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.

Diabetes is a chronic disease. Some complications can be prevented, their effects can be slowed down. Sedentary lifestyle increases the risk of obesity and consequently the predisposition to diabetes II. The article aimed to demonstrate the positive and negative effects of exercise on active and sedentary diabetics and on pathophysiology, evaluating the effects after 3 and 6 months. The study involved 90 participants, both male and female, with type II diabetes, aged 45, divided into two groups: Group A (n=50, sedentary) and Group B (n=40, active). We evaluated anthropometric parameters, blood chemistry values, which are fundamental for the transversal evaluation of the results. In group A improvements were less noticeable than group B. The most improved parameter is blood sugar, Glycemic values and BMI. Cholesterol and Hb1Ac decreased but more slowly than previous parameters. The expectations of the study were, not only in recognizing the therapeutic and preventive powers of exercise, but above all in choosing to program a motor protocol after a team work between diabetologist, sports doctor and kinesiologist and/ or personal trainer. Physical activity is an additional therapy to insulin.

Study aim: Real-time EEG neurofeedback (NFB) training is gaining popularity as a cognitive-motor enhancement tool in elite sports. However, its protocol-specific effectiveness across disciplines and outcome domains remains unclear. This systematic review and meta-analysis examined the effects of real-time EEG NFB in elite athletes, focusing on protocol characteristics, targeted outcomes, and effect sizes. Material and methods: A search of six databases (2000–2025) identified 24 studies involving national- or international-level athletes. Protocols were categorized by frequency band (e.g., SMR, beta, theta), sport discipline, and performance domain (cognitive, motor, psychological). Risk of bias was assessed using RoB 2 and ROBINS-I; GRADE was applied to evaluate evidence certainty. Eleven studies provided quantitative data for meta-analysis using standardized mean differences (SMD). Results: Most studies involved individual sports, with judo, archery, and shooting most common. SMR and beta protocols dominated in precision sports; theta protocols were prevalent in combat sports. Meta-analysis showed a large pooled effect (SMD = 1.26; 95% CI: 1.05–1.45), with high heterogeneity ( I ² = 94.1%). GRADE indicated moderate certainty for cognitive and psychological outcomes. Conclusion: EEG NFB appears effective in enhancing attention, motor control, and anxiety regulation in elite athletes. However, methodological variability highlights the need for standardized protocols and further replication.

Study aim: The aim of the study was to investigate the variability and symmetry of upper limb physiological tremor in elite sport dancers under simulated competitive conditions. Material and methods: Eighteen elite dancers (9 males, 9 females; age:16.1 ± 2.3 years; training experience: 8 ± 2.1 years; body mass: 62.0 ± 10.4 kg; height: 169.8 ± 7.9cm) all members national team and finalists in Polish championship, participated in the study. The experimental protocol involved a two-phase simulated competition, during which participants performed five standard dances in both semifinal and final rounds. Tremor signals were recorded before and after simulation using tri-axial accelerometry in a standardized postural postition. Frequency-domain analysis was conducted to extract tremor amplitude (L) and frequency (F) in two specific bands: 2–5 Hz and 9–14 Hz. Repeated measures ANOVA was used to analyze the effects of condition (pre vs. post), limb side, and gender. Results: Post-simulation tremor amplitude in the 9–14 Hz range increased significantly ( p = 0.0003; η _p ² = 0.60), suggesting the presence of neuromuscular fatigue and possible increased recruitment of fast-twitch muscle fibers. A significant interaction between limb side and gender (p = 0.006; η _p ² = 0.384) revealed asymmetrical tremor only in male participants, with higher post-exercise values in the dominant arm. Conclusions: Physiological tremor may serve as a low-cost, non-invasive biomarker of neuromuscular fatigue and motor asymmetry in competitive dance. Its application could support individualized training monitoring, optimize fatigue management, and injury prevention strategies in sports requiring high motor precision.

In recent years, many articles have been published describing the impact of physical activity and diet on bone health. This review has aimed to figure out the possible epigenetic mechanisms that influence bone metabolism. Many studies highlighted the effects of macro and micronutrients combined with exercise on the regulation of gene expression through miRs. The present review will describe how physical activity and nutrition can prevent abnormal epigenetic regulation that otherwise could lead to bone-metabolism-related diseases, the most significant of which is osteoporosis. Nowadays, it is known that this effect can be carried out not only by endogenously produced miRs, but also through those intakes through the diet. Indeed, they have also been found in the transcriptome of animals and plants, and it is possible to hypothesise an interaction between miRNAs produced by different kingdoms and epigenetic influences on human gene expression. In particular, the key to the activation pathways triggered by diet and physical activity appears to be the activation of Runt-related transcription factor 2 (RUNX2), the expression of which is regulated by several miRs. Among the main miRs involved are exercise-induced miR21 and 21-5p, and food-induced miR 221-3p and 222-3p.

The Timed-Up and Go (TUG) test is widely utilized by healthcare professionals for assessing fall risk and mobility due to its practicality. Currently, test results are based solely on execution time, but integrating technological devices into the test can provide additional information to enhance result accuracy. This study aimed to assess the reliability of smartphone-based instrumented TUG (iTUG) parameters. We conducted evaluations of intra- and inter-device reliabilities, hypothesizing that iTUG parameters would be replicable across all experiments. A total of 30 individuals participated in Experiment A to assess intra-device reliability, while Experiment B involved 15 individuals to evaluate inter-device reliability. The smartphone was securely attached to participants’ bodies at the lumbar spine level between the L3 and L5 vertebrae. In Experiment A, subjects performed the TUG test three times using the same device, with a 5 min interval between each trial. Experiment B required participants to perform three trials using different devices, with the same time interval between trials. Comparing stopwatch and smartphone measurements in Experiment A, no significant differences in test duration were found between the two devices. A perfect correlation and Bland–Altman analysis indicated good agreement between devices. Intra-device reliability analysis in Experiment A revealed significant reliability in nine out of eleven variables, with four variables showing excellent reliability and five showing moderate to high reliability. In Experiment B, inter-device reliability was observed among different smartphone devices, with nine out of eleven variables demonstrating significant reliability. Notable differences were found in angular velocity peak at the first and second turns between specific devices, emphasizing the importance of considering device variations in inertial measurements. Hence, smartphone inertial sensors present a valid, applicable, and feasible alternative for TUG assessment.