Sports Injury Risk Research Articles

Effects of a preparatory training protocol on the movement and body stability of handball players

Handball is a sport with a high risk of injury, The prevalence of injury is only obtainable through a thorough and comprehensive analysis. One of the most commonly used test batteries for interpreting the characteristics of an injury is the Functional Movement Screen (FMSTM). It makes possible to possible to identify movement limitations and asymmetries which are believed to impact injury risk in sports. The aim of this study is to use the FMSTM to determine whether an eight-week training protocol can predict and prevent injuries in handball. The study sample comprised the fifteen members of the Borac handball club youth team. The initial measurement showed that most players (80%) had an overall score in the test battery ranging from 15 to 20 points. In addition, three players were found to have asymmetry. Only one of the three players had an overall score in the FMSTM of ≤14. The participants scored the lowest in the initial measurement for Rotary Stability, followed by Deep Squat and Hurdle Step Left. They scored the highest in the Shoulder Mobility. After the implementation of the exercise protocol for improving body mobility and stability, the final measurements showed that all the participants had an overall score in the FMSTM of >14. The value of eta squared showed that training in between the two measurements had a significant impact. At the time of testing and protocol implementation no players sustained any injuries during matches or in training. This study confirmed that the FMSTM can be used to predict injuries in sports.

Lower Extremity Flexibility Profile in Basketball Players: Gender Differences and Injury Risk Identification.

Analysis of the flexibility profile of basketball players (BPs) can reveal differences in range of motion (ROM) by gender and also identify those players who are at higher risk for sports injuries. A descriptive observational study was conducted to determine the lower extremity flexibility profile of sixty-four basketball players and gender differences to identify players at higher risk of injury due to limited and asymmetric ROM in one or more movements. Participants: Sixty-four (33 male and 31 female) competitive athletes from the national leagues of the Spanish basketball league system participated in the present study (power of sample size ≥0.99). The eight passive ROM tests of the hip, knee and ankle were assessed using the ROM-SPORT battery. Each player completed a questionnaire on age, basic anthropometric data, dominant extremities, and training and sport-related variables. The lower extremity flexibility profile was established at 15° and 10° hip extension (HE), 39° and 38° ankle dorsiflexion with knee extended (ADF-KE), 40° and 39° ankle dorsiflexion with knee flexed (ADF-KF), 43° and 43° hip abduction (HAB), 75° and 61° hip abduction with the hip flexed (HAB-HF), 78° and 83° hip flexion with the knee extended (HF-KE), 134° and 120° knee flexion (KF), and 145° and 144° hip flexion (HF) by male and female basketball players, respectively. Sex differences in HE, HAB-HF, and KF were observed in BPs (p ≤ 0.01; Hedges’ g ≥ 1.04). Players reported limited ROM in ADF-KF, HE, HAB-HF, HF-KE, and KF; and asymmetric ROM mainly in HE, ADF-KE, KF, ADF-KF, and HF-KE. In conclusion, this study provides gender-specific lower extremity flexibility profile scores in BPs that can help athletic trainers and athletic and conditioning trainers to identify those players who are at higher risk of injury due to abnormal ROM scores.

Candidate genes associated with athletes' skeletal muscle functions regulation

It is generally recognized that an elite athlete's status is a multifactorial phenotype depending on many environmental and genetic factors. Variations in the sequence of nucleotides in deoxyribonucleic acid (DNA), in particular, single-nucleotide variants (SNVs) act as key internal factors associated with achieving high results in sports. The determination of specific individuals' genetic characteristics allows us to identify athletes who have the greatest genetically determined potential for certain sports that require speed, strength or endurance manifestation. Of course, peculiarities of the structure and function of skeletal muscles are among the most important characteristics in sports results context, in sports associated with the development of power / strength or endurance phenotypes. The composition and function of skeletal muscles are controlled by many different genes, and their SNVs can serve as strength or endurance athletes' status biomarkers. (1) Background: to conduct a thematic review of candidate genes studies and their single-nucleotide variants (SNVs) associated with the functioning of skeletal muscles in athletes. (2) Methods: A search for articles for the period from 2010 to 2020 was conducted in the databases SCOPUS, Web of Science, Google Calendar, Clinical keys, PubMed, e-LIBRARY using keywords and their combinations; (3) Conclusions: The identification of genetic biomarkers associated with muscular system regulation can help neurologists, sports doctors and coaches in developing personalized strategies for selecting children, adolescents and young adults for endurance, strength and speed sports (for example, running short, medium or long distances). Such a personalized approach will increase sports performance and reduce the risk of sports injuries of the musculoskeletal system.

