The large number of studies in the recent decade dealing with knee injury prevention seems not effective enough to cause a decline in knee injury rates. Thus, it has been proposed to use non-linear mathematical models that simulate the operation of complex and dynamic systems.

The present study aims to analyze the dynamic relationships of the risk factors for knee injuries through system dynamics modeling to effectively predict and prevent knee injury. The first part of this project includes a qualitative study informing the theoretical non-linear interrelationships among the risk factors. The aim is to examine the initial hypothetical model formulated in the first part of the project through statistical analysis such as factor analysis and structural equation modeling. Pre-season and in-season data from questionnaires and biomechanical measurements for risk factors will be collected from at least 100 athletes who participate in high-risk sports. The athletes will be monitored for injuries during one season, and these data will be used in the next part of the research plan. The next part of the project aims to develop a dynamic simulation model for predicting knee injuries using specific equations. The function of the simulation model will predict the propensity of knee injuries over time. The next step includes the validation and calibration of the model based on the knee injuries that occurred during the season. The validated and calibrated model will then provide implications for effective policy decisions in knee injury prevention.

Although a large number of studies in the recent decade deal with the understanding of the risk factors and the improvement of injury prevention programs, the occurrence of knee injuries remains high. As a result, many studies recognize that sports injuries such as knee ACL injury is the result of a complex interaction of multiple risk factors which are interconnected in a non-linear way. Given the approaches to understanding the etiology of knee injuries so far, in most cases, some risk factors are examined and linked linearly to an injury. These approaches are useful enough to show the linear relationship between a particular risk factor and an injury, however, they fail to present the overall picture and dynamic interaction of the coexisting risk factors of an injury. Thus, in recent years, it has been proposed to use non-linear mathematical model that simulates the operation of complex and dynamic systems, with the ultimate goal of better understanding the dynamic interaction of various risk factors and improving prevention programs. Simulation modeling is considered to be a wise option because the complexities of problems are far beyond our capability to solve them manually. Simulation methods can be categorized into four main groups: Monte Carlo, discrete-event simulation, system dynamics, and agent-based simulation. Each group has its benefits regarding the topic examined. System dynamic modeling has been found useful in epidemic modeling and disease prevention strategies but to our knowledge has not yet been used in sports injury prevention topics. Therefore, the present study aims to analyze the captured dynamics of the risk factors for knee injuries through system dynamic modeling.

A better understanding of the intercorrelations among existing risk factors that contribute to knee injuries will be achieved through system dynamic methodology. Further, factors of comparable significance in injury prevention will be revealed. The use of system dynamics modeling in the field of sports injury prevention has not been incorporated into research methodology yet. This project will be the first attempt to capture the causal relationships among key risk factors for a knee injury and their dynamic interplay over time through system dynamic modeling. The developed dynamics model can be used to predict knee injuries and plan effective injury prevention programs.

SD modeling can be developed following specific tasks, including a clear explanation of the problem, generating a qualitative diagram of the system structure, converting the qualitative hypothesis to a quantitated simulation model, testing model, and informing policy decisions about model's implications.

The methodological procedure in this research project can be separated into three consecutive parts. The first part is a qualitive study that will inform about the theoretical non-linear interrelationships among the risk factors. The first step of the qualitative study is a comprehensive literature review to make a list of factors affecting knee injury among athletes and develop hypotheses about their interrelationships. Then, a Causal Loop Diagram (CLD) will be formulated based on the information extracted from the literature review and the application of group modelling building methodology. By this methodology experts in the field of sports injuries (this could be the modeling team) and stakeholders (sports scientists, doctors, other medical experts, coaches, trainers) will engaged in the modelling process based on a series of script workshops.

Specifically, the methodology of group modelling building is based on specific script exercises. More precisely, initially the reviewer will formulate a first perception of the causal relationships among the factors and a first overview of the Causal Loop Diagram. Afterwards, the modeling team will be incorporated in the modelling process. Approximately four series will be conducted for the formulation of the CLD. Then the CLD will be presented to main stakeholders selected by modeling team so as to engage their opinions about the CLD. Their opinions will be used to update the model. Then, the final casual diagram will be formulated.

The second part of the research project the object will be to quantify the interrelationships among factors using a structural equation model approach (SEM). Preseason and in-season data from 100 athletes will be collected using questionnaires and laboratory measurements that have been widely used in knee injury prediction surveys 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. It is similar but more powerful than regression analyses as it can investigate multiple hypothesized relationships among variables simultaneously while multiple regression does not allow such a holistic modeling. SEM can include several integrated analytic techniques such as group variance comparisons associated with ANOVA as well as regression analysis. Factor analysis is another special case of SEM whereby unobserved variables (factor or latent variables) are calculated from measured variables. In this way, SEM allows researchers to explain the development of phenomena such as diseases or injuries. The athletes will be monitored for injuries during one season and these data will be used in the third part of the research plan as described below.

In the third part, based on findings of the two previous studies described above, a dynamic simulation model for the prediction of knee injuries will be developed. The already collected data from the previous steps and analysis will be used to develop the simulation model. Using specific equations, the function of the simulation model will predict the propensity of knee injuries. Based on the interaction among the variables expressed in the CLD a perception/prediction of the likelihood of knee injury will be provided. Furthermore, through the model it would be able to see the changes in the variable of interest that is knee injuries if testist to alter the values of a variable.

The last step includes the validation and calibration of the model. The aim of this step is to test how close the estimation for knee injuries of the model were to the incidence of knee injuries occurred during the season.

It is expected that the members of sports medicine community use the results of the study to predict knee injuries and gain insight of the key risk factors, as well as their interrelationships and effectively plan injury prevention programs and strategies

Inclusion Criteria:

• Healthy professional athletes that participate in team sports (football, handball, basketball)

Exclusion Criteria:

• Injured athletes

100 healthy athletes (17-40 years old) that participate in sports that include activities such as jumping, slowing down, and change of direction by pivoting such as football, basketball, and handball