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In sport, there is extensive evidence that supports the benefits associated with a mastery climate. However, limited studies have explored how physical tools could be used to promote mastery climates in youth sport contexts. Using an action research approach, we sought to understand the benefits and drawbacks of applying tools grounded in goal setting to promote a mastery environment: (a) an "arm sleeve" to be worn by athletes during training and matches and (b) a "reflection sheet" for use pre- and posttraining/-matches. These tools were implemented for a 3-week period with a U13 academy team (18 players and two coaches). Based on observation notes, focus groups, and one-on-one interviews, the analysis showed that the arm sleeves were helpful reminders for process goals, whereas the coaches had abandoned the use of reflection sheets due to lack of time. The benefits and drawbacks of the tools are discussed while pedagogical and practical implications are considered. [ABSTRACT FROM AUTHOR]

An athlete's training aims to achieve the highest possible sports results by improving physical dispositions which lead to cardiac adaptive changes. The annual training cycle is divided into periods. The preparatory period begins with gradually increasing training intensity and volume until the competitive period occurs, when the athlete's maximum performance is expected. Finally, the athlete enters a phase of loss of fitness, which is called detraining. Detraining is a time of resting both physically and mentally from the training regime and usually lasts about 4 weeks for endurance athletes. We collected data from much research on athletes' detraining. According to these data, the earliest change after detraining seems to be a decrease in left ventricular wall thickness and left ventricular mass, followed by decreased performance parameters, diastolic diameter of the left ventricle and size of the left atrium. A reversal of adaptive changes affects the left heart chamber first, then the right atrium and, finally, the right ventricle. Training reduction is often proposed as a method of differentiating an athlete's heart from cardiomyopathies. The aim of this study is to consider the diagnostic value of detraining in differentiating athletes' hearts from cardiomyopathies. We suggest that detraining cannot be conclusive in differentiating the disease from adaptive changes. Although a withdrawal of the characteristic morphological, functional and electrocardiographic changes occurs in healthy athletes during detraining, it can also concern individuals with cardiomyopathies due to the lower expression of abnormal features after decreased training loads. Therefore, a quick diagnosis and individual assessments using imaging and genetic tests are essential to recommend a proper type of activity. [ABSTRACT FROM AUTHOR]

This paper discusses the use of an improved random forest regression algorithm (RFRA) to predict the total score of powerlifters. The paper collected the age, weight, and total score of multiple powerlifters, and then used an improved RFRA to build a predictive model. The parameters of this model are optimized by a differential squirrel search algorithm. The experimental results show that our proposed method can effectively predict the total score of powerlifters with an error of less than 10%, which can provide a reference for experts and athletes before training or competition. [ABSTRACT FROM AUTHOR]

This study aimed to optimise performance prediction in short-course swimming through Principal Component Analyses (PCA) and multiple regression. All women's freestyle races at the European Short-Course Swimming Championships were analysed. Established performance metrics were obtained including start, free-swimming, and turn performance metrics. PCA were conducted to reduce redundant variables, and a multiple linear regression was performed where the criterion was swimming time. A practical tool, the Potential Predictor, was developed from regression equations to facilitate performance prediction. Bland and Altman analyses with 95% limits of agreement (95% LOA) were used to assess agreement between predicted and actual swimming performance. There was a very strong agreement between predicted and actual swimming performance. The mean bias for all race distances was less than 0.1s with wider LOAs for the 800 m (95% LOA −7.6 to + 7.7s) but tighter LOAs for the other races (95% LOAs −0.6 to + 0.6s). Free-Swimming Speed (FSS) and turn performance were identified as Key Performance Indicators (KPIs) in the longer distance races (200 m, 400 m, 800 m). Start performance emerged as a KPI in sprint races (50 m and 100 m). The successful implementation of PCA and multiple regression provides coaches with a valuable tool to uncover individual potential and empowers data-driven decision-making in athlete training. [ABSTRACT FROM AUTHOR]

