Investigation of the Effects of Sports Injuries on the Quality of Life of Adolescent Athletes

The aim of the study is to examine the effects of sports injuries experienced by adolescent athletes between the ages of 13 and 18 on their quality of life during training and competition. The universe of the study consists of licensed athletes between the ages of 13-18 who play sports at the Manisa Youth and Sports Provincial Directorate. In the study, purposeful sampling method was chosen, and the study sample consisted of 170 athletes (97 females, 73 males) with sports injuries and 90 athletes (49 females, 41 males) without sports injuries, totally 260 volunteers. The demographic information form developed by the researchers, the Adolescent Form of the Quality of Life for Children (PedsQL) developed by Varni et al. (1999), the Adolescent Form of the Age of 13-18, and the Causes of Injury in Sports Questionnaire prepared by Alkaabi (2015) were used as data collection methods. As a result, it was found that female athletes had more sports injuries in adolescent athletes than men. The number of athletes who suffered injuries in defense sports is higher than in other branches. As a result, it has been determined that sports injury negatively affects the quality of life. It was found as a result of the study data that they experienced more injuries in training than in competitions. Since the injury is mostly in the winter season and the athletes stated that they did not use protective equipment during the injury, the trainers should be informed, they should be thoughtful about taking the necessary precautions and precautions and should be offered the necessary supervision. Keywords: Adolescent athlete, injury, quality of life

Statistical Calculation Method and Analysis of Athletes’ Biorhythm State and Sports Injury

In recent years, attention has been focused on the prevention and treatment of sports injuries. However, no athlete injury prevention system has been established. Athletes’ injury prevention has become an important research field, but it is still used in current sports injury statistics. Traditional calculation methods are difficult to meet the requirements of modern halo. In order to understand the relationship between the athlete’s biorhythm state and the statistical calculation method of sports injury and to reduce the damage caused by sports injury to athletes, we have carried out statistics on various physical parameters of athletes in a certain university in this city through example analysis and creatively introduced the statistical calculation method of the fault tree, and the corresponding results provide a certain research foundation for the subsequent research. The research results in this article prove that formulating a suitable sport mode for athletes can improve their athletic ability by more than 10%, reduce damage to athletes, and extend their professional life. Generally speaking, athletes’ injuries can be reduced by more than 15%. This shows that appropriate injury statistics calculation methods and research on sports injury prevention in advance are extremely important for athletes.

The Relationshıp Between Injury Anxiety and Forgiveness Flexıbility: A Research for Elite Field Hockey Athletes

Purpose: This study aims to examine the relationship between injury anxiety and forgiveness flexibility levels of elite field hockey players. Injury anxiety and forgiveness flexibility levels were compared in terms of gender, the status of being a national athlete, injury history, and frequency of training. Method: In line with the purpose of the research, it is descriptive research in which the relational survey model, one of the quantitative research methods, is used. With this method, it is used to grasp and explain the relationship between various variables without any attempt to change the current situation. In this framework, a research model was created that aims to determine the existence of covariance between two or more variables. The research population consists of field hockey players. The population of the study is elite field hockey players who participated in national or international field hockey tournaments. The research sample consists of 161 participants (66 Female, 41%; 95% Male, 59%) selected by convenience sampling method, which is one of the non-random sampling methods. The “Sports Injury Anxiety” scale was used to determine the injury anxiety levels of field hockey players who participated in the study voluntarily. The “Forgiveness Flexibility Scale” was used to determine the levels of forgiveness flexibility, and the “Personal Information Form” prepared by the researchers was used to collect information about demographic characteristics (gender, status of being a national athlete, injury history, training frequency). Conclusion: It was determined that there was a significant difference in injury anxiety and forgiveness flexibility scores of elite field hockey players according to gender and training frequency variables. On the other hand, while there was a significant difference in injury anxiety scores according to the status of being a national athlete and having an injury, no significant difference was found in the mean scores of forgiveness flexibility. As a result of the analysis performed to determine the relationship between injury anxiety and forgiveness flexibility, a positive and significant relationship was determined between the injury anxiety and forgiveness flexibility levels of the athletes. Finally, it was concluded that training frequency is an important predictor of injury anxiety, and as the training frequency increases, the athlete's injury anxiety level decreases. Keywords: Field hockey, injury anxiety, forgiveness flexibility

