Introduction
Stiffness in the neuromuscular system can lead to dynamic instability of the knee. Dynamic knee instability, sometimes referred to as knee valgus, is the result of a multitude of lower limb motions and rotations, including hip adduction, internal rotation, knee abduction, tibial external rotation, and anterior displacement of the tibia [1]. Anterior cruciate ligament (ACL) damage is more likely to occur when aberrant lower limb neuromuscular activity aggravates the degree of knee valgus.
[2]. On the other hand, joint stress and the chance of ACL injury during sports activities are increased by neuromuscular dysfunctions, which are the main cause of non-contact ACL injuries in athletes [2]. In an investigation conducted in 2010 to determine the standard knee valgus angle in physically active individuals, it was shown that men naturally exhibit a knee valgus angle of 3-8 degrees while landing on two legs and 1-9 degrees when landing on one leg. In addition, the results of an independent 2011 study that aimed to determine the average dynamic knee valgus (DKV) in 14.21-year-old male and female basketball and volleyball players showed that the natural valgus angle in the right and left legs during a single-leg squat with a knee flexion angle of 60 degrees is 7.08 degrees and 8.68 degrees, respectively [3]. An injury or disturbance of knee stability can be caused by some causes, one of which is weariness, centrally and environmentally [4]. When metabolic exhaustion sets in during exercise and impairs dynamic knee joint stability, there may be a higher chance of knee injury [5]. Increased pressures and torques on the knee as well as disruptions in joint stability under fatigue circumstances are primarily caused by diminished muscle force, poor coordination, and delayed neuromuscular activation [6]. Furthermore, evidence points to a correlation between weariness and injury, with a larger proportion of injuries occurring around the end of games [7]. Thus, it is essential to assess how weariness affects an athlete’s performance throughout a sporting event. While the effects of weariness have been extensively documented in the literature on sports science, very few studies have looked at how an actual game affects an athlete’s performance. Neuromuscular exhaustion is one variable that may be altered to potentially affect the risk of lower limb injury. [8-10]. Two types of exhaustion come from exercise that causes a decrease in muscular power, environmental fatigue and central fatigue [11]. Physiological mechanisms suggest that external weariness primarily arises in metabolic systems following neuromuscular junctions, whereas central exhaustion develops in the neurological system before neuromuscular junctions [11]. These pathways modify neuromuscular regulation and reduce muscle’s capacity to produce appropriate function [12]. Numerous fatigue procedures have been utilized in previous research. Protocols for mitigating environmental fatigue focus on certain muscles and are brief [8, 13]. Conversely, long-term central fatigue protocols target the cardiovascular system as well as motor control. They involve agility workouts that mimic more realistic sports postures, such as jumping and running on a treadmill [8, 10, 11] can be applied to evaluate basketball players’ weariness. These guidelines resemble basketball rules in part [14, 15]. Throughout a basketball game, they comprise alternating workouts that incorporate running, jumping, direction changes, running, and recuperation [15]. A study using a basketball practice simulation protocol observed decreased quadriceps muscle strength, which significantly affected jump performance and sprint speed [15]. Furthermore, athletes’ landing biomechanics were adversely affected by the use of an intermittent workout routine that replicates the demands of a ninety-minute football game [16, 17]. The literature on sports science suggests trustworthy methods available for accurately modeling particular sports tasks. Nonetheless, most research employs techniques within synthetic laboratory settings, which differ significantly from actual gaming environments. Therefore, conducting a field study in a basketball training session would be interesting. Since landing jumps are frequent in basketball and knee valgus angle and fatigue are major risk factors for ACL injury, basketball players with DKV patterns following fatigue application need to be conscious of the differential valgus angle between their dominant and non-dominant foot. Therefore, the present study was conducted to examine the valgus angles of both the dominant and non-dominant feet in basketball players exhibiting DKV patterns after the application of fatigue. 

