Athletic Injury Prevention Research Articles

Proprioception Testing in Junior Athletes Training using the “Gyko” Sensor

Proprioceptive training is crucial for enhancing coordination, balance, and injury prevention in athletes, especially in their developmental stages. This study investigates the application of the Gyko sensor for testing proprioceptive abilities in junior athletes. The Gyko sensor, a portable motion analysis device, provides real-time data on movement and postural control. The study assesses the effectiveness of proprioceptive training by analyzing parameters such as balance, stability, and joint positioning accuracy in young athletes. Results highlight the sensor's potential in monitoring improvements and tailoring individualized training programs to optimize athletic performance and reduce injury risk.

Towards Achilles Tendon Injury Prevention in Athletes with Structural MRI Biomarkers: A Machine Learning Approach.

Recent advancements in artificial intelligence have proven their effectiveness in orthopaedic settings, especially in tasks like medical image analysis. This study compares human musculoskeletal radiologists to artificial intelligence in a novel, detailed, short, and cost-effective examination of Achilles tendon magnetic resonance images to uncover potential disparities in their reasoning approaches. Aiming to identify relationships between the structured assessment of the Achilles tendon and its function that could support injury prevention. We examined 72 athletes to investigate the link between Achilles tendon structure, as visualised in magnetic resonance images using a precise T2*-weighted gradient echo sequence with very short echo times, and its functional attributes. The acquired data were analysed using advanced artificial intelligence techniques and reviewed by radiologists. Additionally, we conducted statistical assessments to explore relationships with functional studies in four meaningful groups: dynamic strength, range of motion, muscle torque and stabilography. The results show notable linear or non-linear relationships between functional indicators and structural alterations (maximal obtained Spearman correlation coefficients ranged from 0.3 to 0.36 for radiological assessment and from 0.33 to 0.49 for artificial intelligence assessment, while maximal normalised mutual information ranged from 0.52 to 0.57 for radiological assessment and from 0.42 to 0.6 for artificial intelligence assessment). Moreover, when artificial intelligence-based magnetic resonance assessment was utilised as an input, the associations consistently proved more robust, or the count of significant relationships surpassed that derived from radiological assessment. Ultimately, utilising only structural parameters as inputs enabled us to explain up to 59% of the variance within specific functional groups. This analysis revealed that structural parameters influence four key functional aspects related to the Achilles tendon. Furthermore, we found that relying solely on subjective radiologist opinions limited our ability to reason effectively, in contrast to the structured artificial intelligence assessment. Cross-sectional studies.

OPTIMISING EFFORT CAPACITY IN TRIATHLON

Background. My personal triathlon practice inspired the creation of a specialized training regimen to improve triathlon training in Romania. This program aims to enhance performance, well-being, and injury prevention for athletes. At the INTERDISCIPLINARY RESEARCH CENTRE (CCI) “Dr. Alexandru Partheniu” of UNEFS, we used AEROSCAN technology to assess exercise capacity, measuring heart and breathing rates with the Bruce Protocol for precise physiological monitoring. Objectives. This research demonstrates that tailored training cycles for swimming, cycling, and running can significantly enhance triathlon performance. The goal was to create a customizable, comprehensive training program to maximize the overall training process and provide a scientific foundation for triathlon training in Romania. This research aims to demonstrate that tailored training cycles can significantly enhance triathlon performance. Methods. We used observation, experimental, statistical-mathematical, and bibliographic study methods to interpret the experiment's outcomes. The bibliographic study provided the theoretical basis, while observation and experimental methods enabled real-time data collection. Statistical analysis was crucial for interpreting the data. Results. Using AEROSCAN and the Polar system, we evaluated exercise capacity and tracked heart rate. The Bruce protocol was applied for both initial and final tests. The data showed significant improvements in endurance, speed, and overall performance, supported by statistical analysis, after implementing the customized training program. Conclusion: Our research indicates that tailored athletic training regimens for each triathlon discipline can significantly enhance performance and promote athletes' health and longevity. The results highlight the importance of individualized training plans and suggest potential improvements in Romanian triathlon training practices. Individualized training programs lead to notable improvements in performance and athlete well-being.