Candidate Genes of Regulation of Skeletal Muscle Energy Metabolism in Athletes.

All biological processes associated with high sports performance, including energy metabolism, are influenced by genetics. DNA sequence variations in such genes, single nucleotide variants (SNVs), could confer genetic advantages that can be exploited to achieve optimal athletic performance. Ignorance of these features can create genetic “barriers” that prevent professional athletes from pursuing a career in sports. Predictive Genomic DNA Profiling reveals single nucleotide variations (SNV) that may be associated with better suitability for endurance, strength and speed sports. (1) Background: To conduct a research on candidate genes associated with regulation of skeletal muscle energy metabolism among athletes. (2) Methods: We have searched for articles in SCOPUS, Web of Science, Google Scholar, Clinical keys, PubMed, e-LIBRARY databases for the period of 2010–2020 using keywords and keywords combinations; (4) Conclusions: Identification of genetic markers associated with the regulation of energy metabolism in skeletal muscles can help sports physicians and coaches develop personalized strategies for selecting children, teenagers and young adults for endurance, strength and speed sports (such as jogging, middle or long distance runs). However, the multifactorial aspect of sport performances, including impact of genetics, epigenetics, environment (training and etc.), is important for personalized strategies for selecting of athletes. This approach could improve sports performance and reduce the risk of sports injuries to the musculoskeletal system.

Early Warning Model of Track and Field Sports Injury Based on RBF Neural Network Algorithm

Track and field events are the foundation of sports events. To learn other sports events well, you must also have a certain grasp of track and field events. As the movements of each track and field event become more and more complex, the competition becomes more and more intense, and the technical requirements are getting higher and higher. Therefore, sports injuries are gradually increasing. The prevention of sports injuries is a hot spot in sports medicine. Studies have found that as many as twenty factors can directly or indirectly cause track and field sports injuries. Based on the collected data on the injury-causing factors of elite athletes’ track and field sports, and on the basis of in-depth research on mobile data processing technology and early warning risks, a mobile early warning system and radial system using sports injury risk early warning algorithms, functional design and architecture are proposed. The basis function (RBF) neural network algorithm is released. Research on Early Warning Model of Sports Injury. This article analyzes and evaluates different hazard factors through the method of literature data, expert review and extensive analysis. In the investigation, the various causes of the injury were analyzed in detail, and it was found that most sports injuries were caused by students’ lack of attention, technical errors, insufficient preparation activities, etc., and finally proposed countermeasures to solve the problem, so that the sports injury situation was obtained improve.

Agility Score of Junior Soccer Athletes in Jakarta

Introduction: Agility score is important in setting the rehabilitation’s goal of injured athlete. More agility requirement needs more rehabilitation effort. The aim of this study is to measure the agility score of junior soccer athletes in Jakarta and compare the agility score based on athlete’s playing positions.
 Methods: Agility score of 65 junior athletes measured using T-Agility test. Subjects categorized based on playing positions which are defender, midfielder and forward. Mean agility score of each group was then compared. Data analyzed using descriptive analysis and one-way annova.
 Results: The mean agility score is 10.89 ± 0.47 seconds. The mean agility score in defender is 11.02 ± 0.51, midfielder 10.79 ± 0.41 and forward 10.73 ± 0.47. No significance statistical difference between the three groups (p=0,70).
 Conclusion: Agility score of junior soccer athletes in Jakarta using the T-Agility test is 10.89 ± 0.47 seconds and may illustrate the risk of sport injury. The forward soccer player has higher agility score compared to the midfielder and defender.