Materials and Methods
The study’s statistical population included male basketball players in the semi-professional league from Kermanshah Province, Iran who were between the ages of 16 and 26 years and had a DKV angle of more than 8 degrees. With the aid of G*Power software, version 3.1 and findings from a prior study [18], the sample size for the current investigation was established. Based on this, the software calculated a sample size of 27 people with a test power of 80% and a confidence level of 0.95. Considering the potential dropout of participants, three additional individuals were included in each group beyond the sample size calculated by the software. Before the commencement of the study, participants completed medical and sports information questionnaires, and a consent form was obtained. Before the study, we conducted a briefing session to provide participants with sufficient information about the research and to assure its safety. The inclusion criteria included participants who were men and within the age range of 16 to 26 years and had DKV defects. They had engaged in basketball training for a minimum of two years and three times a week. The exclusion criteria included those who had experienced injury or surgery, significant cardiovascular, respiratory, or neurological disorders in the past six months, or had structural valgus or varus.
Anthropometric measurements, including height, weight, shoulder width, hip width, leg length, medial-lateral knee joint distance, external and internal malleolus distance, and Q angle, were obtained using a digital stadiometer (Inbody BSM 170, Japan), a smart scale (Mi-Smart-Scale2, China), a caliper (Mitutoyo, Japan), and a goniometer [19]. We placed reflex markers between the first and second metatarsal joints (figure 1), on the tibial tuberosity (Figure 2), and the anterior superior iliac spine (Figure 3) on both sides [19].
The tuck jump assessment (TJA) was utilized for initial screening to identify DKV patterns [20]. To do the TJA, participants had to stand with their feet shoulder-width apart and jump vertically, trying to lift their knees as high as possible. At the apex of the jump, the thighs should be parallel to the floor. Participants were told to start the following tuck leap after landing. Under the examiner’s supervision, this test was run for 10 s [19]. After meeting the criteria to enter the assessment, participants were then prepared for further evaluations. Their angle of incidence should be more than 8 degrees [21]. After the selection of eligible players, each one was prepared at the sports facility’s dedicated basketball court at 5:00 PM with prior coordination. Angles were recorded using a camera and a filming tripod. The camera tripod was adjusted relative to the subject’s height and in a frontal view [22]. After selecting eligible participants, each was instructed to stand in the designated area and perform three countermovement jumps [23]. The average of the three jumps was recorded using a camera (Nikon d300). Subsequently, the knee valgus angle was measured using KINOVEA software, version 0.9.5. In this study, the basketball fatigue protocol lasted for 40 minutes [24]. During the game, only athletes can drink water to achieve the game’s control over food and fluid intake. The participants received an assessment after the game using the Borg rating of perceived exertion (RPE) scale to measure their mental fatigue [24]. All rest periods during the game adhered to those in regular gameplay. After the game, immediately to reduce the fatigue effect, the participant performed three counter-movement jumps again to measure the knee angles. A digital camera recorded the angles and analyzed them using KINOVEA software.
An angle was drawn using three points for the knee joint (anterior superior iliac spine, middle of the patella, and second metatarsal head) to examine the moment of initial contact (Figure 4) and the deepest knee flexion (Figure 5) in the frontal plane during landing. The first frame from the start of the movement was chosen. The final landing frame was then chosen, just like in the previous step, and an angle was once more drawn based on the Figure. To obtain the initial contact and deepest knee flexion angles in the frontal plane, the angle of the knee joint at the time of takeoff (first frame of the jump phase) was finally subtracted from the angle of the knee joint at the time of landing (last frame of the landing phase at initial contact and maximum flexion) [25]. 

Statistical methods
The study employed descriptive and inferential statistics to examine the unprocessed data obtained. Descriptive statistics, such as Mean±SD, were used to describe the person’s dependent variables and demographic characteristics. We used the Shapiro-WILK test to determine the normality of data distribution. For every movement task, we utilized paired t-ests to see if the data had a normal distribution. Ultimately, the study’s raw data were compiled into an Excel spreadsheet and subjected to analysis using IBM Corp.’s SPSS software, version 23 (Armonk, NY, USA). Notably, this study’s significance level was established at 95% with an alpha threshold of 0.05 or below.

