Recent advances in training monitoring are centered on the statistical indicators of the concentric phase of the movement. However, those studies lack consideration of the integrity of the movement. Moreover, training performance evaluation needs valid data on the movement. Thus, this study presents a full-waveform resistance training monitoring system (FRTMS) as a whole-movement-process monitoring solution to acquire and analyze the full-waveform data of resistance training. The FRTMS includes a portable data acquisition device and a data processing and visualization software platform. The data acquisition device monitors the barbell’s movement data. The software platform guides users through the acquisition of training parameters and provides feedback on the training result variables. To validate the FRTMS, we compared the simultaneous measurements of 30–90% 1RM of Smith squat lifts performed by 21 subjects with the FRTMS to similar measurements obtained with a previously validated three-dimensional motion capture system. Results showed that the FRTMS produced practically identical velocity outcomes, with a high Pearson’s correlation coefficient, intraclass correlation coefficient, and coefficient of multiple correlations and a low root mean square error. We also studied the applications of the FRTMS in practical training by comparing the training results of a six-week experimental intervention with velocity-based training (VBT) and percentage-based training (PBT). The current findings suggest that the proposed monitoring system can provide reliable data for refining future training monitoring and analysis.

Accurate measurement of training intensity is crucial for optimizing endurance running performance. Lactate Threshold Heart Rate (LT_HR) and Speed (LT_speed) are commonly used but require invasive, costly methods. Heart Rate Variability (HRV) metrics offer a non-invasive alternative by assessing autonomic nervous system activity. This study evaluated the validity of various HRV metrics in predicting LT_HR and LT_speed during indoor and outdoor graded exercise tests (GXTs) in 19 recreational runners. HRV thresholds (HRVTs) were determined using SD1, SD2, DFA-α1, DFA-α2, SDNN, and RMSSD via the Dmax method, and are referred to as HRVT₁ to HRVT₆. Indoor GXT results showed HRVT_HR often underestimated LT_HR, with all HRVTs, except HRVT_4speed (DFA-α2, p = 0.06), showing significant differences from LT. Moderate correlations were observed for HRVT_5HR (SDNN, r = 0.66), HRVT_2speed (SD2, r = 0.56), and HRVT_3speed (DFA-α1, r = 0.70). Outdoor GXTs showed no significant differences between most HRVTs and LT, except HRVT_6HR (RMSSD, p = 0.03). HRVT_speed demonstrated moderate correlations (r = 0.54–0.7) with LT_speed. While HRVT and LT may reflect different physiological thresholds, HRVTs, particularly those based on DFA-α2, show promise as non-invasive predictors of LT in recreational runners. Received: 03 May 2025 Accepted after revision: 13 October 2025 Accepted Manuscript online: 13 October 2025 © . Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany

This study compared two physiological modeling approaches, the Peronnet-Thibault (P-T) model and the Minimal Power (MP) model, to identify key parameters representing individual physiological characteristics and to explore their applications in running training. Model parameters were estimated using nonlinear least squares fitting, and predictive performance was evaluated by the mean absolute error (MAE). Results from the World Running Records (WRR) indicated that the MP model generally outperformed the P-T model in linking running performance with physiological variables, demonstrating greater capability in extracting physiological parameters. Further validation using the British Runner Records (BRR) showed that the MP model achieved MAE values of 3.02% for males and 3.47% for females, reflecting strong generalization to real running performance. Furthermore, descriptive analyses of the relationships between MP model parameters and running performance further support its potential value in personalized training and performance prediction.

Blood lactate reflects the balance between lactate production and clearance during exercise, and lactate thresholds are widely used in endurance testing an

This study compared the effects of velocity-based training (VBT) and percentage-based training (PBT) on lower-body strength and the sticking region in squat exercises among resistance-trained males over a six-week intervention. Twenty resistance-trained males were randomized to train in a 10% velocity loss threshold VBT group (n = 10, age = 21.96 ± 2.27 years, body height = 1.79 ± 0.04 m, body mass = 70.98 ± 6.16 kg) or a traditional PBT group (n = 10, age = 22.12 ± 2.38 years, body height = 1.78 ± 0.02 m, body mass = 74.14 ± 4.87 kg), each training twice weekly for six weeks. Changes in the squat one-repetition maximum (1RM), relative strength, the countermovement jump (CMJ), the standing long jump (SLJ), duration of the sticking region, and average velocity in the sticking region were measured using pre- and post-tests. Results indicated significant improvements in the squat 1RM, relative strength, and the CMJ for both groups, with no significant changes in sticking region velocity (p > 0.05). Except for squat 1RM improvements (p < 0.01), no other significant differences were noted between groups post-intervention (p > 0.05). In conclusion, both interventions significantly enhanced lower-body strength, with the VBT regimen showing greater effectiveness. Neither approach significantly altered the duration or velocity of the sticking region, though the PBT regimen yielded more favorable improvements in sticking region performance. These findings suggest that differences in muscle adaptation and strength qualities between regimens might explain the varied impacts on the sticking region. Future research could explore these aspects to further refine training strategies for improving sticking region performance across various movement phases.