Attribute Reduction Algorithm Based Early Warning Model of Sports Injury

With the continuous development of track and field sports, a relatively complete and scientific system has been formed in terms of training, and more attention has been paid to the planned training of teenagers for many years, psychological training and recovery training, and strengthening medical supervision and scientific research. In order to speed up the scientific training of track and field and improve the level of track and field training in China, in view of the characteristics of track and field injuries such as multiple causes and complexity, this paper analyzes and studies the attribute reduction algorithm based on attribute reduction algorithm, and proposes a sports injury early warning model based on mutual information. Taking the reduction results as input neurons, a BP neural network with hidden layer is established in MATLAB environment. The results show that: call 85% of the track and field athletes in the sample information as training samples for training. After the simulation experiment of the model, it is found that the error value of the prediction results is basically controlled in the range of-0.025-0.05, which meets the requirements of early warning accuracy of injury risk level of athletes, and the accuracy rate of early warning of sports injury risk reaches 100%.

Big Data and Deep Learning Model for FMS Score Prediction of Aerobics Athletes

In recent years, competitive aerobics has developed rapidly in my country, and the corresponding sports injury risks have gradually increased. A number of studies have shown that due to the characteristics of aerobics itself, difficult movement requirements, fast-paced music accompaniment and coherent coordinated movements, athletes will suffer sports injuries if they are not paying attention. Therefore, discovering the causes of athletes’ injuries in time and preventing them in time is crucial for improving athletes’ skill level and prolonging sports life. Through the functional movement screening (FMS) test, understanding young aerobics athletes’ insufficiency in trunk stability, joint flexibility, muscle extension, and core strength can further help athletes reduce the risk of sports injuries. Therefore, this article proposes a novel sports injury risk model based on big data technology and deep learning, which can effectively predict the risk of sports injury and can play a positive role in improving the quality of athletes’ movements and prolonging their sports life.

Chronic Sequelae After Muscle Strain Injuries: Influence of Heavy Resistance Training on Functional and Structural Characteristics in a Randomized Controlled Trial

Background: Muscle strain injury leads to a high risk of recurrent injury in sports and can cause long-term symptoms such as weakness and pain. Scar tissue formation after strain injuries has been described, yet what ultrastructural changes might occur in the chronic phase of this injury have not. It is also unknown if persistent symptoms and morphological abnormalities of the tissue can be mitigated by strength training. Purpose: To investigate if heavy resistance training improves symptoms and structural abnormalities after strain injuries. Study Design: Randomized controlled trial; Level of evidence, 1. Methods: A total of 30 participants with long-term weakness and/or pain after a strain injury of the thigh or calf muscles were randomized to eccentric heavy resistance training of the injured region or control exercises of the back and abdominal muscle. Isokinetic (hamstring) or isometric (calf) muscle strength was determined, muscle cross-sectional area measured, and pain and function evaluated. Scar tissue ultrastructure was determined from biopsy specimens taken from the injured area before and after the training intervention. Results: Heavy resistance training over 3 months improved pain and function, normalized muscle strength deficits, and increased muscle cross-sectional area in the previously injured region. No systematic effect of training was found upon pathologic infiltration of fat and blood vessels into the previously injured area. Control exercises had no effect on strength, cross-sectional area, or scar tissue but a positive effect on patient-related outcome measures, such as pain and functional scores. Conclusion: Short-term strength training can improve sequelae symptoms and optimize muscle function even many years after a strain injury, but it does not seem to influence the overall structural abnormalities of the area with scar tissue. Registration: NCT02152098 (ClinicalTrials.gov identifier).

Early Warning System of Tennis Sports Injury Risk Based on Mobile Computing

In recent years, sports injuries in professional tennis players have gradually increased and sports injuries will break the sports training system and affect the long-term growth of new tennis players. Avoiding athlete injuries has become an important factor in improving training quality and game performance and ensuring the sustainable development of young tennis players’ competitiveness. Therefore, this article will use the RBF neural network algorithm and cluster analysis method to establish a tennis sports injury risk early warning model and finally establish a tennis sports injury risk early warning system so that tennis players can reduce their injuries. In this article, we use the questionnaire survey method, expert interview method, mathematical statistics method, and logical analysis method to investigate and analyze the results of training injuries of Chinese tennis players and coaches. The experimental results in this article show that among 48 tennis players of different ages, who are participating in formal training and tennis competitions, 15 young tennis players have been injured more than 6 times, accounting for 31.2% of the total; 20 have been injured 3 to 6 times, accounting for 41.7% of the total; 9 of them have been injured several times, accounting for 18.8% of the total; and 4 have been injured, accounting for 8.3% of the total. After using the tennis sports injury risk warning system based on the algorithm of RBF neural network in mobile computing, the tennis sports injury rate has dropped to 5%. It can be seen that the system has high feasibility and practicability.

