Description
Background:
Recent evidence suggests analyzing sports injuries as a complex phenomenon and proposes ecological and dynamic system approaches to better understand the etiology of injuries1. This procedure is supported by the fact that the factors contributing to sports injury interplay dynamically increase or decrease the risk of injury across seasons.Objectives: The present qualitative study aimed to present a conceptual model of the ACL injury risk factors through System Dynamics (SD) methodology. The proposed model exposes some of the complex nonlinear interactions of the ACL injury risk factors. It is the first part of the development of a simulation model through SD methodology.
Objectives:
The present qualitative study aimed to present a conceptual model of the Anterior Cruciate Ligament (ACL) injury risk factors through System Dynamics (SD) methodology2. The proposed model exposes some of the complex nonlinear interactions of the ACL injury risk factors. It is the first part of the development of a simulation model through SD methodology.
Methods:
The SD methodology for conceptual model creation was used, based on evidence from ACL injury-relevant literature and brainstorming with experts. For the design of the causal loop diagram Vensim PLE x64 software was used.
Results:
Although the present evidence on ACL risk factors is based mainly on the linear effect of the neuromuscular ACL risk factors, other groups of risk factors seem to interact dynamically increasing the risk for an ACL injury. Thus, the present initial conceptual ACL injury model (Figure 1.) has synthesized the dynamic interaction among intrinsic factors such as anatomical and neuromuscular but also includes the contribution of other risk factors such as social and psychological. Specifically, as presented in the ACL injury causal loop diagram, the level of neuromuscular control affects landing biomechanics, which in combination with the anatomical factors that increase ACL shear forces could formulate a profile that predisposes an athlete to ACL injury. In addition, the simultaneous dynamic interaction of the pressuring behaviors of the social environment and team of the athlete, as well as the level of coping of the athlete with the physiological and psychological demands, synthesize a very complex framework of ACL injury etiology. The present diagram shows whether the relationship between two variables is positive or negative. When a positive change in a variable causes a positive change to the other variable and a negative causal link appears in case of opposite polarities.
Conclusions:
The present ACL causal loop model constitutes an initial conceptual presentation of the dynamic interaction of intrinsic and extrinsic risk factors for ACL injury. Future development includes converting the qualitative hypothesis to a quantitated simulation model. The model will lead sports scientists to a better understanding of the dynamic interaction of ACL risk factors and to create plausible strategies for ACL injury prevention programs.
References:
1.Bittencourt NFN, Meeuwisse WH, Mendonça LD, et al. Complex systems approach for sports injuries: moving from risk factor identification to injury pattern recognition—narrative review and new concept. Br J Sports Med. 2016;50(21):1309–14
2.Sterman JD. Business Dynamics: Systems Thinking and Modeling for a Complex World. Boston: Irwin/McGraw-Hill; 2000.
