, Portugal
  3. 3 LARSYS, Interactive Technologies Institute, 9020-105 Funchal, Portugal
  4. 4 Institute of Physical Culture Sciences, Medical College, University of Rzeszów, 35-959 Rzeszów, Poland
  5. 5 Research Center in Sports Sciences, Health Sciences, and Human Development, (CIDESD), 5000-801 Vila Real, Portugal
  6. 6 Swiss Center of Expertise in Life Course Research LIVES, 1227 Carouge, Switzerland
  7. 7 CIPER, Faculty of Human Kinetics, University of Lisbon, 1495-751 Lisbon, Portugal
  8. 8 ISAMB, Faculty of Medicine, University of Lisbon, 1649-020 Lisbon, Portugal
  1. Correspondence to Professor Hugo Sarmento; hugo.sarmento{at}uc.pt

Abstract

Background One of the challenges for professional football players is injuries. Due to their influence on their teams, injuries greatly impact the sports business. This research aims to assess predictive models of injury risk in male professional football players.

Methods A systematic literature review was performed, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The search was conducted in the PubMed, Web of Science and Scopus databases. Two independent reviewers screened articles, assessed eligibility and extracted data. Methodological quality was determined by the Newcastle–Ottawa Scale.

Results 26 studies met the inclusion criteria.

Discussion Various statistical techniques were used in research on injury prediction in professional football, with logistic regression being the most used. The assessment predictors, especially the area under the receiver operating characteristic Curve, showed significant variation, which indicates the prediction models’ efficacy. The focus was frequently on lower limb injuries, where several risk predictors, including muscular strength, flexibility and global positioning system-derived data, were found to substantially impact the occurrence of injuries. Prominent predictors included age, position, physiological parameters, injury history and genetic polymorphisms.

Conclusions This comprehensive analysis highlights the complexity of injury prediction and reinforces the necessity for football injury research to adopt a multivariate approach with accuracy and comprehensiveness.

PROSPERO registration number CRD42023465524.

  • Exposure
  • Injury Compensation
  • Risk Perception
  • Systematic Review
  • Risk Factor Research
  • Epidemiology

Data availability statement

Data sharing not applicable as no datasets generated and/or analysed for this study.

https://doi.org/10.1136/ip-2024-045322

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    Data availability statement

    Data sharing not applicable as no datasets generated and/or analysed for this study.

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    Footnotes

    • Contributors Conceptualisation, FM, KP, HS and ERG; Methodology, FM, FS, CF and DM; Validation, KP, HS, AM and ERG; Formal analysis, FM, FS, CF and DM; Investigation, FM, FS, CF and DM; Resources, HS and ERG; Writing—original draft preparation, FM, CF and DM; Writing—review and editing, KP, ERG, AM and HS; Visualisation, FM, ERG, AM and HS; Project administration, ERG, KP and HS; Funding acquisition, ERG and HS; Guarantor, H.S. All authors have read and agreed to the published version of the manuscript. FM accepted full responsibility for the finished work and/or the conduct of the study, had access to the data and controlled the decision to publish.

    • Funding FM acknowledges support from Foundation for Science and Technology under a doctoral scholarship 2023-2027 (2023.01187.BD). FM, CF and ERG acknowledge support from LARSyS—Portuguese national funding agency for science, research and technology (FCT) pluriannual funding 2020–2023 (Reference: UIDB/50009/2020). This research was funded by the Portuguese Recovery and Resilience Program (PRR), IAPMEI/ANI/FCT under Agenda C645022399-00000057 (eGamesLab).

    • Competing interests None declared.

    • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

    • Provenance and peer review Not commissioned; externally peer reviewed.