An investigation of the effects of sensation seeking and impulsivity on extreme sport participation and injury using path analysis

Objective Extreme sport participation and injury rates have increased in recent decades. This study aimed to investigate sub-dimensions of impulsivity and sensation seeking that contribute to participation and injury risk in extreme sports. Participants Data included cross-sectional survey responses from 7,109 college students (Mage = 19.68, SD = 2.31). Methods This study utilized path analysis to investigate sub-dimensions of sensation seeking and impulsivity as predictors of extreme sport participation and injury across 3 models. Results Results of the final model identify risk seeking and lack of perseverance as the two strongest predictors of extreme sports injury, risk seeking, experience seeking, and lack of premeditation as the strongest positive predictors of extreme sports participation, and lack of perseverance as the strongest negative predictor of extreme sports participation. Conclusions These results will contribute to targeted prevention and intervention efforts for extreme sports injury among young adults based on identified individual personality factors.

Prediction Model of Juvenile Football Players’ Sports Injury Based on Text Classification Technology of Machine Learning

As the level of soccer in our country has improved rapidly, the level of skill has gradually improved, and the requirements for training of athletes have increased. Due to changes in athlete training methods, it has been decided that athletes must bear a great risk of sports injuries. Accurate prediction of injuries is very important for the development of youth soccer. Based on this, this paper proposes a text classification algorithm based on machine learning and builds a sports injury prediction model that can accurately predict athlete injuries, reduce athlete injuries during training, and be effective. We put forward various sports suitable for young athletes, and put forward some measures to prevent and alleviate athletes’ injuries. This article selects 48 football players from a college of physical education of a university for testing. The athletes participating in the experiment use professional equipment to collect exercise volume and exercise load data, and real-time records of each athlete's physical fitness data within half a year, through the athlete's exercise volume, exercise load, body metabolism, and physical indicators to predict their sports injury. Experiments show that from the degree of injury, it can be seen that the severe injury is the least, with 5 cases of muscle injury, 2 cases of fascia ligament injury, and 1 case of joint injury. There were 25 cases of mild injuries, accounting for 41.0% of the total. This is because the athlete’s sports injury prediction model has better prediction capabilities, allowing athlete coaches and therapists to optimize training courses, ultimately preventing injuries, improving training levels, and reducing rehabilitation costs.

الخطر المؤمن منه فی التأمین على اللاعبین المحترفین ـ الریاضات القتالیة نموذجا ـ

في هذا البحث قمت بتطبيق القواعد العامة لعقد التأمين على صورة جديدة منه وهي التأمين على اللاعبين المحترفين من خطر الإصابات الرياضية، وفصلت القول في رکن الخطر لأنه محل هذا العقد فبينت المقصود بالإصابات الرياضية وما يشمله التأمين منها، ومدى توافر الضوابط القانونية للخطر المؤمن منه في عقد التأمين وفق نظريته العامة، ثم بينت أطراف العقد والتزاماتهم، وانتقلت بعد ذلک لمرحلة أخرى وهي تطبيق هذا النوع من التأمين على الرياضات القتالية کواحدة من الرياضات الأوليمبية التي تتم ممارستها في مصر لدى قطاع کبير من الرياضيين. In this research, I have applied the general rules of the insurance contract in a new form, namely, insurance on professional players against the risk of sports injuries. I have tackled in detail the element of danger, as it is the subject matter of this contract. Thus, I explained what is meant by sports injuries and what is covered by insurance. Moreover, the availability of legal controls for the insured risk in the insurance contract has been handled in accordance with its general theory, showing the contracting parties and their obligations. After that, I have tackled the application of this type of insurance to combat sports as one of the Olympic sports practiced in Egypt by a large number of athletes.

