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.

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.

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, 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, the review positions exercise timing as a context-dependent lever within periodized training design.