CARDIOVASCULAR RESPONSE OF ATHLETES UNDER DIFFERENT TRAINING CONDITIONS

ABSTRACT Introduction: Improving cardiovascular function is one of the main training goals of many sports. Objective: To understand the characteristics of the cardiovascular response of athletes under different training conditions. Methods: Thirty male basketball students were enrolled. The subjects were divided into A and B groups according to their years of training, with 15 students in each group. Exercise fatigue tests were performed, starting at a low intensity and gradually increasing the load to a relatively high degree of fatigue. Results: The RMSSD value was 42.82±31.41ms in group A and 46.48±35.26ms in group B undera low fatigue state. The LF/HF value of the athletes in group A was 2.86±1.47 and the LF/HF value of the athletes in group B was 2.94±1.68. The RMSSD value was 40.78±31.17ms and 32.37±36.42ms for groups A and B, respectively, undera high fatigue state. Conclusions: Athletes with more years of training can mobilize more cardiac reserves to meet the increase in exercise load in a fatigue state and have better autonomic nervous regulation in the process of reaching a higher degree of fatigue state. Level of evidence II; Therapeutic studies - investigation of treatment results.

Pre-exercise Carbohydrate Drink Adding Protein Improves Post-exercise Fatigue Recovery

Purpose: This study aimed to assess the requirement of protein in pre-exercise carbohydrate drinks for optimal endurance performance at high intensity and post-exercise fatigue recovery.Methods: Endurance performance at 85% V.⁢O2peak of young men (age 20 ± 0.9 years, V.⁢2peak 49.3 ± 0.3 L/min) was measured for two consecutive days using cycling time to exhaustion and total work exerted 2 h after three isocaloric supplementations: RICE (50 g, protein: 1.8 g), n = 7; SOY + RICE (50 g, protein: 4.8 g), n = 7; and WHEY + RICE (50 g, protein: 9.2 g), n = 7.Results: Endurance performance was similar for the three supplemented conditions. Nevertheless, maximal cycling time and total exerted work from Day 1 to Day 2 were improved in the WHEY + RICE (+21%, p = 0.05) and SOY-RICE (+16%, p = 0.10) supplemented conditions, not the RICE supplemented condition. Increases in plasma interleukin-6 (IL-6) were observed 1 h after exercise regardless of supplemented conditions. Plasma creatine kinase remained unchanged after exercise for all three supplemented conditions. Increases in ferric reducing antioxidant power (FRAP) after exercise were small and similar for the three supplemented conditions.Conclusion: Adding protein into carbohydrate drinks provides no immediate benefit in endurance performance and antioxidant capacity yet enhances fatigue recovery for the next day. Soy-containing carbohydrate drink, despite 50% less protein content, shows similar fatigue recovery efficacy to the whey protein-containing carbohydrate drink. These results suggest the importance of dietary nitrogen sources in fatigue recovery after exercise.

The Ryanodine Receptor as a Sensor for Intracellular Environments in Muscles

The ryanodine receptor (RyR) is a Ca2+ release channel in the sarcoplasmic reticulum of skeletal and cardiac muscles and plays a key role in excitation–contraction coupling. The activity of the RyR is regulated by the changes in the level of many intracellular factors, such as divalent cations (Ca2+ and Mg2+), nucleotides, associated proteins, and reactive oxygen species. Since these intracellular factors change depending on the condition of the muscle, e.g., exercise, fatigue, or disease states, the RyR channel activity will be altered accordingly. In this review, we describe how the RyR channel is regulated under various conditions and discuss the possibility that the RyR acts as a sensor for changes in the intracellular environments in muscles.

