System Dynamics Model for Acute Non-contact Lower Extremity Injuries Prediction

Hamstring Injury, Interaction, Risk Factors, Sport Injury

Hamstrings strain, Complex Systems, System dynamics, Risk factors interaction, Predictive modeling, Nonlinear

Despite the extensive research on prevention and prediction strategies, hamstrings strains injury (HSI) persists at a high rate in team sports and specifically in football. An initial injury increases the risk for re-injury and affects performance, whereas the financial cost for athletes and teams is crucial due to the time needed for appropriate rehabilitation. For that reason, it is critical to formulate better strategies in order to predict and prevent HSI. This study aims to develop a system dynamics (SD) model to evaluate HSI risk. First, a literature review will be carried out on the current approaches and identification of intrinsic and extrinsic risk factors of hamstrings strain injuries. Second, co-creation workshops based on the method of Group Modeling Building (GMB) will be applied to develop the SD for the HSI model. This co-creation process will involve stakeholders such as sports physiotherapists, doctors, and sports scientists. After creating the SD for HSI model, a one-year prospective cohort study will be performed to validate the model with real data and evaluate the ability of the model to predict HSIs. Sports teams will be invited to take part in the validation of the model. Multiple biomechanical parameters and other personal characteristics will be collected. Then, athletes will be monitored for the occurrence of injury and their exposure to injury risk during training and games. The factors' non-linear interaction will be assessed with the statistical method of structural equation modeling and factor analysis. In this way, the factors' interactions extracted for the qualitative phase of the study (group modeling building process) will be quantitatively evaluated. Validating the model with real data will provide a computer simulation platform to test plausible strategies for preventing hamstrings strain injuries prior to implementation and optimize intervention programs.

Introduction Appropriate hamstring muscle function is essential for the execution of most athletic activities. Muscle injuries, especially the hamstrings muscles, are among the injuries with the higher incidence in team sports. Specifically, muscle injuries constitute approximately one-third of all time-loss injuries in European football clubs, whereas injuries in the hamstrings muscle represent 12% of all injuries. Moreover, the financial impact of the one-month rehabilitation of a player with an HSI in a European team is equal to 500.000 euros. Recently, a systematic review examined 179 HSI's related risk factors and concluded that there is a need to explore these factors' complex and nonlinear interrelationship. Τhe utilization of complex systems computational methods in the sports injury field provides a valid insight into injury etiology and, consequently, a more effective injury prediction. SD modeling and its application to health-related research has been rapidly increasing over the last few years. Examples of successful SD applications include the topics of obesity and diabetes, cancer, cardiovascular, and other chronic diseases in order to capture and better understand the complex etiology and the recovery of the concussion. To sum up, the SD modeling method has proven to be an effective approach to deal with health system problems. However, to the investigators' knowledge, no study has been carried out using SD modeling in order to investigate the complex and dynamic nature of interaction among the factors that contribute to HSI. Aim of the study This study aims first to develop a System Dynamics for Lower Extremity (SDLE) model for evaluating the risk of hamstrings injuries. Further, the model will be calibrated and validated with real data to quantify the factors' interaction and test the ability of the model to predict HSIs. The final aim is to test plausible prevention strategies and propose appropriate policies. Methodology General description of methodological procedure of the SDLE project The proposed project's methodological phases and the proposed study's timetable are presented. Following a clear problem statement, a review of HSI risk factors is to be carried out. Risk factors will be used as variables for developing the SDLE model. This will be facilitated by initially employing the causal loop modeling technique in a series of co-creation workshops with the main stakeholders. Here, the methodology of Group Modeling Building (GMB) will be employed. The aim is to get valuable input from stakeholders such as sports physiotherapists, doctors, coaches, and sports scientists. The output of the co-creation workshops will be a causal loop model depicting the main interrelationships among the HSI risk factors. The creation of the CLD will serve as a communication tool to share the various mental models that exist among stakeholders. Following the development of the CLD in agreement with the stakeholders, we will proceed to the stock and flow model, whereby we quantify the variables identified in the CLD and distinguish between stocks and flows. Then, the formulated SDLE model will be calibrated using real data (risk factors, injuries, exposure rate) from team sports athletes. The final step will be running the simulation model through sensitivity analysis. We will carry out experiments by testing plausible interventions prior to implementation to reduce the risks and tackle the problem of hamstrings injuries. The main phases of the proposed project are described in more detail as follows. SDLE model validation with real data Teams' sports athletes will be invited to participate in this study. The athlete should be free of injury for at least six months or fully rehabilitated from a previous injury to participate in the study. In this phase, a one-year prospective cohort study will be conducted. This phase includes pre-season measurements, injuries and exposure rate data collection during the season, and follow-up measurements in the middle of the season. The recorded data will be inserted into the formulated SDLE model to calibrate and validate the model. Firstly, demographic details and medical history will be collected. Then, specific biomechanical measurements will be assessed. Data processing and statistical analysis The structural equation model approach (SEM) will be used to quantify the interrelationships among collecting variables. Structural equation modeling is a set of statistical techniques used to measure the complex relationships among variables to test the validity of theory using real data. Sensitivity analysis and scenario planning After the SDLE model has been calibrated and validated, different scenarios will be assessed. Different interventions will be applied in order to evaluate the impact of these interventions by means of the SDLE simulation model. As a result, effective policies for tackling the problem of acute noncontact LE injuries will be proposed.

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