Nutritional supplementation is widely used in resistance training, yet assessment of “hypertrophy” is often confounded by body-composition surrogates. This narrative review, anchored in mechanistic plausibility, integrates trials reporting morphology-direct outcomes (ultrasound/MRI). Across 46 eligible trials, protein/essential amino acids (EAA) showed consistent benefits when daily intake was <1.6 g·kg⁻¹·day⁻¹ or when per-meal leucine provision was <2–3 g; effects plateaued once intakes exceeded ~2.0 g·kg⁻¹·day⁻¹. Creatine monohydrate (3–5 g·day⁻¹, with or without loading) produced measurable increases in muscle thickness or cross-sectional area in interventions lasting ≥8–12 weeks, mediated by enhanced training volume and quality. β-hydroxy-β-methylbutyrate (HMB, 3 g·day⁻¹) demonstrated conditional utility during high training stress or caloric deficit, but was largely neutral in well-fed, resistance-trained cohorts. Adjuncts such as omega-3 fatty acids (1–2 g·day⁻¹), citrulline (6–8 g pre-exercise), and collagen (10–15 g·day⁻¹ plus vitamin C) primarily facilitated training tolerance, recovery, or connective-tissue adaptation, rather than driving hypertrophy directly. A tiered model is proposed: protein/EAA as the foundation, creatine as amplifier, HMB as conditional agent, and adjuncts as facilitators. Methodological heterogeneity, short intervention length, and inconsistent imaging protocols remain limiting factors, underscoring the need for standardized ultrasound/MRI and adequately powered, preregistered trials.

Hamstrings–quadriceps (H–Q) imbalance represents a biomechanical marker of knee instability and injury risk in running. This in silico study introduces a simulation-derived machine-learning framework designed to estimate H–Q imbalance using biomechanical features conceptually mappable to inertial-sensor domains. 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, PR-AUC 0.918, F1 0.940, and Brier 0.056, outperforming a calibrated logistic baseline. Dynamic H:Q ratio and knee-moment symmetry were the dominant predictors, while co-contraction contributed complementary nuance. These results indicate that simulation-derived digital frameworks can reproduce IMU-relevant biomechanical variability, enabling interpretable machine learning for objective assessment of muscular balance in sports medicine.

This review critically examines training and testing strategies in youth athletes, focusing on growth, maturation, and specialization. Early specialization, occurring in athletes younger than 12 years, is associated with training volumes above 15 h/week and with dropout rates of nearly 30–35% before age 15. The aim was to evaluate age- and maturity-specific protocols, identify limitations of adult-derived models, and provide evidence-informed recommendations. A structured search in PubMed, Scopus, and Web of Science between 2000 and 2025 identified 325 articles; after screening, 87 were included, with 19 addressing maturation, 16 testing, 18 training load, 15 motor skill development, and 19 specialization. Results show that growth variability, particularly during peak height velocity (10–12 y girls; 12–14 y boys), increases injury risk two- to threefold. Standardized adult testing (e.g., VO₂max treadmill) is often unreliable in adolescents, while field-based protocols such as the Yo-Yo IR1, CMJ, and FMS provide safer and more valid alternatives. Flexible periodization with deload cycles of 1–2 weeks and monitoring through RPE, GPS, and HR improves both safety and adaptation. Diversified sport participation further reduces burnout and supports long-term outcomes. Integrating maturity status into training and testing optimizes development, minimizes injury, and sustains lifelong athlete engagement.

The study examined whether regional differences in physical education (PE) allocation influence adolescent health in Romania. Using data from all 42 counties, we compared weekly PE minutes with two outcomes: prevalence of obesity and participation in daily moderate-to-vigorous physical activity (MVPA). Counties reported an average of 92.5 minutes of PE per week, with a wide range from 60 to 150 minutes. Adolescent obesity prevalence varied from 9.0% to 24.5%, while MVPA rates ranged from 34.0% to 68.2%. Correlation analysis revealed that higher PE time was moderately associated with lower obesity (r = –0.38, p = .015) and higher MVPA (r = 0.44, p = .006). Multivariate regression confirmed that every additional 10 minutes of weekly PE corresponded to a 0.7 percentage point decrease in obesity prevalence and a 0.9 percentage point increase in MVPA, independent of household income and urbanization. Counties with the lowest PE allocation consistently showed the worst health indicators, highlighting regional inequities.These findings demonstrate that PE policy functions as a significant predictor of adolescent health at population level. Establishing a nationwide minimum of 150 minutes per week, coupled with resource investments in disadvantaged regions, could reduce obesity, promote active lifestyles, and close rural–urban gaps. Physical education should be recognized not only as a curricular requirement but also as a strategic public health intervention with measurable benefits for national well-being.