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Related Experiment Video

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How should we categorise self-reported data on subsequent injuries?

Philip Von Rosen1, Annette Heijne1

  • 1a Department of Neurobiology, Care Sciences, and Society (NVS), Division of Physiotherapy , Karolinska Institutet , Alfred Nobels Allé 23, SE-141 83 Huddinge , Sweden.

European Journal of Sport Science
|March 10, 2017
PubMed
Summary

Understanding subsequent injuries is crucial in sports epidemiology. This study introduces a new model for classifying subsequent injuries using self-reported athlete data, distinguishing between recurrent and new injuries.

Keywords:
Re-injuriesexacerbationsindex injuryprevention

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Area of Science:

  • Sports Medicine
  • Injury Epidemiology
  • Data Science in Sports

Background:

  • Previous injuries significantly increase the risk of new or more severe injuries in athletes.
  • Self-reported data offers valuable insights into injury problems and athlete health.
  • Accurate classification of subsequent injuries is essential for effective injury prevention strategies.

Purpose of the Study:

  • To present a novel method for classifying subsequent injuries using self-reported data.
  • To address the challenges associated with categorizing subsequent injuries based on athlete self-reports.
  • To introduce and demonstrate the Subsequent Injuries Adjusted for Self-reported data (SIAS) model.

Main Methods:

  • Utilized self-reported injury data from 101 adolescent elite track & field athletes over a 52-week period.
  • Applied the newly developed SIAS model to classify 71 identified subsequent injuries.
  • Collected data on athlete recovery status, injury onset, and injury type.

Main Results:

  • Identified 71 subsequent injuries, with 69.0% classified as recurrent and 31.0% as new injuries.
  • The majority of subsequent injuries (84.5%) occurred after athletes reported full recovery from a previous injury.
  • 15.5% of subsequent injuries occurred in athletes who had not fully recovered from a prior injury.

Conclusions:

  • The SIAS model effectively classifies subsequent injuries using self-reported data, considering recovery status, onset, and type.
  • The model aligns with consensus recommendations for injury definition and classification.
  • This approach enhances the understanding of subsequent injuries within self-reported datasets in sports.