Design of the Exercise Load Data Monitoring System for Exercise Training Based on the Neural Network

In order to monitor the sports load data of athletes in sports training, this paper studies the methods and systems of sports load monitoring and fatigue warning based on neural network technology. In this paper, the neural network parallel optimization algorithm based on big data is used to accurately estimate the motion load and intensity according to the determined motion mode and acceleration data, so as to realize the real-time monitoring of the exercise training. The results show that the value of η is usually small to ensure that the weight correction can truly follow the direction of the gradient descent. In this paper, 176 samples were extracted from the monitoring data collected by the “National Tennis Team Information Platform,” 160 of which were selected as training samples and the other 16 as test samples. Ant colony size M = 20. The minimum value Wmin of the weight interval is −2, and the maximum value Wmax is 2. The maximum number of iterations is set to 200. σ = 1; that is, only one optimal solution is retained. The domain is divided into 60 parts evenly; that is, r = 60. Generally, η can be taken as any number [28] between [10-3, 10], but the value is usually small to ensure that the weight correction can truly follow the direction of the gradient descent. In this paper, the value is 0.003. In the early warning stage of exercise fatigue, reasonable measurement units of exercise fatigue time were divided according to the characteristics of different exercise items. It is proved that the Bayesian classification algorithm can effectively avoid the sports injury caused by overtraining by warning the fatigue and preventing the sports injury caused by overtraining.

EFFECT OF METABOLIC ENZYMES ON ACCELERATION ABILITY IN EXERCISE FATIGUE

ABSTRACT Introduction Study the relationship between the metabolic enzyme and the biological image, filtered by an adaptive filtering algorithm. Objective The research aims to In this study, human metabolic enzymes were evaluated by electrocardiogram and electromyogram images, and an adaptive filtering algorithm removed the noises in the images. Methods The electrocardiogram and electromyogram images at different periods were obtained, and the calculation method and application scope of the adaptive filtering algorithm were analysed. Results Adaptive filter was designed by the combination of adaptive filtering algorithm and dynamic information. Therefore, the artefact of the image was removed. Conclusions The adaptive filtering algorithm can effectively remove the noise or artefact in electrocardiogram and electromyogram signals. The optimal image information can be obtained. Level of evidence II; Therapeutic studies - investigation of treatment results.

ANALYZING THE INFLUENCING FACTORS OF SPORTS FATIGUE BASED ON ALGORITHM

ABSTRACT Objective: This paper discusses the monitoring method of exercise fatigue and analyzes the influencing factors of exercise fatigue. Methods: Based on the feature extraction method of the fatigue image signal, a series of changes caused by exercise fatigue are analyzed by the biofeedback technique. SVM algorithm and neural network model are used to identify the fatigue state of motion. Characteristics of electroencephalogram (EEG) and electromyography (EMG) during fatigue. Results: When sports fatigue occurred, the composite index of bio-feedback technology shows a decrease in HRV index and increases in HRV time-domain indicators, frequency-domain indicators, and SAa values. Conclusions: It has a high degree of systematization. The proposed method is non-invasive and has practical application value. Level of evidence II; Therapeutic studies - investigation of treatment results.

The Effect of Endurance Exercise Modality on Markers of Fatigue

Background: Exercise power output, and resulting fatigue, is regulated based on central and peripheral sensory input. Whether exercise mode, specifically, contributes to this regulation remains unexplored. Objective: This study was designed to determine if differences in markers of fatigue would be present during two time trials of similar duration and intensity, as a result of exercise mode (cycling and rowing). Method: In a randomized crossover design, nine subjects completed the two 7-min time trials, on different days. Exercise power output, heart rate, rating of perceived exertion, and blood lactate measurements were analyzed using repeated-measures ANOVAs. Results: There was a significant interaction between mode and time for power output (p =.02), but no significant differences between matched time points were observed for any of the dependent variables used to assess fatigue (p >.05). Conclusion: Similar levels of heart rate, perceived exertion, and blood lactate for time trials on different modes, but with the same duration and directed intensity, suggest that in a laboratory environment, exercise is regulated more by physiological disturbance and sensory cues than by exercise mode. These findings support the sensory tolerance limit of exercise fatigue.