Post activation potentiation (PAP) has shown improved performance during movements requiring large muscular power output following contractions under near maximal load conditions. PAP can be described as an acute enhancement of performance or an enhancement of factors determining an explosive sports activity following a preload stimulus. In practice, PAP has been achieved by complex training, which involves a combination of a heavy loaded exercise followed by a biomechanically similar explosive activity, best if specific for a particular sport discipline. The main objective of this study was to investigate the effects of PAP on performance in explosive motor activities specific for basketball, luge and athletics throws. The novel approach to the experiments included individualized recovery time (IRT) between the conditioning exercise and the explosive activity. Additionally, the research groups were homogenous and included only competitive athletes of similar age and training experience. Thirty one well trained athletes from 3 different sport disciplines participated in the study. All athletes performed a heavy loaded conditioning activity (80-130%1RM) followed by a biomechanically similar explosive exercise, during which power (W) or the rate of power development (W/s/kg) was evaluated. The results of our experiment confirmed the effectiveness of PAP with well-trained athlets during explosive motor activities such as jumping, throwing and pushing. Additionally, our research showed that eccentric supramaximal intensities (130% 1RM) can be effective in eliciting PAP in strength trained athletes. Our experiments also showed that the IRT should be individualized because athletes differ in the strength level, training experience and muscle fiber structure. In the three experiments conducted with basketball players, track and field athletes and luge athletes, the optimal IRT equaled 6 min. This justifies the need to individualize the volume and intensity of the CA, and especially the IRT, between the CA and the explosive activity.

The main objective of this research was to determine the effects of a long-term ketogenic diet, rich in polyunsaturated fatty acids, on aerobic performance and exercise metabolism in off-road cyclists. Additionally, the effects of this diet on body mass and body composition were evaluated, as well as those that occurred in the lipid and lipoprotein profiles due to the dietary intervention. The research material included eight male subjects, aged 28.3 ± 3.9 years, with at least five years of training experience that competed in off-road cycling. Each cyclist performed a continuous exercise protocol on a cycloergometer with varied intensity, after a mixed and ketogenic diet in a crossover design. The ketogenic diet stimulated favorable changes in body mass and body composition, as well as in the lipid and lipoprotein profiles. Important findings of the present study include a significant increase in the relative values of maximal oxygen uptake (VO_2max) and oxygen uptake at lactate threshold (VO₂ LT) after the ketogenic diet, which can be explained by reductions in body mass and fat mass and/or the greater oxygen uptake necessary to obtain the same energy yield as on a mixed diet, due to increased fat oxidation or by enhanced sympathetic activation. The max work load and the work load at lactate threshold were significantly higher after the mixed diet. The values of the respiratory exchange ratio (RER) were significantly lower at rest and during particular stages of the exercise protocol following the ketogenic diet. The heart rate (HR) and oxygen uptake were significantly higher at rest and during the first three stages of exercise after the ketogenic diet, while the reverse was true during the last stage of the exercise protocol conducted with maximal intensity. Creatine kinase (CK) and lactate dehydrogenase (LDH) activity were significantly lower at rest and during particular stages of the 105-min exercise protocol following the low carbohydrate ketogenic diet. The alterations in insulin and cortisol concentrations due to the dietary intervention confirm the concept that the glucostatic mechanism controls the hormonal and metabolic responses to exercise.

The bench press exercise (BP) plays an important role in recreational and professional training, in which muscle activity is an important multifactorial phenomenon. The objective of this paper is to systematically review electromyography (EMG) studies performed on the barbell BP exercise to answer the following research questions: Which muscles show the greatest activity during the flat BP? Which changes in muscle activity are related to specific conditions under which the BP movement is performed? PubMed, Scopus, Web of Science and Cochrane Central Register of Controlled Trials (CENTRAL) in the Cochrane Library were searched through June 10, 2016. A combination of the following search terms was used: bench press, chest press, board press, test, measure, assessment, dynamometer, kinematics and biomechanics. Only original, full-text articles were considered. The search process resulted in 14 relevant studies that were included in the discussion. The triceps brachii (TB) and pectoralis major (PM) muscles were found to have similar activity during the BP, which was significantly higher than the activity of the anterior deltoid. During the BP movement, muscle activity changes with exercise intensity, velocity of movement, fatigue, mental focus, movement phase and stability conditions, such as bar vibration or unstable surfaces. Under these circumstances, TB is the most common object of activity change. PM and TB EMG activity is more dominant and shows greater EMG amplitude than anterior deltoid during the BP. There are six factors that can influence muscle activity during the BP; however, the most important factor is exercise intensity, which interacts with all other factors. The research on muscle activity in the BP has several unresolved areas, such as clearly and strongly defined guidelines to perform EMG measurements (e.g., how to elaborate with surface EMG limits) or guidelines for the use of exact muscle models.

Resistance exercise is a popular form of conditioning for numerous sport disciplines, and recently different modes of strength training are being evaluated for health benefits. Resistance exercise differs significantly in nature, and several variables determine the direction and range of adaptive changes that occur in the muscular and skeletal system of the body. Some modes of resistance training can also be effective in stimulating the cardiovascular system. These variables include exercise selection (general, specific, single or multi joint, dynamic, explosive), type of resistance (free weights, variable resistance, isokinetics), order of exercise (upper and lower body or push and pull exercises), and most of all the training load which includes intensity expressed as % of 1RM, number of repetitions, number of sets and the rest interval between sets. Manipulating these variables allows for specific adaptive changes which may include gains in muscle mass, muscle strength or muscle endurance. It has been well established that during resistance exercise fatigue occurs, regardless of the volume and intensity of work applied. The peripheral mechanisms of fatigue have been studied and explained in more detail than those related to the CNS. This review is an attempt to bring together the latest knowledge regarding fatigue, both peripheral and central, during resistance exercise. The authors of this review concentrated on physiological and biochemical mechanisms underlying fatigue in exercises performed with maximal intensity, as well as those performed to exhaustion with numerous repetitions and submaximal load.

This systematic review and meta-analysis investigates the application of artificial intelligence (AI) in sports performance analysis. Sixteen peer-reviewed studies spanning 13 distinct sports disciplines were included, employing a variety of AI techniques—from classical machine learning algorithms to advanced deep learning and computer vision models. Methods applied encompassed Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, reinforcement learning, and predictive modeling architectures. The pooled average classification accuracy was 87.78% (95% CI: 82.66–92.90), although substantial heterogeneity was observed across studies (I² = 93.75%). Computer vision and deep learning-based approaches were associated with higher performance metrics in several studies, particularly in movement-intensive sports such as tennis and basketball. Nevertheless, several challenges were identified, including lack of standardization in model evaluation, limited algorithmic transparency, and difficulties in generalizing findings from controlled laboratory environments to real-world competitive settings. The results underscore the promising role of AI in optimizing training protocols, supporting tactical decisions, and enhancing injury prevention strategies. Further research is warranted to address the ethical, methodological, and practical considerations surrounding the deployment of AI in sports contexts.

The Valsalva manoeuvre, intra‑abdominal pressure (IAP) and intrathoracic pressure (ITP) play important roles in resistance training and common daily activities. The purpose of this review is to summarize the ITP and IAP responses to resistance exercises and to determine which exercises elicit...