Aims: To assess body composition by means of BOD POD in the large cohort of Italian Olympic athletes of many sport disciplines (studied at the same time), and to provide possible reference values for body composition in elite athletes. Methods: 1556 elite athletes, who took part in the selection procedure for the 2016 Rio Olympic Games for the National Italian Olympic Committee (CONI), were retrospectively studied. Body composition was determined using air plethysmography-based BOD POD. Results: We observed that Fat Mass (FM) and Fat-free Mass (FFM) should be considered as two mutually independent domains in elite athletes. By performing Principal Component Analysis, we defined two independent main domains (respectively, representing FM and FFM), which presented different trends according to gender and static or dynamic exercise load. Lastly, we reported possible reference values for FM index and FFM index, respectively, representing the largest contributors to FM domain and FFM domain, and calculated as FM or FFM (kg)/height (m2). Conclusions: Our findings might provide a basis to optimize the practical approach to body composition in athletes, highlighting the importance of considering indicators of fat mass and lean mass "simultaneously" and not specularly, according to different sport disciplines as well. Moreover, these data might contribute to standardize reference values for body composition in elite athletes, with a view to potentially helping to monitor and guide training regimens, prevent related detrimental practices and plan cardiometabolic prevention and rehabilitation programs. [ABSTRACT FROM AUTHOR]

Wearable devices in sports have been used at the professional and higher collegiate levels, but not much research has been conducted at lower collegiate division levels. The objective of this retrospective study was to gather big data using the Catapult wearable technology, develop an algorithm for musculoskeletal modeling, and longitudinally determine the workloads of male college soccer (football) athletes at the Division III (DIII) level over the course of a 12-week season. The results showed that over the course of a season, (1) the average match workload (432 ± 47.7) was 1.5× greater than the average training workload (252.9 ± 23.3) for all positions, (2) the forward position showed the lowest workloads throughout the season, and (3) the highest mean workload was in week 8 (370.1 ± 177.2), while the lowest was in week 4 (219.1 ± 26.4). These results provide the impetus to enable the interoperability of data gathered from wearable devices into data management systems for optimizing performance and health. [ABSTRACT FROM AUTHOR]

Background: Acute altitude has a relevant impact on exercise physiology and performance. Therefore, the positive impact on the performance level is utilized as a training strategy in professional as well as recreational athletes. However, ventilatory thresholds (VTs) and lactate thresholds (LTs), as established performance measures, cannot be easily assessed at high altitudes. Therefore, a noninvasive, reliable, and cost-effective method is needed to facilitate and monitor training management at high altitudes. High Alt Med Biol. 25:94–99, 2024. Methods: In a cross-sectional setting, a total of 14 healthy recreational athletes performed a graded cycling exercise test at sea level (Munich, Germany: 512 m/949 mbar) and high altitude (Zugspitze: 2,650 m/715 mbar). Anaerobic thresholds (ATs) were assessed using a novel method based on beat-to-beat repolarization instability (dT) detected by Frank-lead electrocardiogram (ECG) monitoring. The ECG-based ATs (ATdT°) were compared to routine LTs assessed according to Dickhuth and Mader. Results: After acute altitude exposure, a decrease in AT was detected using a novel ECG-based method (ATdT°: 159.80 ± 52.21 W vs. 134.66 ± 34.91 W). AtdT° levels correlated significantly with LTDickhuth and LTMader, at baseline (rDickhuth/AtdT° = 0.979; p < 0.001) (rMader/AtdT° = 0.943; p < 0.001), and at high altitude (rDickhuth/AtdT° = 0.969; p < 0.001) (rMader/AtdT° = 0.942; p < 0.001). Conclusion: Assessment of ATdT is a reliable method to detect performance alterations at altitude. This novel method may facilitate the training management of athletes at high altitudes. [ABSTRACT FROM AUTHOR]