Results
Table 1 lists the Mean±SD of the participant’s demographic characteristics, including age, weight, height, body mass index (BMI), shoulder width, hip width, anterior superior iliac spines (ASIS) distance, knee condyle width, ankle width, lower limb length, Q angle and Borg RPE.


Table 2 presents descriptive statistics, including the Mean±SD, for the research variables.



Discussion 
This study was conducted to compare dominant and non-dominant knee valgus angles in basketball players with DKV deficiency after fatigue. According to the study, basketball players had a larger dominant knee valgus angle than their non-dominant knee valgus angle, but the functional loss brought on by tiredness did not increase the knee valgus angle. A related study conducted by Ford et al. involved measuring the amount of knee valgus movement and the varus-valgus angle during vertical jumps among 81 basketball players (47 females and 34 males). The method employed was to have individuals jump from a box and immediately jump upwards. After analyzing the landing phase, it was determined that the valgus angle differs significantly between the dominant and non-dominant leg [26]. Similarly, Brophy et al found that fatigue did not affect the mechanisms of the ACL [27]. The fatigue did not affect the knee angle in the current study due to the results of simulated exercises with an actual game. Additionally, other factors, such as players’ readiness level and sports discipline, also play a significant role in this matter. Ilaghi-Hosseini et al. found a substantial variation in the knee angle changes between the non-dominant and dominant legs, which is consistent with the results of this investigation [28]. Additionally, Ludwig et al reported a significant difference in the kinematics of the dominant and non-dominant legs during single-leg landing in football players after researching 66 professional players and 48 amateur players [29]. They reported greater stability during single-leg landing in the non-dominant leg compared to the dominant leg. In another study consistent with the results of the present study, Izawa et al. reported that gender and dominant/non-dominant legs significantly affect shock absorption capacity during landing. Moreover, vertical ground reaction force and internal ground reaction force, which are risky factors for knee injury during landing, are higher in women than men and in the non-dominant leg compared to the dominant leg [30]. Consistent with the results of the current investigation, Bila et al. concluded that cutting and landing motions on the non-dominant leg differ from those on the dominant leg [31] . Research indicated a higher likelihood of knee injury and increased valgus during the end of the game compared to before the game starts; for example, in a study that contradicts the present research, chappell et al concluded that both groups of men and women experienced increased knee valgus movements during landing after fatigue [6]. The reason for this difference in results between the present study and Chapelle’s study can be players’ readiness level because in the present study, semi-professional players participating in the country’s first division league were involved and with up-to-date training, they may have achieved better compatibility with basketball skills. The results of the present study contradict the research of Murphy and Dickin (2015) because they stated in their separate study that fatigue can increase the likelihood of ACL injury during landing by altering kinematic variables [32, 33]. However, in the present study, a 40-minute basketball game did not result in a change in knee valgus angle, which could be caused by the exhaustion protocol that was employed. 

Conclusion
The study’s results demonstrated that basketball players’ dominant and non-dominant legs have different knee valgus angles, with the dominant limb having a larger knee valgus angle than the non-dominant leg. Also, the results showed that fatigue does not raise the players’ knee valgus angle. The semi-professional players taking part in the country’s first division league, who were involved in the present study, may have improved their compatibility with basketball skills through up-to-date training due to their readiness level. By obtaining the results, it is possible to better understand the kinetic indicators in dangerous situations, which can help athletes learn the proper techniques to use in such dangerous situations.

Limitations
Lack of control over daily physical activity and routine life activities. Lack of control over the psychological and motivational state of the participants. The intensity of occupational work and the level of daily activity. Lack of control over individual differences and genetic factors of the participants.

Ethical Considerations
Compliance with ethical guidelines
This research received approval from the Ethics Committee at the University of Guilan (Code: IR.GUILAN.REC.1402.083).

Funding
This paper is derived from the master’s thesis of Rashin Asadpour’s, which was endorsed by the Department of Corrective Exercise and Sport Injuries, Faculty of Physical Education and Sport Science, the University of Guilan.

Authors' contributions
All authors equally contribute to preparing all parts of the research.

Conflict of interest
The authors declared no conflict of interest.

Acknowledgments
The authors express their gratitude to all participants involved in this study.


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