The present study aimed to investigate the impact of practicing sports activities on manual skills, focused on reaction time depending on manual laterality. The objectives of the study were to identify the differences in improving simple, optional, and cognitive reaction times in the manual executions of student athletes who practice team sports involving the manual handling of the ball (volleyball, basketball, handball) in comparison with student athletes who practice individual sports and with non-athletic students; to identify the differences regarding the reaction time of the right- and left-handed executions depending on the manual lateralization of the subjects (right- and left-handedness) between the three experimental samples: team sports group (TSG), individual sports group (ISG), and the group of non-athletes (NAG) through the use of computer tests. The study included 335 subjects who were divided into three groups: TSG with 102 subjects, ISG with 112 subjects, and NAG with 121 subjects. The subjects of the study were given five computer tests to evaluate three types of reaction time: simple reaction time (Start/Stop Test), choice reaction time (Check Boxes Test, Hit-the-dot Test), and time of cognitive reaction (Trail making Test part A and B). The results were analyzed regarding right- and left-handedness, as well as the execution hand (right hand or left hand) in solving the tests. The results of the study highlighted significant statistical differences between the three groups: TSG, ISG, and NAG. The best results were recorded by TSG in all tests, and the lowest by NAG. Statistically significant differences were also recorded between the executions with the dominant hand compared to the executions with the non-dominant hand in relation to right- and left-handedness. The study highlighted that the smallest differences in all the study groups were recorded in the simple reaction time test, where the differences between the right-handed and left-handed executions were the lowest, reflecting the best level of symmetrization of the motor executions.

Symmetry and asymmetry significantly influence running biomechanics, performance, and injury risk. Given the practical, ethical, and methodological constraints inherent in human-subject studies, computational modeling emerges as a valuable alternative for exploring biomechanical asymmetries in detail. This study systematically evaluated the mechanical effects of lower limb imbalance during running using a simulation-based musculoskeletal framework in OpenSim. A total of 130 simulations were performed, incorporating controlled asymmetries in limb strength, stride length, and ground reaction forces (±5% and ±10%), to quantify alterations in joint moments, ground reaction forces (GRF), and muscular activation patterns. Results demonstrated clear biomechanical deviations under asymmetric conditions. Vertical ground reaction forces (GRF) decreased on the weaker limb and increased on the stronger limb, with peak knee joint moments rising by up to 20% under pronounced asymmetry. Muscle activation in major lower limb muscles, including the gastrocnemius and quadriceps, increased substantially on the stronger side, reflecting compensatory mechanical loading. These findings highlight the negative consequences of uneven limb loading and support the use of computational modeling to guide personalized training, rehabilitation, and injury prevention strategies.

Exercise adaptation depends on overload that is resolved by recovery, yet the same biology becomes maladaptive when immune, endocrine, metabolic, and muscle-centered stress signals fail to normalize. Exercise-induced maladaptation represents a systems-level failure of biological resolution, with direct relevance to disease-like dysregulation. Functional overreaching, non-functional overreaching, and overtraining syndrome remain difficult to diagnose because no single biomarker provides adequate specificity, temporal stability, or clinical portability. This narrative review synthesizes human and mechanistic evidence across proteomics, transcriptomics, metabolomics, endocrine profiling, extracellular vesicles, and mitochondrial quality-control biology to define the molecular architecture most relevant to athlete monitoring. Across these layers, the most coherent signatures cluster in immune-acute-phase activation, redox-buffering strain, endocrine drift, altered substrate availability, excitation–contraction dysfunction, integrated stress-response signaling, and defects in autophagy–mitophagy and lysosomal remodeling. Three translational elements emerge from this synthesis: a systems-convergence model of recovery failure, a staged biomarker deployment hierarchy, and a provisional recovery failure index. The practical priority is therefore not a solitary marker, but serial phenotype-anchored multimarker panels that connect circulating signals with muscle-centered biology and support decision-making before prolonged recovery failure becomes entrenched.