Ensuring athlete physical fitness using Cyber-Physical Systems (CPS) in training environments.

Sports have been a fundamental component of any culture and legacy for centuries. Athletes are widely regarded as a source of national pride, and their physical well-being is deemed to be of paramount significance. The attainment of optimal performance and injury prevention in athletes is contingent upon physical fitness. Technology integration has implemented Cyber-Physical Systems (CPS) to augment the athletic training milieu. The present study introduces an approach for assessing athlete physical fitness in training environments: the Internet of Things (IoT) and CPS-based Physical Fitness Evaluation Method (IoT-CPS-PFEM). The IoT-CPS-PFEM employs a range of IoT-connected sensors and devices to observe and assess the physical fitness of athletes. The proposed methodology gathers information on diverse fitness parameters, including heart rate, body temperature, and oxygen saturation. It employs machine learning algorithms to scrutinize and furnish feedback on the athlete's physical fitness status. The simulation findings illustrate the efficacy of the proposed IoT-CPS-PFEM in identifying the physical fitness levels of athletes, with an average precision of 93%. The method under consideration aims to tackle the existing obstacles of conventional physical fitness assessment techniques, including imprecisions, time lags, and manual data-gathering requirements. The approach of IoT-CPS-PFEM provides the benefits of real-time monitoring, precision, and automation, thereby enhancing an athlete's physical fitness and overall performance to a considerable extent. The research findings suggest that the implementation of IoT-CPS-PFEM can significantly impact the physical fitness of athletes and enhance the performance of the Indian sports industry in global competitions.

Graphene Aerogel‐Based Pressure Sensors with Gradient Structure for Subtle Physiological Signals and Motion Monitoring

AbstractHigh‐performance flexible wearable pressure sensors require the simple preparation method coupled with a well‐designed structure. Graphene aerogel has garnered significant attention as a flexible pressure sensor due to its exceptional features such as high porosity, favorable compressibility, and fine conductivity. However, it remains challenging to precisely control the 3D structure of the graphene aerogel within the sensors, which limits the detection sensitivity and application range. Here, a freeze‐assisted transfer printing strategy (FATPS) is proposed for fabricating a high‐performance graphene aerogel‐based sensor with the gradient structures. Compared to sensors prepared using mold methods, the sensitivity of the sensor produced through FATPS can be enhanced by nearly tenfold. Furthermore, the gradient structure empowers the graphene pressure sensor with excellent reliability and rapid response time (50 ms) within the pressure range of 0.1–50 kPa. The sensor exhibits the capability to monitor and recognize various physiological activities, including pulse, vocalization, coughing, swallowing, and finger bending. It is expected that the sensor has remarkable potential applications in medical diagnosis, disability aid, and athletic injury prevention and so on.

Lack of utilizing evidence based training protocols for fitness training and injury prevention in athletes: The need to promote sports physical therapy in Pakistan

Lack of utilizing evidence based training protocols for fitness training and injury prevention in athletes: The need to promote sports physical therapy in Pakistan

The Influence of Menstrual Cycle Phases on Maximal Strength Performance in Healthy Female Adults: A Systematic Review with Meta-Analysis.

Maximal strength is a significant factor in achieving peak performance and injury prevention in athletes. In individualization strategies for the efficient development of athletes, it is necessary to consider the respective components separately. The purpose of this study was to systematically examine the effects of the different cycle phases on isometric, isokinetic, and dynamic maximum strength. A systematic literature review was conducted; databases were searched from January 1960 to September 2023. The included studies focused on the expression of maximal strength in the earlier follicular phase as well as at least one comparative phase. Of the initial 707 articles identified, 22 met the selection criteria and were included. The studies considered a total of 433 subjects. Our results revealed medium effects (weighted mean standardized mean difference (SMD) = 0.60; seven studies) for isometric maximal strength in favor of the late follicular phase, small effects (weighted mean SMD = 0.39; five studies) for isokinetic maximal strength in favor of the ovulation phase, and small effects (weighted mean SMD = 0.14; three studies) for dynamic maximal strength in favor of the late follicular phase. The results indicate that the early follicular phase is unfavorable for all strength classes. Peak performance in isometric strength is seen in the late follicular phase, whereas isokinetic strength peaks during ovulation. Dynamic strength is optimal in the late follicular phase.

