Objective: The study aimed to compare the differences in the performance of seven session-rating of perceived exertion (RPE)-derived metrics (coupled and uncoupled acute: chronic workload ratio (ACWR), weekly ratio of workload change, monotony, standard deviation of weekly workload change, exponentially weighted moving average (EWMA), and robust exponential decreasing index (REDI)) in classifying the performance of an injury prediction model after taking into account the time series (no latency, 5-day latency, and 10-day latency).Design: The study documented the RPE of eight curlers in their daily training routine for 211 days prior to the Olympic Games.Methods: Seven Session-RPE (sRPE)-derived metrics were used to build models at three time series nodes using logistic regression and multilayer perceptron. Receiver operating characteristic plots were plotted to evaluate the model’s performance.Results: Among the seven sRPE-derived metrics multilayer perceptron models, the model without time delay (same-day load corresponding to same-day injury) exhibited the highest average classification performance (86.5%, AUC = 0.773). EMWA and REDI demonstrated the best classification performance (84.4%, p < 0.001). Notably, EMWA achieved the highest classifying accuracy in the no-delay time series (90.0%, AUC = 0.899), followed by the weekly load change rate under the 5-day delay time series (88.9%, AUC = 0.841).Conclusion: EWMA without delay is a more sensitive indicator for detecting injury risk.

This study aimed to investigate the differences in proprioceptive changes at different time points (Pre vs. Post vs. 90 min vs. 24 h) before and after ischemic preconditioning. It followed a within-subject, self-controlled design, and a total of 21 trained male participants were assessed using two-point discrimination threshold tests on thigh and knee joint position sense testing. The results demonstrated that ischemic preconditioning effectively improved proprioceptive accuracy (two-point discrimination, right lower limb, p < 0.001; two-point discrimination, left lower limb, p < 0.001; knee position sense, right lower limb, p = 0.001; knee position sense, left lower limb, p = 0.014) and stability (two-point discrimination, right lower limb, p < 0.001; two-point discrimination, left lower limb, p = 0.002; knee position sense, right lower limb, p < 0.001; knee position sense, left lower limb, p = 0.003), with the optimal time point for enhancement identified at 90 min. This research suggests administering IPC 90 min before warm-up or competition to enhance athletic performance.

Background: This study investigated the diurnal variation in endurance performance and the corresponding ventilatory/metabolic responses during a maximal incremental cycling test. Methods: Thirty physically active young men (age = 23.5 ± 2.2 years; weekly exercise volume: ≥6 h·wk⁻¹) with regular daily routines were recruited for a randomized crossover study. Each participant completed two maximal incremental cycling tests to volitional exhaustion: one in the morning (07:00–09:00) and another in the evening (17:00–19:00). The two sessions were separated by a one-week washout period. Key ventilatory and metabolic variables, including maximal voluntary ventilation (MVV) and maximal oxygen uptake (VO_2max), were continuously measured, and time to exhaustion (TTE) was monitored. Paired-samples t-tests were used to compare morning versus evening outcomes. Results: Key performance and physiological variables, including MVV (p < 0.01), VO_2max (p < 0.01), and TTE (p < 0.01), were significantly improved in the evening as compared to the morning. Conclusions: Both ventilatory/metabolic function and endurance performance during a maximal incremental cycling test induce a pronounced diurnal rhythm in trained young men, with superior outcomes observed in the evening.

This study investigated the neural timing control characteristics of countermovement jumps between elite athletes and nonathletes using muscle synergy theory. Twenty male participants (high-level group [HLG, N = 10] and untrained group [UG, N = 10]) performed the countermovement jump while surface electromyography from eight lower limb muscles and three-dimensional force plate data were recorded. Nonnegative matrix factorization extracted three muscle synergy modules, and bootstrap resampling with bias-corrected 95% confidence intervals (CIs) analyzed temporal parameters. Results demonstrated: (a) HLG achieved significantly greater jump height (95% CI: [−0.139, −0.026]); (b) HLG exhibited delayed activation onset (95% CI: [−0.227, −0.015]), peak timing (95% CI: [−0.300, −0.028]), and offset (95% CI: [−0.216, −0.014]) in Synergy module 1, delayed onset (95% CI: [−0.225, −0.054]) and offset (95% CI: [−0.224, −0.072]) in Synergy module 2, and delayed offset in Synergy module 3 (95% CI: [−0.197, −0.017]); and (c) HLG showed narrower full width at half maximum in Synergy module 1 (95% CI: [0.009, 0.094]), with no group differences in amplitude parameters. These findings suggest that, compared with UG, HLG exhibit delayed synergy activation timing during the countermovement jump. This temporal pattern may be associated with more effective use of the eccentric phase and more coordinated interjoint timing during propulsion.