Background and Study Aim. It is well known that neuromuscular electrostimulation and plyometric exercises positively influence the gain of explosive force in the lower limbs. The combination of plyometric method and electrostimulation begins to gain a positive trend, being included in the modern training of athletes due to the important gain of explosive force. The purpose of this study is to investigate the effects of combining plyometric and electrostimulation methods in specific training for the development of lower limb muscle strength in long jumpers. Material and Methods. The study was conducted on 15 women's long jump athletes, age category 18 years ± 1,622, with an average weight of 50.80 kg ± 3,932, height of 165.87cm ±3,292 and foot plant of 39.00 ±1,254. The group carried out the experimental training program for a duration of 12 weeks, with means specific to the two methods, three times a week, lasting 60 minutes. It was assessed: horizontal speed on the last 10m of the run up, 1 RM (a maximum rep for a squat); vertical jump; flight time and performance achieved in the competition. The maximum repetition for a squat was tested with the GykoRePower system, the vertical jump was determined with the OptoJump Next system, and the performance in the test was recorded during the competition. The vertical jump, the average value of power used to develop the explosive force and the length of the jump were evaluated. The recorded data on the muscle profile of the subjects were analyzed with the SPSS statistical analysis program Results. The statistical analysis performed with T-student obtained significant differences between the two tests of the experiment group (p ≤ 0.001): 1RM registers a progress of 10.476 kg with a progress rate of 18.07%, optimal load registers a progress of 3.627 kg with a progress rate of 8.93%, vertical jump achieves a progress of 4.567 cm with a progress rate of 16.09% with a flight time progress of 0.03833 s with a progress rate of 7.39%, and the speed on the last 10 m of run up progresses by 0.5234 s with a progress rate of 6.79%. Subjects recorded a progress for competition performance of 36.467 cm compared to the initial test with a progress rate of 8.50%. Conclusions. The research results are objective arguments that highlight that a specific training based on combining plyometric method with electrostimulation determines significant improvements in the development of explosive force of the lower limbs in long jumps with positive effects on sports performance for long jumpers. [ABSTRACT FROM AUTHOR]

In the process of aerobics competition, it is necessary to evaluate the training and performance of athletes and coaches with real-time data. However, traditional data recording methods have some limitations, such as relying on manual recording or being limited by the number and location of sensors. The research aims to use light sensors and machine learning techniques to develop an application that can detect, record and simulate aerobics competition data in real time. The movement trajectory and posture information of athletes are captured by light sensors, and the data is analyzed and simulated with the help of machine learning algorithms to provide more accurate and comprehensive competition data. The light sensor can accurately detect the posture and movement of the human body, and transmit the data to the computer through the data transmission module. The sensor data is processed and analyzed using machine learning algorithms, which are trained to recognize specific movements and postures and compare them to preset criteria. The results show that athletes and coaches can obtain real-time competition data through the application, and analyze and feedback to improve the accuracy of training effect and performance evaluation. [ABSTRACT FROM AUTHOR]

Traditional coaches need to rely on experience and intuition to adjust athletes' movements and postures, which has the problems of strong subjectivity and unstable effects. In this paper, an image optical processing method based on artificial intelligence is proposed to realize the adaptive adjustment of aerobics athletes. The research uses the image optical processing technology to collect and analyze the movement of athletes in the training process and extract the key movement parameters. Then, combined with artificial intelligence algorithm, the extracted action parameters are analyzed and compared, so as to achieve accurate evaluation and adjustment of athletes' actions. Finally, the adjustment plan is applied to the training of athletes, and through real-time monitoring and feedback mechanisms, the athletes' movements and postures are continuously adjusted and optimized. Through experiments and practical applications, the system can accurately determine the movements and postures of athletes, and provide corresponding adjustment plans based on the actual situation. Compared with traditional manual adjustments, the system has higher accuracy and stability, which can effectively improve the technical level and performance ability of athletes. [ABSTRACT FROM AUTHOR]

Sports effect evaluation is the main module of modern information construction. To improve the accuracy of sports effect evaluation and enhance the training level of athletes, this paper aims to conduct in-depth research on sports effect evaluation based on feature selection under the background of big data. Firstly, a sports evaluation system is designed based on the Window Presentation Foundation (WPF) that adopts a Client–Server (C/S) mode. The overall architecture of the system is proposed by expounding the functional modules and the schema of information tables in the database. To assess the impact of sports, the data mining algorithm and big data analytics technique called Random Forest (RF) is used as the core evaluation system. With the help of information gain, feature selection is performed, based on which decision trees are constructed by generating branch nodes. The random forest is created by combining all the decision trees, which helps in improving the generalization. The proposed method is experimentally verified and it is found that the overall stability of the effect evaluation system is good and it effectively improves the efficiency and accuracy of sports effect evaluation, thus enhancing the level of athletes' training. The proposed sports effect evaluation method performs excellently in the experimental validation, as seen by the remarkable F1 Score of 85.32%, average accuracy of 84.28%, precision of 86%, and recall of 82.28%. In comparison, this performs noticeably better than current approaches, proving its improved efficacy in assessing the effects of sports and confirming its ability to improve athlete training proficiency. [ABSTRACT FROM AUTHOR]

