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Sports medicine (Auckland, N.Z.)
Oct, 2020;50 (10): 1757-1770.

Sports Injury Forecasting and Complexity: A Synergetic Approach.

Sergio T Fonseca, Thales R Souza, Evert Verhagen, Richard van Emmerik, Natalia F N Bittencourt, Luciana D M Mendonça, André G P Andrade, Renan A Resende, Juliana M Ocarino
Author Information
  1. Sergio T Fonseca: Graduate Program in Rehabilitation Sciences, Physical Therapy Department, and Centro de Treinamento Esportivo, Universidade Federal de Minas Gerais, 6627, Pampulha, 31270-901, Belo Horizonte, MG, Brazil. sfonseca@ufmg.br. ORCID
  2. Thales R Souza: Graduate Program in Rehabilitation Sciences, Physical Therapy Department, and Centro de Treinamento Esportivo, Universidade Federal de Minas Gerais, 6627, Pampulha, 31270-901, Belo Horizonte, MG, Brazil.
  3. Evert Verhagen: Amsterdam Collaboration on Health & Safety in Sports, Department of Public and Occupational Health, Amsterdam Movement Sciences, Amsterdam UMC, Amsterdam, Netherlands.
  4. Richard van Emmerik: Motor Control Laboratory, Department of Kinesiology, University of Massachusetts Amherst, Amherst, MA, 01003, USA.
  5. Natalia F N Bittencourt: Uni-BH University, Belo Horizonte, MG, Brazil.
  6. Luciana D M Mendonça: Physical Therapy Department, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Diamantina, MG, Brazil.
  7. André G P Andrade: Graduate Program in Sports Sciences, Sports Department, and Centro de Treinamento Esportivo, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.
  8. Renan A Resende: Graduate Program in Rehabilitation Sciences, Physical Therapy Department, and Centro de Treinamento Esportivo, Universidade Federal de Minas Gerais, 6627, Pampulha, 31270-901, Belo Horizonte, MG, Brazil.
  9. Juliana M Ocarino: Graduate Program in Rehabilitation Sciences, Physical Therapy Department, and Centro de Treinamento Esportivo, Universidade Federal de Minas Gerais, 6627, Pampulha, 31270-901, Belo Horizonte, MG, Brazil.
PMID: 32757162 DOI: 10.1007/s40279-020-01326-4

Abstract

The understanding that sports injury is the result of the interaction among many factors and that specific profiles could increase the risk of the occurrence of a given injury was a significant step in establishing programs for injury prevention. However, injury forecasting is far from being attained. To be able to estimate future states of a complex system (forecasting), it is necessary to understand its nature and comply with the methods usually used to analyze such a system. In this sense, sports injury forecasting must implement the concepts and tools used to study the behavior of self-organizing systems, since it is by self-organizing that systems (i.e., athletes) evolve and adapt (or not) to a constantly changing environment. Instead of concentrating on the identification of factors related to the injury occurrence (i.e., risk factors), a complex systems approach looks for the high-order variables (order parameters) that describe the macroscopic dynamic behavior of the athlete. The time evolution of this order parameter informs on the state of the athlete and may warn about upcoming events, such as injury. In this article, we describe the fundamental concepts related to complexity based on physical principles of self-organization and the consequence of accepting sports injury as a complex phenomenon. In the end, we will present the four steps necessary to formulate a synergetics approach based on self-organization and phase transition to sports injuries. Future studies based on this experimental paradigm may help sports professionals to forecast sports injuries occurrence.

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Grants

  1. Code 001/Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
  2. 306477/2019-0/Conselho Nacional de Desenvolvimento Científico e Tecnológico
  3. 00364-18/Fundação de Amparo à Pesquisa do Estado de Minas Gerais

MeSH Term

Athletic Injuries
Forecasting
Humans
Risk Assessment
Journal Article Review
Article has an altmetric score of 16

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