Our latest collaboration with the Physiotherapy research group of the university UFMG in Brazil, sheds light on sports injury forecasting and complexity. 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.
Different types of systems. In the simple system, parts do not interact and the outcome is the direct sum of the parts. In the complicated system, simple interactions occur and many more elements produce the outcome. However, the outcome is still the sum of the parts. In the complex system, complex interactions among elements produce an outcome that is different from the parts (exemplified by the different colors). In the complex system, the star represents the emergent outcome and the intermediate symbols represent the states that are dependent upon the interactions with the other elements. The outcome also affects the behavior of the elements (see recursive arrows). In this system, the outcome cannot be predicted by its basic elements.
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. We also 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.
Steps to establish a complex systems approach to sports injury forecasting
Sergio Fonseca, Thales Souza, · Evert Verhagen,· Richard van Emmerik, · Natalia Bittencourt, · Luciana Mendonça, · André Andrade, · Renan Resende, · Juliana M. Ocarino. Sports Injury Forecasting and Complexity: A Synergetic Approach. Sports Medicine (2020) https://doi.org/10.1007/s40279-020-01326-4