CONSTRUCTION OF INTELLIGENT CAMPUS TENNIS PLAYERS’ BODY DATA MONITORING AND INJURY WARNING SYSTEM BASED ON DATA FUSION

ABSTRACT As far as competitive sports are concerned, scientifically organizing the training process and strictly following the training rules are the only way to success. Tennis is the foundation of all sports. It develops physical and motor skills in a comprehensive and effective manner. Biomechanical diagnosis is mainly carried out on the basis of force-time curve recording of different movement modes, and the generated data is processed by computer through a special measuring device. The main purpose of this study is to use data fusion technology to analyze and evaluate the injury factors involved in various sports. Based on this, a dynamic chain model for early warning of sports injury risk factors is established, which can provide a reference for athletes to avoid and reduce the risk of injuries and ensure normal training and competition. In tennis training, scientific training monitoring is applied to diagnose and monitor athletes’ physical function and psychological state. Conduct humanized and scientific management of the training process.

Artificial intelligence and data processing in injury diagnosis and prevention in competitive sports: A literature review

Artificial intelligence (AI) application opens an exciting perspective for predicting injury risk and team sports performance. A better understanding of the techniques of AI employed and of the sports that are using AI is warranted. The purpose of this study is to identify which AI approaches have been applied to investigate sports performance and injury risk

Early Warning Model of Sports Injury Based on RBF Neural Network Algorithm

Sports injury is a common problem in athletes’ training. The sports injury assessment model is a physical method to determine the sports injury attributes of specific parts by predicting and evaluating the risk of sports injury. In this paper, we use a neural network to realize big data analysis of sports injury data. Big data network is a method of capturing Internet information by means of cloud computing, which is usually used in the construction of Wan and LAN. This paper analyzes the source of sports risk and the main injury factors, designs the sports injury estimation model based on big data analysis, establishes a new assessment model based on RBF neural network, and builds the big data network environment required for the model operation by improving the topological structure, combining big data and deep neural network. In the built environment, the risk assessment of sports injury can be completed by determining the risk source and identifying the risk factors. The realization of the model can be constrained by the uncertainty conditions so that it can achieve a good operation state.

Adiposity as a Risk Factor for Sport Injury in Youth: A Systematic Review.

To determine whether high or low adiposity is associated with youth sport-related injury. Ten electronic databases were searched to identify prospective studies examining the association between adiposity [body mass index (BMI) or body fat] and a future time-loss or medical attention sport-related musculoskeletal injury or concussion in youth aged 20 years and younger. Two independent raters assessed the quality (Downs and Black criteria) and risk of bias (Joanna Briggs Institute Critical Appraisal Tool). Random-effects meta-analyses were used to calculate pooled odds ratio [95% confidence interval (CI)] of injury. Of 11 424 potentially relevant records, 38 articles were included with 17 eligible for meta-analyses. In qualitative synthesis, no clear association was identified between adiposity and any sport injury; however, 16/22 studies identified high adiposity as a significant risk factor for lower-extremity injury. Meta-analyses revealed higher BMI in youth with any sport-related injury and lower BMI in youth who developed a bone stress injury (BSI) compared with noninjured controls. The pooled OR (95% CI) examining the association of BMI and injury risk (excluding bone injury) was 1.18 (95% CI: 1.03-1.34). A major source of bias in included articles was inconsistent adjustment for age, sex, and physical activity participation. Level 2b evidence suggests that high BMI is associated with greater risk of youth sport injury, particularly lower-extremity injury and excluding BSI or fracture. Although pooled mean differences were low, anthropometric risk of injury seems to be dependent on type and site of injury in youth sport.