Sports Medicine and Artificial Intelligence: A Primer

Artificial intelligence (AI) represents the fourth industrial revolution and the next frontier in medicine poised to transform the field of orthopaedics and sports medicine, though widespread understanding of the fundamental principles and adoption of applications remain nascent. Recent research efforts into implementation of AI in the field of orthopaedic surgery and sports medicine have demonstrated great promise in predicting athlete injury risk, interpreting advanced imaging, evaluating patient-reported outcomes, reporting value-based metrics, and augmenting the patient experience. Not unlike the recent emphasis thrust upon physicians to understand the business of medicine, the future practice of sports medicine specialists will require a fundamental working knowledge of the strengths, limitations, and applications of AI-based tools. With appreciation, caution, and experience applying AI to sports medicine, the potential to automate tasks and improve data-driven insights may be realized to fundamentally improve patient care. In this Current Concepts review, we discuss the definitions, strengths, limitations, and applications of AI from the current literature as it relates to orthopaedic sports medicine.

Intelligent Badminton Training Robot in Athlete Injury Prevention Under Machine Learning

This study was developed to explore the role of the intelligent badminton training robot (IBTR) to prevent badminton player injuries based on the machine learning algorithm. An IBTR is designed from the perspectives of hardware and software systems, and the movements of the athletes are recognized and analyzed with the hidden Markov model (HMM) under the machine learning. After the design was completed, it was simulated with the computer to analyze its performance. The results show that after the HMM is optimized, the recognition accuracy or data pre-processing algorithm, based on the sliding window segmentation at the moment of hitting reaches 96.03%, and the recognition rate of the improved HMM to the robot can be 94.5%, showing a good recognition effect on the training set samples. In addition, the accuracy rate is basically stable when the total size of the training data is 120 sets, after the accuracy of the robot is analyzed through different data set sizes. Therefore, it was found that the designed IBTR has a high recognition rate and stable accuracy, which can provide experimental references for injury prevention in athlete training.

The Evolution of Psychological Response to Athlete Injury Models for Professional Sport

Elite competitive sport is linked with a unique collection of stressors distinct from the general population. While there have been advancements in understanding the role that stressors play within the elite sporting environment, uncertainty still exists around a clear process for measuring stressors, and their specific relationship to injury. A number of models have been proposed as useful frameworks for investigating and describing the role of stress and its interaction with the psychological response to athletic injury. While these models provide evolving points of view drawing on different theoretical backgrounds regarding their interpretation of athletic stress and injury, they offer little application to the applied elite sporting environment, and no detail of how they these models support athletes, and high performance staff in the applied setting. This narrative review will present two popular theoretical psychological models of sports injury rehabilitation. We argue that these models could be better applied in the current sporting environment if they utilized biological markers such as cortisol measures of personality. Extending from the Biopsychosocial model of injury, we present an updated model of injury quantifying the psychophysiological response for athletes [1]. This model is aligned with the current applied sporting landscape, incorporating the implementation of measurement practice guidelines, and offering high-performance staff an example that can be applied to their unique setting by assessing individuals' distinct measures of cortisol and personality in response to stress and injury.

Staffing, Financial, and Administrative Oversight Models and Rates of Injury in Collegiate Athletes

Context Structural features of health care environments are associated with patient health outcomes, but these relationships are not well understood in sports medicine. Objective To evaluate the association between athlete injury outcomes and structural measures of health care at universities: (1) clinicians per athlete, (2) financial model of the sports medicine department, and (3) administrative reporting structure of the sports medicine department. Design Descriptive epidemiology study. Setting Collegiate sports medicine programs. Patients or Other Participants Colleges that contribute data to the National Collegiate Athletic Association (NCAA) Injury Surveillance Program. Main Outcome Measure(s) We combined injury data from the NCAA Injury Surveillance Program, sports medicine staffing data from NCAA Research, athletic department characteristics from the United States Department of Education, and financial and administrative oversight model data from a previous survey. Rates of injury, reinjury, concussion, and time loss (days) in NCAA athletes. Results Compared with schools that had an average number of clinicians per athlete, schools 1 standard deviation above average had a 9.5% lower injury incidence (103.6 versus 93.7 per 10000 athlete-exposures [AEs]; incidence rate ratio [IRR] = 0.905, P < .001), 2.7% lower incidence of reinjury (10.6 versus 10.3 per 10000 AEs; IRR = 0.973, P = .004), and 6.7% lower incidence of concussion (6.1 versus 5.7 per 10000 AEs; IRR = 0.933, P < .001). Compared with the average, schools that had 1 standard deviation more clinicians per athlete had 16% greater injury time loss (5.0 days versus 4.2 days; IRR = 1.16, P < .001). At schools with sports medicine departments financed by or reporting to the athletics department (or both), athletes had higher injury incidences (31% and 9%, respectively). Conclusions The financial and reporting structures of collegiate sports medicine departments as well as the number of clinicians per athlete were associated with injury risk. Increasing the number of sports medicine clinicians on staff and structuring sports medicine departments such that they are financed by and report to a medical institution may reduce athlete injury incidence.