Introduction and objective: The study focused on identifying the differences regarding the implementation of a program that uses Fitlight technology and targets the development of simple and reactive repetitive speed in manual and bilateral manual executions. Methodology: There were 311 subjects in the study, including 243 student-athletes (handball, basketball, and volleyball) and 65 recreational subjects (control group). Study phasing: initial testing; implementing a 6-week experimental program using the Fitlight technology to improve manual, repetitive speed; final testing. Study tests: the Tapping the plates test-unilateral execution with the dominant hand (TP_dominant hand), the random reactive plate touching test_unilateral execution with the dominant hand (TPR_dominant hand), Touching the plates test- bilateral execution (TP_bilateral), Test Touching the plates bilateral execution-random reactive (TPR_bilateral). Results: The ANOVA analysis highlighted significant differences between the male and female groups, respectively, for all four versions of the Tapping Plate Test, for p<0.05. While analyzing the results of the male/female sports game groups, the most significant differences in progress were recorded in TP_dominant hand between Volleyball-Basketball with 0.179/0.126 executions; TPR_ dominant hand between Volleyball-Basketball -0.126/0.086 executions; TP_bilateral between Volleyball-Basketball -0.154/-0.173 executions; bilateral TPR_ between Volleyball-Handball -0.016/-0.033 executions. Analyzing the study results, we found that the control group recorded lower results than all the experimental groups. Conclusions: The Fitlight technology positively impacted the environment of simple and reactive repetitive speed as part of practicing an experimental program of specific exercises adapted to team sports games.

Player exploitation in esports refers to the practice of exploiting players for financial or other gain without providing fair compensation or benefits. This can include issues such as low wages lack of job security and poor working conditions. Examples of player exploitation in esports include signing players with long-term contracts with low wages and no benefits and the use of non-disclosure agreements. Agreement (NDA) prevents players from discussing their pay or working conditions. This situation makes it difficult for players to negotiate better contracts or speak out against abuse leaving teams if they are unhappy with their situation. Another example is the lack of proper training support and benefits for players such as healthcare and mental health. Many esports teams do not provide enough resources for players to improve their skills and maintain good physical condition. This can lead to fatigue and injuries which can seriously affect a player’s career. Many esports players are young and the lack of access to these resources makes them vulnerable to exploitation. The esports industry is still relatively new and unregulated which can make it easy for companies and teams to exploit players. However, there are many organizations and advocacy groups working to improve working conditions and ensure fair treatment for sports athletes. Athlete exploitation in esports in general is a serious issue that needs to be addressed to ensure that athletes are treated fairly and have the resources they need to succeed in their careers.

Hamstrings–quadriceps (H–Q) imbalance represents a biomechanical marker of knee instability and injury risk in running. This in silico (simulation-based) study introduces a digital machine learning framework designed to estimate H–Q imbalance using biomechanical features derived entirely from synthetic running trials and conceptually mappable to inertial-sensor domains. Key biomechanical predictors included the dynamic hamstrings-to-quadriceps ratio (H:Qdyn), the knee moment limb symmetry index (LSI), and the early-stance co-contraction index (CCI), all standard indicators of muscular balance and coordination in sports medicine. A reduced musculoskeletal framework emulating flexor–extensor balance, limb symmetry, and co-contraction patterns generated 573 synthetic running trials for 160 virtual subjects across three speeds. These interpretable features trained a calibrated gradient-boosting classifier evaluated via ROC-AUC, PR-AUC, balanced accuracy, F1, and Brier score. Across all conditions, the model achieved ROC-AUC 0.933 (95% CI 0.908–0.958), balanced accuracy 0.943 (95% CI 0.924–0.962), PR-AUC 0.918 (95% CI 0.892–0.943), F1 0.940 (95% CI 0.919–0.958), and Brier 0.056 (95% CI 0.041–0.072), outperforming the logistic baseline. Dynamic H:Q ratio and knee moment symmetry were the dominant predictors, while co-contraction provided complementary biomechanical nuance. These results demonstrate that simulation-derived frameworks can reproduce IMU-relevant biomechanical variability, enabling interpretable machine learning for transparent assessment of muscular balance in sports medicine.