Novel Study for the Early Identification of Injury Risks in Athletes Using Machine Learning Techniques

This innovative study addresses the prevalent issue of sports injuries, particularly focusing on ankle injuries, utilizing advanced analytical tools such as artificial intelligence (AI) and machine learning (ML). Employing a logistic regression model, the research achieves a remarkable accuracy of 90.0%, providing a robust predictive tool for identifying and classifying athletes with injuries. The comprehensive evaluation of performance metrics, including recall, precision, and F1-Score, emphasizes the model’s reliability. Key determinants like practicing sports with injury risk and kinesiophobia reveal significant associations, offering vital insights for early risk detection and personalized preventive strategies. The study’s contribution extends beyond predictive modeling, incorporating a predictive factors analysis that sheds light on the nuanced relationships between various predictors and the occurrence of injuries. In essence, this research not only advances our understanding of sports injuries but also presents a potent tool with practical implications for injury prevention in athletes, bridging the gap between data-driven insights and actionable strategies.

Predictive modeling for injury prevention in athletes using artificial intelligence

Predictive modeling for injury prevention in athletes using artificial intelligence

From data to action: a scoping review of wearable technologies and biomechanical assessments informing injury prevention strategies in sport

BackgroundThe purpose of this scoping review was to evaluate the current use of technologies in sports settings for training adaptation and injury prevention. The review aimed to map the existing literature, identify key concepts and themes, and highlight gaps in research, thus offering guidance for future studies.MethodsThis study followed the guidelines of the PRISMA extension for scoping reviews and a search in four major databases was conducted.ResultsA total of 21 studies were included. The findings highlighted the widespread use of various technologies, including wearable devices and force plates, to monitor athletes’ performance and inform evidence-based decision-making in training and injury prevention. Variables such as Player Load, changes of direction, and acute chronic workload ratio were identified as key metrics in injury prediction.ConclusionsThis review uncovers a dynamic field of research in athlete injury prevention, emphasizing the extensive use of varied technologies. A key finding is the pivotal role of Player Load data, which offers nuanced insights for customizing training loads according to sport-specific demands, player positions, and the physical requirements of various activities. Additionally, the review sheds light on the utility of tools like force plates in assessing fatigue, aiding recovery, and steering injury rehabilitation, particularly in sports prone to knee and ankle injuries. These insights not only enhance our understanding of injury prevention but also provide a strategic direction for future research, aiming to boost athlete safety, performance, and career longevity.

Research on the protection of athletes from injury by flexible conjugated materials in sports events

Sports are essential to everyone’s health because they assist athletes to establish physical and mental balance by strengthening muscles and ligaments. High-intensity training and low-quality equipment for sports tend to cause a wide range of injuries to the athlete. Higher education graduates’ regular education and lives are disrupted, either directly or indirectly, by sports injuries. Therefore, understanding the prevalence and root causes of college athletes’ injuries is crucial for enhancing student athletes’ performance and fostering healthy development. The ever-changing nature of injuries associated with sports and the patchy availability of rehabilitation facilities across India cause alarm. Inaccurately identifying players’ physical indications, uncomfortable clothing, and dissatisfaction with sports equipment are among the issues that can arise. The study investigates the potential of nanoparticles combined with sports flexible conjugate materials for injury prevention in athletes. The article proposed nanotechnology combined with flexible conjugated materials in sports events (Nano-FCM-SE) in sports training, explores the possibility of conjugated materials in enhancing the training effects of athletes, monitoring the status of sports, and bettering equipment. Sports equipment can help keep athletes safe by incorporating nanotechnology and flexible conjugated materials with superior optical, electrical, and other capabilities. Convenience, waterproof materials, flexibility, lightweight, aesthetics, breathability, and durability are evaluated for use in Nano-conjugated sports equipment materials. Evidence suggests that using flexible conjugated materials in athletic training can improve athlete performance and help the overall development of sports. The proposed method yields less negative results than MSI-TENG, TCM-MS, and RANSAC. The proposed damage severity model performs poorly relative to competitors (0.2). Compared to conventional models, the given models are effective on equipment. The sports injury protection system reported in this research has 5.17 percentage points greater detection efficiency than the current state of the art. Hierarchical strategies have the best RMSE for athlete safety. The findings of such methodologies in athlete safety on Nano conjugate materials and sports biology on sporting events and equipment underline the importance of precise data for athlete safety and performance.