This study aimed to develop a performance prediction model for speed climbing based on session-RPE (sRPE) and countermovement jump (CMJ), and to determine optimal pre-competition taper strategies. Training data were collected from 12 speed climbers over approximately 180 days (931 daily observations), including daily sRPE, CMJ, and climbing performance. The Banister fitness–fatigue impulse-response model was employed to quantify the dose–response relationship of training load, combined with random forest and gradient boosting for nonlinear feature analysis. Parametric grid search was used to optimize taper duration and daily load sequences. CMJ emerged as the most stable predictor of climbing performance (Pearson r = −0.319), ranking first in random forest feature importance (23.4%), while same-day sRPE showed negligible correlation with performance (r = 0.043). Group-level Banister model estimates showed a fitness time constant of τ1 = 34 ± 19 days and a fatigue time constant of τ2 = 17 ± 5 days, yielding τ1/τ2 = 2.0. Optimal taper duration ranged from 5 to 21 days, with load reduced to 29–90% of normal training load, exhibiting highly individualized characteristics. The physiological basis of tapering lies in exploiting the time gap τ1 > τ2—allowing fatigue to dissipate before fitness decays. A taper window of τ2 to τ1 days (approximately 17–34 days) is recommended, with an exponential decay curve for progressive load reduction. Taper strategies should be individualized, with daily CMJ monitoring for real-time load adjustment during the taper period.

Objective: This exploratory study aimed to: (1) verify whether session-RPE (sRPE) can serve as a reliable workload-monitoring indicator in female adolescent swimmers aged 13–17 years; (2) compare the same-week injury-classification performance of sRPE-, exponentially weighted moving average (EWMA)-, and robust exponential decreasing index (REDI)-derived indicators using a multilayer perceptron (MLP) model; and (3) identify candidate indicator combinations for future prospective validation in youth swimming. Design: A 12-week prospective observational tracking study was conducted with 20 female adolescent 200 m individual medley swimmers (aged 13–17 years) from a single provincial youth swimming team (Zhejiang). Twenty-five injury events were recorded across 240 athlete-weeks. Because repeated athlete-weeks were analyzed as independent samples and injury status was classified within the same week as the load indicators, the classification findings are preliminary internal-validation results that may overestimate model performance; they are not externally validated prospective predictions. Methods: Pearson and Spearman correlation analyses, intraclass correlation coefficient (ICC), and Bland-Altman plots were used to evaluate associations and agreement between sRPE-derived indicators and traditional training-load indicators (swimming distance and training duration). Twelve sRPE-derived indicators (four each from sRPE, EWMA, and REDI) were used as input features for MLP same-week binary classification models. REDI was included because its natural-logarithmic weighting emphasizes long-term cumulative load, complementing the recency-weighted EWMA. Model performance was evaluated using area under the ROC curve (AUC), with Bootstrap resampling (1000 iterations) to characterize internal stability. Results: Swimming distance, which shares no computational component with sRPE, was very strongly correlated with sRPE (r = 0.965, p < 0.001). Training duration was also strongly correlated with sRPE (r = 0.972, p < 0.001), but this duration correlation is partly part-whole by construction because duration enters the sRPE calculation. After Z-score standardization, the ICC between duration and sRPE reached 0.614 (substantial agreement), although standardization does not imply direct interchangeability. Among the 12 single indicators, EWMA uncoupled ACWR had the highest observed AUC (0.946). The EWMA four-indicator combination (coupled ACWR + uncoupled ACWR + Diff + SD) had the highest observed internal AUC (0.958). Uncoupled ACWR and Diff indicators showed numerically higher AUCs than their coupled and SD counterparts, respectively; these differences were not formally tested statistically and confidence intervals overlapped. Conclusions: In this small, single-team, single-event sample of female adolescent 200 m individual medley swimmers, sRPE showed moderate-to-substantial agreement with traditional workload indicators. The EWMA four-indicator model showed the highest observed internal discriminative ability for same-week injury classification (AUC = 0.958), with EWMA uncoupled ACWR the highest-ranked single indicator (AUC = 0.946). These findings are preliminary because of the small number of injury events, class imbalance, athlete-level clustering, binary injury labels, absence of simpler-model comparison, and lack of lagged or external validation. The exploratory two-tier warning thresholds (yellow > 0.5, red > 0.7) are conceptual, non-optimized boundaries and require prospective derivation and validation in independent cohorts before any operational use.