Predicting Sports Injuries in Male Student Athletes Using FMS, Y Balance, and LESS Tests

Background & Purpose
In response to the high incidence of sports injuries, researchers have recently focused on the development of field screening tools that are capable of identifying risk factors for musculoskeletal injuries, including faulty and compensatory movement patterns, weak core stability, and deficiencies in athletes' balance, so that targeted interventions can be carried out. FMS, LESS and Y balance test are some examples of the most famous functional tests that can be used to identify athletes at risk of injury. But in general, the evidence and results presented for the use of this tool in injury prediction are contradictory, and their use as an injury prediction tool among athletes is challenging. Therefore, the present study was conducted with the aim of using Y balance test, LESS and FMS test to identify male athletes prone to injury.
Methods & Materials
This prospective cohort study investigated the correlation between functional tests including Y balance test, LESS and FMS and sports injury as well as the ability to predict injury through these tests. The population of the current research was male university athletes and the research sample was 189 male student athletes participating in the 15th Iranian Student Sports Olympiad, who were evaluated as available. First, the examiners were trained in six teams of five people. Before the start of the Olympiad and before the dispatch of the convoys, the trained forces appeared at the venue of the teams' camp and evaluated the tests from the samples and the scores of the subjects. it is registered. The registration of the injuries of the athletes during the competitions was done in such a way that the examiners were present next to the doctor of the competitions during the competitions and when a player was injured and needed the help of the medical team, this situation was registered as an injury.  Other things such as mechanism of injury, type of movement leading to injury, time of injury, position of the player at the time of injury, history of injury, area of injury were recorded in the data collection form through interviews with the injured athlete. In order to statistically analyze the data, logistic regression test and Pearson correlation coefficient were used. Also, the descriptive information related to sports injuries was reported in the form of frequency and frequency percentage. The significance level in this research was considered equal to 0.95 and alpha smaller than 0.05. The information obtained and recorded from the evaluations was analyzed using SPSS version 26 software.
Results
A total of 39 injuries were registered during the competition in 189 subjects. Among them, 8 injuries were due to contact, 24 were non-contact injuries, 4 injuries were due to contact with the ground and 3 injuries were due to contact with equipment. Bivariate logistic regression was used to test the present hypothesis. In this way, the dependent variable "injured" was classified with two classes "injured" and "no injury". Injured people were given a number of 1 and uninjured people were given a number of zero. In this research, there were four independent predictor variables, including FMS total score, LESS test total score, and y balance test total score for both legs as predictor variables. These variables were entered into the model with the advanced method of Wald's statistics to check and predict the relationship between the variables and the amount of damage. Based on the results, none of the predictor variables were included in the process of the logistic regression equation. This means that in this research, the predictor variables did not have the ability to predict the criterion variable, i.e. injury.
Conclusion
In this study, the injury rate was calculated at 20/64 per 100 people, which is a high rate compared to many major sports events in the world. Considering the more than twice the rate of injuries in the Student Olympiad compared to the Tokyo Olympic Games, which usually has a lot of pressure and was also held during the peak of the Coronavirus; It is suggested that in addition to the physical and mental preparation of participating student athletes, other factors affecting the occurrence of injuries such as proper timing for holding the Olympiad, use of standard sports equipment and facilities such as hall, clothes And the right sports shoes should also be taken into account so that we can witness the minimum injury with the correct management of the risk factors of the sports environment. It should be mentioned that with the recommendations made by the International Olympic Committee in recent years, the injury rate among athletes participating in the Olympics has not only increased but also decreased. The results of this research showed that none of the predictor variables, including: functional movement screening test, y balance test and landing error scoring system, have the ability to predict injury in boys participating in the Student Olympiad.
Keywords: Injury Prevention, Functional Movement Screen (FMS), Y Balance Test (YBT), Landing Error Scoring System (LESS).
Article Message
According to the results of this research, it can be concluded that it is not possible to predict injury in a general way using FMS, Y balance test and less. Although these tests have different natures, there is a possibility that the athlete does not have a strong weakness in performing these tests due to his high preparation, and this can be one of the possible reasons for the lack of predictability of these tests in this research.