INFLUENCE OF PREVENTIVE TRAINING PROGRAM FEEDBACK COMPLEXITY ON ATHLETE INJURY RISK

Background: Preventive training programs (PTPs) reduce injury risk in youth athletes. Corrective verbal feedback is an integral component of PTP implementation; however, too many cues delivered at once may be too complicated for youth athletes. PTPs with simplified cues may be more effective as traditional PTPs to improve athlete injury risk. Hypothesis/Purpose: The purpose of this study was to evaluate changes in movements associated with injury risk youth athletes participating in a season-long PTP: with simplified feedback cues, with traditional feedback cues, or in a warm-up of the coaches’ choosing. Methods: A cluster-randomized controlled trial was utilized. Youth soccer teams were randomized into: simplified or traditional PTP, or the control group. Simplified and traditional PTPs were the same duration (10-15 minutes) with the same exercises, but the simplified PTP only provided sagittal plane feedback cues (e.g., “get low”) and the traditional PTP provided feedback in all planes of motion (e.g., “don’t let your knees cave inward”). Teams in the control group performed their coach’s warm-up. Participants completed two test sessions (PRE-season, POST-season) with three trials of a jump-landing task evaluated using the Landing Error Scoring System (LESS). The LESS is a valid and reliable clinical movement assessment. Participants jumped off a 30-cm high box to a distance half their height and immediately rebounded straight in the air for maximum vertical height. A single, reliable rater graded all trials. Three trials were averaged together for one composite LESS score at each time point. A generalized linear model was used to evaluate differences in composite LESS score between warm-up (Simplified, Traditional, Control) over time (PRE, POST) while controlling for team. All data were analyzed using SPSS Version 21.0 (p<0.05). Results: There were no significant differences between Simplified and Traditional PTPs (P>0.05) so the PTPs were combined into a single group (Intervention) and compared to Control from PRE to POST. Four-hundred and twenty athletes (Intervention n=291 athletes, Control n=129 athletes) participated. The Intervention group improved LESS scores ((Mean±SE [95% CI]) Intervention PRE:6.32±0.17 [5.99,6.67], Intervention POST: 5.36±0.16 [5.05,5.69], Control PRE: 6.58±0.35 [5.94,7.30], Control POST: 6.09±0.34 [5.47,6.79])(P=.04). Conclusion: Regardless of PTP, athletes improved movement technique. These findings suggest that simplified corrective feedback is as effective as more complex feedback. Future studies should look to train coaches to focus on simplified cues and evaluate impact on PTP implementation.

The Concept of Sport Sampling Versus Sport Specialization: Preventing Youth Athlete Injury: A Systematic Review and Meta-analysis

Background: The prevalence of youth athletes specializing in 1 sport has been increasing over the past decade. Subsequently, the rate of youth athlete injury has also been increasing. It is possible that an association exists between youth specialization and sports injury rate. Purpose: To determine if sport sampling is associated with a lower sports injury rate in youths compared with youths who specialize in 1 sport. Study Design: Systematic review and meta-analysis. Methods: A systematic review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines using PubMed, Embase, and the Cochrane library. Inclusion criteria included studies written in the English language, studies with athletes between 7 and 18 years of age, studies that report injury rates, and studies that specify if athletes were sport samplers or specialized in a sport. Data relevant to this study, including injuries and patient characteristics, were extracted and statistically analyzed. Results: The initial search identified 324 studies, 6 of which met inclusion criteria. From these 6 studies, the total participant number was 5736. Of those, 2451 (42.7%) were “sport samplers,” 1628 (28.4%) were “sport specializers,” and 1657 (28.9%) were considered “others” (ie, could not be classified as true samplers or true specializers). The average age of all the athletes was 14.6 years (range, 7-18 years). Sport specializers had a significantly higher injury risk than the sport samplers (RR, 1.37; 95% CI, 1.19-1.57; P < .0001). There was a higher risk of injury in the “others” group when compared with the “sport sampler” group (RR, 1.21; 95% CI, 1.14-1.29; P < .0001). There was a higher risk of injury in the “sport specializer” group over the “others” group (RR, 1.09; 95% CI, 1.04-1.14; P < .005). Conclusion: Sport sampling is associated with a decreased risk of sports injury in youth athletes when compared with those who specialize in 1 sport. Injury rates increase as a youth athlete becomes increasingly specialized. Youth athletes would benefit substantially from participating in sport sampling.