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Computational methods to model complex systems in sports injury research: agent-based modelling (ABM) and systems dynamics (SD) modelling
  1. Adam Hulme1,
  2. Scott Mclean1,
  3. Paul M Salmon1,
  4. Jason Thompson2,
  5. Ben R Lane1,
  6. Rasmus Oestergaard Nielsen3
  1. 1 Centre for Human Factors and Sociotechnical Systems, Faculty of Arts, Business and Law, University of the Sunshine Coast, Sippy Downs, Queensland, Australia
  2. 2 Transport, Health and Urban Design (THUD) Research Hub, Melbourne School of Design, Faculty of Architecture, Building and Planning, University of Melbourne, Melbourne, Western Australia, Australia
  3. 3 Department of Public Health, Section for Sports Science, RunSafe Research Group, Aarhus University, Aarhus, Denmark
  1. Correspondence to Dr Adam Hulme, Centre for Human Factors and Sociotechnical Systems, Faculty of Arts, Business and Law, University of the Sunshine Coast, Sippy Downs QLD 4556, Australia; ahulme{at}

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‘Systems thinking’,1 2 complexity theory3 and the ‘complex systems approach’4–7 are gaining momentum among leading sports injury researchers. One reason for this is a growing recognition that traditional risk factor identification methods (eg, stepwise regression modelling) fail to reflect the complex mechanisms of sports injury causation.1 4 8 9 Effective sports injury prevention requires us to understand the complex relationships that occur among a ‘web of interacting determinants’,4 rather than try to isolate the causal effect of individual factors.1 To better understand sports injury mechanisms, researchers are exploring several different approaches. One of them—and the focus of this editorial—is using computational methods that have the potential to describe and simulate the complex and dynamic nature of sports injury causation and prevention in ‘complex systems’.1 4 5 7

Two computational methods for modelling complex systems

The main characteristics of complex systems are described in table 1. These characteristics justify the use of two computational modelling approaches: agent-based modelling (ABM) and systems dynamics (SD) modelling. It is important to note that static modelling (eg, computer-based spreadsheets), differential equations, automata and process algebraic models, Bayesian networks, machine learning, neural networks, social network analysis and Monte Carlo methods are, unlike ABM and SD modelling, not able to model or simulate dynamic causal feedback among fundamentally different factors. Rather, predictive and simple statistical modelling, as well as mathematical algorithms that forecast the probability of future events, are useful for understanding certain aspects of complex systems at fixed time points and/or across one or more levels. These latter approaches are still computational in nature; however, they serve a purpose for a specific type of problem. We also alert the reader that the use of advanced statistical modelling to better understand the complex relationships between time-varying exposures (eg, training/match workloads) and sports injury development (eg, …

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  • Contributors AH was responsible for the concept and write-up. All authors had editorial input into the manuscript.

  • Funding This study was funded by Australian Research Council (FT140100681).

  • Competing interests None declared.

  • Patient consent Not required.

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