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 a dynamic alternation between catabolic and anabolic states coordinated primarily by AMP-activated protein kinase (AMPK) and mechanistic target of rapamycin (mTOR). While transient activation of these pathways underpins beneficial molecular remodeling, the system-level consequences of sustained anabolic drive remain insufficiently conceptualized in exercise biology. This article presents a conceptual mechanistic narrative review integrating evidence from molecular nutrition, exercise physiology, redox biology, and epigenetic regulation to define limits of adaptive signaling. We propose the Metabolic Overdrive Model, a systems-level framework describing the transition from adaptive AMPK–mTOR oscillation to a high-anabolic lock-in state characterized by persistent mTORC1 activation, suppressed AMPK signaling, altered NAD⁺ economy (SIRT1–PARP imbalance), redox dysregulation, and progressive epigenetic drift. Using exercise and training as models of sustained metabolic stress, we synthesize mechanistic parallels across energy sensing, oxidative signaling, and chromatin regulation without implying pathological causality. The framework generates testable predictions linking prolonged post-exercise anabolic signaling (>24 h) to specific molecular signatures, including AMPK phosphorylation status, NAD⁺ availability, PARylation, histone acetylation, and DNA methylation dynamics. By reframing exercise adaptation as a loss-of-oscillation phenomenon rather than a linear continuum, this model provides a mechanistic language for hypothesis generation, biomarker-guided periodization, and future experimental validation.

Exercise-derived reactive oxygen species (ROS) are required for mitochondrial and hypertrophic adaptations, creating a practical trade-off: antioxidant strategies may support short-term performance and recovery yet blunt training signals when mis-timed or over-dosed. We performed a structured narrative review informed by transparent database searches of MEDLINE, Scopus, and SPORTDiscus (2000–2025), prioritizing human intervention studies and using mechanistic evidence to interpret plausibility. Evidence was mapped by antioxidant class, dose, timing, training modality, and context. Across trials, chronic high-dose vitamins C/E taken close to key sessions are most consistently associated with attenuation of redox-sensitive signaling, whereas food-first polyphenols and selected bioactives (e.g., tart cherry/anthocyanins, pomegranate, and curcumin) more often support recovery when positioned away from adaptation-critical workouts, without clear evidence of impaired training gains. N-acetylcysteine can acutely improve tolerance to repeated high-intensity exercise, but effects during prolonged training remain uncertain and appear context-dependent. We propose Redox-Adaptive Periodization, aligning antioxidant class, dose, and timing with the primary objective (adaptation vs. immediate readiness) and environmental constraints, and we outline methodological priorities to advance precision redox management.

Exercise-derived reactive oxygen species (ROS) are required for mitochondrial and hypertrophic adaptations, creating a practical trade-off: antioxidant strategies may support short-term performance and recovery yet blunt training signals when mis-timed or over-dosed. We performed a structured narrative review informed by transparent database searches of MEDLINE, Scopus, and SPORTDiscus (2000-2025), prioritizing human intervention studies and using mechanistic evidence to interpret plausibility. Evidence was mapped by antioxidant class, dose, timing, training modality, and context. Across trials, chronic high-dose vitamins C/E taken close to key sessions are most consistently associated with attenuation of redox-sensitive signaling, whereas food-first polyphenols and selected bioactives (e.g., tart cherry/anthocyanins, pomegranate, curcumin) more often support recovery when positioned away from adaptation-critical workouts, without clear evidence of impaired training gains. N-acetylcysteine can acutely improve tolerance to repeated high-intensity exercise, but effects during prolonged training remain uncertain and appear context-dependent. We propose Redox-Adaptive Periodization, aligning antioxidant class, dose, and timing with the primary objective (adaptation vs. immediate readiness) and environmental constraints, and we outline methodological priorities to advance precision redox management.

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.

Time of day is often treated as a scheduling constraint rather than a true programming variable, yet training time is not neutral and can modulate both performance expression and the biological cost of exercise. This structured narrative review synthesizes evidence on morning versus afternoon/evening training in athletes and physically active adults, integrating performance outcomes with internal load, autonomic recovery, sleep interaction, and downstream physiological stress. Acute evidence generally supports superior late-afternoon or early-evening performance, particularly for neuromuscular tasks, whereas chronic training studies do not indicate a universal optimal training time. Instead, adaptation appears to depend on chronotype, habitual training time, the wake-to-training interval, light exposure, nutritional timing, training–testing congruency, and competition-specific demands. Mechanistically, exercise timing likely interacts with BMAL1/CRY loops, AMPK-SIRT1-PGC-1alpha signaling, PGC-1alpha-driven mitochondrial remodeling, and redox-inflammatory pathways, although much of this evidence remains preclinical. Accordingly, this review advances a Performance–Biological Cost (PBC) framework in which training time is judged by the balance between output, recovery opportunity, and residual molecular cost rather than by acute performance alone. By integrating athlete-relevant human evidence with mechanistic insights and translating them into explicit decision tools and testable predictions, this review positions exercise timing as a context-dependent lever within periodized training design.