In the motion recognition system, the motion track of athletes can be obtained in real time and accurately by using the light motion capture technology, which provides strong support for the training and competition of athletes. The purpose of this study is to explore the application of light motion capture technology based on multi-mode sensor in badminton players' motion recognition system, so as to improve the athletes' training effect and competitive level. In this paper, multi-modal sensors combined with light motion capture technology are used to obtain real-time athletes' motion data through sensors installed on athletes, and the data is analyzed and processed by algorithms. The position of athletes is tracked by optical sensors to obtain their motion trajectory. The research results show that the light motion capture technology based on multi-modal sensors can accurately identify the movement posture and action of badminton players, real-time monitoring of athletes' movement status, and provide timely feedback and guidance. Through the application of sports recognition system, the training effect of athletes has been significantly improved, and the competitive level has been effectively improved. [ABSTRACT FROM AUTHOR]

This paper aims to propose a table tennis target detection (TD) method based on deep learning (DL) and multi-scale feature fusion (MFF) to improve the detection accuracy of the ball in table tennis competition, optimize the training process of athletes, and improve the technical level. In this paper, DL technology is used to improve the accuracy of table tennis TD through MFF guidance. Initially, based on the FAST Region-based Convolutional Neural Network (FAST R-CNN), the TD is carried out in the table tennis match. Then, through the method of MFF guidance, different levels of feature information are fused, which improves the accuracy of TD. Through the experimental verification on the test set, it is found that the mean Average Precision (mAP) value of the target detection algorithm (TDA) proposed here reaches 87.3%, which is obviously superior to other TDAs and has higher robustness. The DL TDA combined with the proposed MFF can be applied to various detection fields and can help the application of TD in real life. [ABSTRACT FROM AUTHOR]

The completion degree of sports training can not reach the corresponding standard, and the training effect will be greatly weakened. In order to improve the effect of sports training, the evaluation method of sports training completion degree based on deep residual network is studied. The image collector based on ARM is used to collect the action images of athletes in sports training, and the collected action images are preprocessed based on spatial scale filtering and regression factors. Construct a depth residual network, learn the implicit relationship between athletes' state and the dynamic change process of sports training actions through off-line training, and train the model; In the online application process, the preprocessed action images will be input into the trained evaluation model to evaluate the athletes' sports training action completion in real time. At the same time, residual shrinkage unit and attention mechanism are used to optimize the depth residual network, which improves the training efficiency and evaluation performance of the network. The experimental results show that this method has good evaluation performance under the condition of setting parameters, and can effectively improve the effect of physical training. [ABSTRACT FROM AUTHOR]

To ensure both optimal health and performances, monitoring physiological and psychological states is of main importance for athletes. It is well known that monitoring heart rate variability and using validated questionnaires is useful for monitoring both the health and training status of athletes of different sports. Motorsports such as rally require high levels of physical and mental preparation thus information about psychophysiological status of rally athletes is fundamental. The aim of this study was to assess the autonomic regulation, stress, recovery conditions of one driver and one co-driver competing at the Italian National Rally Championship during their competition period. Heart rate variability parameters, acute recovery and stress states were assessed the day before, during the two days of race and the day following the races. Results showed that driver and co-driver had a sharp decrease of mean RR intervals, root mean square of successive differences between normal heartbeats, and standard deviation of the N-N interval during race days, while the stress index showed the inverse trend, and this behaviour was clearly visible in the Poincaré plots and power spectrum density graphs. The acute recovery and stress states questionnaire showed significant differences in recovery and stress scoring for the driver but not for the co-driver, although the trends were similar. This study describes the psychophysiological demands of a rally competition period suggesting that a daily evaluation of heart rate variability, recovery, stress states is useful for monitoring health status in rally athletes and could be implemented to make decision about training and recovery strategies. [ABSTRACT FROM AUTHOR]