APPLICATION OF WORKING CAPACITY RECOVERY MEANS FOR INJURY PREVENTION OF THE MUSCULOSKELETAL SYSTEM OF ELITE ATHLETES

Competitions in sports of higher achievements are safe if a number of conditions are observed in the process of training activities, precompetition and pre-start preparation. The high intensity and load in modern sports requires adaptation to them at the initial stage of training athletes, which is not always possible for coaches to do in the absence of a specially organized recovery process and programs for the prevention of injuries of the musculoskeletal system. Unfortunately, the number of injuries in adult and children's sports is not decreasing. Existing external and internal injury risk factors require special attention from the administration of sports schools and clubs, coaching staff to training and competition venues, as well as to the selection and application of special means of injury prevention for athletes. Injuries received by an athlete in the process of training or competitive activities require the presence of appropriate training of trainers in first aid.

Addressing the unresolved challenge of quantifying skiing exposure-A proof of concept using smartphone sensors.

In epidemiological studies related to winter sports, especially alpine skiing, an unresolved methodological challenge is the quantification of actual on-snow activity exposure. Such information would be relevant for reporting meaningful measures of injury incidence, which refers to the number of new injuries that occur in a given population and time period. Accordingly, accurate determination of the denominator, i.e., actual "activity exposure time", is critical for injury surveillance and reporting. In this perspective article, we explore the question of whether wearable sensors in combination with mHealth applications are suitable tools to accurately quantify the periods in a ski day when the skier is physically skiing and not resting or using a mechanical means of transport. As a first proof of concept, we present exemplary data from a youth competitive alpine skier who wore his smartphone with embedded sensors on his body on several ski days during one winter season. We compared these data to self-reported estimates of ski exposure, as used in athletes' training diaries. In summary, quantifying on-snow activity exposure in alpine skiing using sensor data from smartphones is technically feasible. For example, the sensors could be used to track ski training sessions, estimate the actual time spent skiing, and even quantify the number of runs and turns made as long as the smartphone is worn. Such data could be very useful in determining actual exposure time in the context of injury surveillance and could prove valuable for effective stress management and injury prevention in athletes.

The Utility of Myotonometry in Musculoskeletal Rehabilitation and Human Performance Programming.

Myotonometry is a relatively novel method used to quantify the biomechanical and viscoelastic properties (stiffness, compliance, tone, elasticity, creep, mechanical relaxation) of palpable musculotendinous structures with portable mechanical devices called myotonometers. Myotonometers obtain these measures by recording the magnitude of radial tissue deformation that occurs in response to the amount of force that is perpendicularly applied to the tissue through a device's probe. Myotonometric parameters such as stiffness and compliance have repeatedly demonstrated strong correlations with force production and muscle activation. Paradoxically, individual muscle stiffness measures have been associated with both superior athletic performance and higher incidence of injury. This suggest there may be optimal stiffness levels that promotes athletic performance while too much or too little may lead to an increased risk of injury. Numerous studies suggest that myotonometry may assist practitioners in the development of performance and rehabilitation programs that improves athletic performance, mitigates injury risk, guides therapeutic interventions, and optimizes return to activity decision making. Thus, the purpose of this narrative review is to summarize the potential utility of myotonometry as a clinical tool that assists musculoskeletal clinicians with the diagnosis, rehabilitation, and prevention of athletic injuries.

