Objective assessment of localized muscle fatigue may support exercise prescription, rehabilitation monitoring, and sports performance management. Surface electromyography (sEMG) reflects neuromuscular activation, whereas B-mode and Doppler ultrasonography provide complementary information on muscle morphology and vascular response. This study evaluated training-dependent biceps brachii fatigue using ultrasound and sEMG and examined whether combined dynamic recordings improved fatigue-state classification. Forty healthy men were allocated to an advanced resistance-training group (n = 20) or a novice group (n = 20). Participants performed seated right-arm 10-kg dumbbell curls until voluntary exhaustion. Because the same absolute load represented different relative intensities across participants and groups, training-status comparisons were interpreted accordingly. Biceps brachii muscle thickness, anterior circumflex humeral artery peak systolic velocity (PSV), sEMG root mean square (RMS), and median frequency (MDF) were measured at baseline, moderate fatigue, and exhaustion. Stage-matched ultrasound video and sEMG recordings from 35 artifact-free participants were evaluated by participant-level five-fold internal cross-validation. Advanced trainees completed more repetitions than novices (16.3 ± 2.7 vs. 13.3 ± 2.3 repetitions, p < 0.001). Baseline-to-exhaustion muscle-thickness change was smaller in advanced trainees than in novices (12.69 ± 4.67
To develop an interpretable machine learning model based on biomechanical measurement parameters to predict peak anterior cruciate ligament (ACL) force during side-cutting maneuvers, thereby providing an assessment method for sports training and monitoring. A total of 204 male football and basketball athletes were recruited. Kinematic, kinetic, and electromyographic parameters during side-cutting maneuvers were extracted using the Qualisys optical motion capture system, force plates, and surface electromyography. Machine learning algorithms were used for model training and testing to predict peak ACL force, and Shapley Additive Explanations (SHAP) were employed to interpret feature values. The Extreme Gradient Boosting (XGBoost) model demonstrated high fitting performance (R² = 0.929), with low overall deviation (RMSE = 0.051) and prediction error (MAE = 0.032). Global SHAP importance analysis indicated that knee flexion angle (KFA), hamstring-to-quadriceps activation ratio (H/Q), hip flexion angle (HFA), knee valgus moment (KVM), ankle valgus angle (AVA), hip adduction angle (HAA), and anterior tibial shear force (ASF) were the key features for predicting peak ACL force. The SHAP importance values of KFA (0.07), H/Q (0.05), HFA (0.03), KVM (0.02), and AVA (0.02) showed relatively high contributions, demonstrating strong predictive ability. The increase in peak ACL force during side-cutting maneuvers is essentially the result of the coupling of dynamic postural control mechanical loads and neuromuscular effects. The machine learning model integrating multidimensional indicators achieved high accuracy, improved the predictive capability of peak ACL force in practical applications, and provided an interpretation for the comprehensive analysis of peak ACL force.
We aimed to develop contemporary reference values among recreationally trained and strength-interested individuals for the squat, bench press, and deadlift, and describe sex- and age-related patterns in estimated relative strength. This cross-sectional study analysed user-reported data collected between 1 January and 31 December 2024 by Strength Level Limited. Submissions reported sex, age, body mass, load lifted, and repetitions completed. Inclusion criteria were age ≥ 12 years, entries for all three lifts on the same day from the same hashed IP address, and ≤ 15 repetitions. Estimated one-repetition maximums (e1RMs) were derived using Brzycki and Epley equations with interpolation between 8 and 10 repetitions. Relative strength was calculated as e1RM divided by body mass (kg/kgbody mass), and percentile norms were generated separately for males and females across age groups. Data from 64,457 males and 10,435 females were included. All lifts were right-skewed, with higher e1RMs and greater variability observed in males. In males, relative strength was greater in young adulthood than adolescence, and lower in middle-aged and older adults. In females, relative strength was similar in adolescence and young adulthood, with lower values observed in middle-aged and older adults. For example, median relative squat strength was 1.77 and 1.38 kg/kgbody mass in young and older males, respectively, and 1.30 and 0.99 kg/kgbody mass in young and older females, respectively. These findings provide contemporary benchmarks for squat, bench press, and deadlift strength among recreational and strength-interested individuals.
This cross-sectional study examined the relative age effect (RAE) in Chinese youth table tennis players and compared its magnitude across competitive level, age and sex. Data were obtained from 1,322 athletes aged 12–15 years competing in the 2024–2025 National Youth Table Tennis Championships and Liaoning Provincial Youth Table Tennis Competitions. Athletes were classified into four birth quarters (Q1–Q4). RAE was tested using chi-square analyses, with Cohen’s w and odds ratios (ORs) used to estimate effect magnitude; analyses were stratified by competitive level, age group and sex. A significant RAE was found in the national high-level sample (P < 0.001), with Q1 athletes over-represented relative to Q4 athletes, whereas no significant RAE was observed in the Liaoning provincial sample. In the national sample, RAE was evident across age groups and was stronger in younger athletes. Sex-stratified analyses suggested earlier RAE expression in females and more pronounced effects in older male age groups. These findings indicate that RAE in Chinese youth table tennis is concentrated in high-level selection contexts and varies by age and sex. Relative age and developmental status should be considered in talent identification.
Whether elite International Powerlifting Federation (IPF) classic powerlifters are becoming younger and whether this reflects earlier recorded competition or a younger active population is unclear. This study examined temporal patterns in age, performance and recorded intervals to elite performance. Population and top-100 analyses covered 2013 to 2025 and Open World Championship podium analyses covered 2013 to 2026. Earlier OpenIPF records informed competition histories. Population-wide age-performance relationships were estimated using locally weighted scatterplot smoothing and profile-cluster bootstrapping. Trends in annual medians were evaluated using Theil-Sen slopes and Kendall tests. Intervals from first recorded eligible competition to first observed elite performance were calculated among profiles with eligible histories. The performance cohort comprised 431,476 entries from 161,073 profiles. The population-wide curve peaked at 26.0 years (95