RETRACTED: Computer-aided physical training sports injury risk simulation based on embedded image system

In teamwork, injuries occur from time to time. The athlete's body is very important, so the rich experience of teamwork can reduce the injury to the athlete, which is a very important aspect of preventing the risk and mitigating the risk. There are several ways to reduce the risk factors of injury. One is to use positioning method, scientists have developed a 2.4 GHz frequency wireless receiving sensor system, referred to as WSN. This system can effectively monitor indoor and outdoor environment and identify and analyze buried active RF in advance. After several frequency tests, more effective performance is obtained, and the transmission power and signal intensity system are developed. It can generate high-frequency amplitudes, and the transmitted data can create independent labels to receive. However, the difficulty in understanding the subtle differences between different statistical methods and making conclusions that lead to erroneous assumptions has been produced from the data. Therefore, the purpose of this study is to obtain the outline to determine the method used to determine the risk of damage, to predict the movement of injuries while developing the model and the existing evidence to highlight the difference between the association and the prediction associated with damaging it. Sports injuries risk factors research aimed to study the example of the method is evaluated using chopped strains. Injuries resulting from the complex interaction of several risk factors. Develop and adapt and, mental, emotional, social events. Therefore covers education system and the necessary physical activities organized by physical education. In order to create a rough picture of the whole creation and technique. The soap places to students who have used a solvent, is embedded in the grease.

A Systematic Review of Strength and Conditioning Protocols for Improving Neck Strength and Reducing Concussion Incidence and Impact Injury Risk in Collision Sports; Is There Evidence?

The objective of this systematic literature review was to evaluate the evidence regarding the development of neck strength in reducing concussion and cervical spine injuries in adult amateur and professional sport populations. PubMed, CINAHL, Science Direct, and Web of Science databases were searched systematically. The criteria for inclusion in the review were as follows: (1) a human adult (≥18 or above); (2) involved in amateur, semi-professional, or professional sports; (3) sports included involved collisions with other humans, apparatus or the environment; (4) interventions included pre- and post-neck muscle strength measures or neck stability measures; (5) outcomes included effects on increasing neck strength in participants and/or injury incidence. Database searches identified 2462 articles. Following title, abstract, and full paper screening, three papers were eligible for inclusion. All of the papers reported information from male participants, two were focused on rugby union, and one on American football. Two of the included studies found a significant improvement in isometric neck strength following intervention. None of the studies reported any impact of neck strengthening exercises on cervical spine injuries. This review has shown that there is currently a lack of evidence to support the use of neck strengthening interventions in reducing impact injury risk in adult populations who participate in sport.

The effect of fatigue on asymmetry between lower limbs in functional performances in elite child taekwondo athletes

BackgroundInter-limb asymmetry above a certain threshold in functional performance indicates increased injury risk in sports. Fatigue has been found to increase bilateral asymmetry in lower-limb jumping performance among high-school and adult athletes, whereas this impact has not been examined in child athletes. This study aimed to examine the effect of fatigue on inter-limb asymmetry in functional performances in elite Taekwondo athletes aged between 9 and 11 years.MethodsPerformance of single-leg jumps, Star Excursion Balance Test (SEBT), and muscle (hamstring and gastrocnemius) flexibility were measured for 13 elite male child Taekwondo athletes (aged 9.85 ± 0.80 years) at both the rested and fatigued states to examine the inter-limb asymmetry. A two-way repeated measures ANOVA was conducted to examine for difference and the interaction between limb (dominant, non-dominant leg) and state (rested, fatigued state) for each test. Paired t test or Wilcoxon signed-rank test was used to compare the asymmetry magnitude at the rested vs. fatigued state for each test, and the variation of performance post fatigue in the dominant vs. non-dominant leg when appropriate.ResultsThe inter-limb asymmetry in triple-hop distance significantly (p = 0.046) increased with fatigue, whereas the asymmetry significantly (p = 0.004) decreased with fatigue in anterior (ANT) reach distance in SEBT. A significant (p = 0.027) limb by state interaction was shown for posterolateral (PL) reach distance in SEBT, wherein a significant (p = 0.005) bilateral difference was only shown at the rested state. The PL reach distance showed a significantly greater decrease (p = 0.028) post fatigue when using the dominant leg for support compared to using the non-dominant leg.ConclusionsFatigue significantly impacts inter-limb asymmetry in jump performances and dynamic balance for child athletes, while the variation of inter-limb asymmetry post fatigue may be different across tests. For the purpose of injury prevention, practitioners should consider assessing the inter-limb asymmetry for children at both the rested and fatigued state and be mindful of the fatigue response of each leg in functional tests.