Background: Effective hypertrophy-oriented resistance training (RT) should comprise a combination of mechanical tension and metabolic stress. Regarding training variables, the most effective values are widely described in the literature. However, there is still a lack of consensus regarding the efficiency of advanced RT techniques and methods in comparison to traditional approaches. Methods: MEDLINE and SPORTDiscus databases were searched from 1996 to September 2019 for all studies investigating the effects of advanced RT techniques and methods on muscle hypertrophy and training variables. Thirty articles met the inclusion criteria and were consequently included for the quality assessment and data extraction. Results: Concerning the time-efficiency of training, the use of agonist–antagonist, upper–lower body supersets, drop and cluster sets, sarcoplasma stimulating training, employment of fast, but controlled duration of eccentric contractions (~2s), and high-load RT supplemented with low-load RT under blood flow restriction may provide an additional stimulus and an advantage to traditional training protocols. With regard to the higher degree of mechanical tension, the use of accentuated eccentric loading in RT should be considered. Implementation of drop sets, sarcoplasma stimulating training, low-load RT in conjunction with low-load RT under blood flow restriction could provide time-efficient solutions to increased metabolic stress. Conclusions: Due to insufficient evidence, it is difficult to provide specific guidelines for volume, intensity of effort, and frequency of previously mentioned RT techniques and methods. However, well-trained athletes may integrate advanced RT techniques and methods into their routines as an additional stimulus to break through plateaus and to prevent training monotony.

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 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.

Volume and intensity of exercise are the basic components of training loads, having a direct impact on adaptive patterns. Exercise volume during resistance training has been conventionally evaluated as a total number of repetitions performed in each set, regardless of the time and speed of performing individual exercises. The aim of this study was to evaluate the effect of varied tempos i.e. regular (REG) 2/0/2/0, medium (MED) 5/0/3/0 and slow (SLO) 6/0/4/0 during resistance exercise on training volume, based on the total number of performed repetitions (REP_sum1-5) and time under tension (TUT_sum1-5). Significant differences in TUT (s) were found in particular sets for each tempo of 2/0/2/0, 5/0/3/0 and 6/0/4/0 (p < 0.001). The ANOVA also revealed substantial differences in the REP for individual sets (p < 0.001). Post-hoc analyses showed that TUT for each set and total TUTsum1-5 were significantly higher in the 5/0/3/0 and 6/0/4/0 tempos compared to 2/0/2/0 (p < 0.001). REP was significantly higher for the 2/0/2/0 tempo compared to 5/0/3/0 and 6/0/4/0 tempo in each set. Total REP_sum1-5, TUT_sum1-5 between 5/0/3/0 and 6/0/4/0 tempos were not significantly different. The main finding of this study is that the movement tempo in strength training impacts training volume, both in terms of repetitions and total time under tension.

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.