Sports Injury Epidemiology: Foundation of Evidence of, by, and for Athletic Trainers.

Sports Injury Epidemiology: Foundation of Evidence of, by, and for Athletic Trainers.

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.

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.

Adoption of Machine Learning Algorithm-Based Intelligent Basketball Training Robot in Athlete Injury Prevention.

In order to effectively prevent sports injuries caused by collisions in basketball training, realize efficient shooting, and reduce collisions, the machine learning algorithm was applied to intelligent robot for path planning in this study. First of all, combined with the basketball motion trajectory model, the sport recognition in basketball training was analyzed. Second, the mathematical model of the basketball motion trajectory of the shooting motion was established, and the factors affecting the shooting were analyzed. Thirdly, on this basis, the machine learning-based improved Q-Learning algorithm was proposed, the path planning of the moving robot was realized, and the obstacle avoidance behavior was accomplished effectively. In the path planning, the principle of fuzzy controller was applied, and the obstacle ultrasonic signals acquired around the robot were taken as input to effectively avoid obstacles. Finally, the robot was able to approach the target point while avoiding obstacles. The results of simulation experiment show that the obstacle avoidance path obtained by the improved Q-Learning algorithm is flatter, indicating that the algorithm is more suitable for the obstacle avoidance of the robot. Besides, it only takes about 250 s for the robot to find the obstacle avoidance path to the target state for the first time, which is far lower than the 700 s of the previous original algorithm. As a result, the fuzzy controller applied to the basketball robot can effectively avoid the obstacles in the robot movement process, and the motion trajectory curve obtained is relatively smooth. Therefore, the proposed machine learning algorithm has favorable obstacle avoidance effect when it is applied to path planning in basketball training, and can effectively prevent sports injuries in basketball activities.

Variations in cricket players' upper body dynamic balance across different levels of competition.

Balance ability has been associated with performance and injury prevention in athletes. Previous studies have investigated the differences in lower body dynamic balance abilities among male high school, collegiate and professional soccer players. To investigate the differences on the Upper Quarter Y-Balance Test (UQYBT) in school, collegiate and employed cricket players. Dynamic balance data were collected for school (SCH; n = 22), collegiate (COL; n = 19) and employed (EMP; n = 15) cricket players of Jamia Millia Islamia, New Delhi, India using the UQYBT standardized protocol. For the UQYBT, the participant reached with one hand in the medial, superolateral and inferolateral directions while maintaining three points of contact (other hand and both feet). The test was performed for both non-dominant and dominant hands. The SCH group had the least medial reach distance than the other two groups (p = 0.010). Also, SCH players tended to exhibit a lower composite reach score than the other groups, but this difference was not significant (p > 0.05). No bilateral differences were observed among the three groups in any of the reach directions. Upper body dynamic balance performance varied with the competition level. This may indicate that athletes' movement strategies may be different depending on the competition level and that normative values may need to be established for each competition level.

A neural network method to predict task- and step-specific ground reaction force magnitudes from trunk accelerations during running activities

Prediction of ground reaction force (GRF) magnitudes during running-based sports has several important applications, including optimal load prescription and injury prevention in athletes. Existing methods typically require information from multiple body-worn sensors, limiting their ecological validity, or aim to estimate discrete force parameters, limiting their ability to assess overall biomechanical load. This paper presents a neural network method to predict GRF time series from a single, commonly used, trunk-mounted accelerometer. The presented method uses a principal component analysis and multilayer perceptron (MLP) to obtain predictions. Time-series r2 coefficients with test data averaged around 0.9 for each impact, comparing favourably with alternative approaches which require additional sensors. For the impact peak, r2 was 0.74 across activities, comparing favourably with correlation analysis approaches. Several modifications, such as subject-specific training of the MLP, may help to improve results further, but the presented method can accurately predict GRF from trunk accelerometry data without requiring additional information. Results demonstrate the scope of machine learning to exploit common wearable technologies to estimate GRF in sport-specific environments.