Zhaoqian Li

This study compared the accuracy of eight velocity‐based one‐repetition maximum (1RM) estimation methods during the free‐weight back squat across sexes. Thirty resistance‐trained individuals (fifteen males and fifteen females) completed two sessions involving four submaximal loads followed by actual 1RM attempts. The load–velocity relationship (LVR) was modeled using both multiple‐point (40%, 55%, 70%, and 85% 1RM) and two‐point (40% and 85% 1RM) methods. Four minimum velocity threshold (MVT) approaches were applied: (a) general MVT (0.30 m·s ⁻¹ ), (b) actual MVT (the velocity at the actual 1RM in session one), (c) individual optimal MVT (the velocity that eliminated the difference between actual and predicted 1RM in session one), and (d) average optimal MVT (average of individual optimal MVTs across both sessions for each sex). Across sexes, only the individual and average optimal MVTs consistently avoided systematic overestimation of 1RM (raw errors ≤ 2.20 kg, p = 0.179–0.938) and produced relatively small absolute errors (4.5%–8.3%) compared with the other MVTs (8.0%–12.4%). Sex did not influence 1RM prediction accuracy ( p = 0.636), whereas the LVR model demonstrated a borderline significant effect ( p = 0.050). The optimal MVT did not differ between males (0.38 m·s ⁻¹ ) and females (0.36 m·s ⁻¹ ) but demonstrated poor reliability. Therefore, when direct 1RM testing is impractical, we recommend employing the unified value (0.37 m·s ⁻¹ ) as an efficient alternative for approximate 1RM estimation across sexes. However, caution should be exercised when using velocity‐based methods to estimate 1RM in the free‐weight back squat, as all methods exhibited relatively wide limits of agreement.

Background: Monitoring neuromuscular fatigue during resistance training informs load prescription and recovery management. Two common approaches, namely within-training assessment (immediately after each set) and post-training assessment (after session completion), differ in their methodological demands, yet their acute responses and within-session reliability remain insufficiently characterized. Hypothesis: Fatigue levels would be influenced by training protocol, value type (maximum vs average), and set number or timepoint, with corresponding differences in within-session reliability. Study Design: Randomized, counterbalanced crossover study. Level of Evidence: Level 2. Methods: A total of 13 well-trained male athletes completed 3 protocols in randomized order: 5 sets of fixed-repetition squats (Fixed), 5 sets of velocity-loss-threshold-controlled squats (VLT), and a control condition. Countermovement jump (CMJ) height was assessed at baseline, after each set, and at 8 post-training timepoints (5-60 minutes). Fatigue was quantified as the percentage of CMJ height relative to baseline, using both maximum and average values of 2 trials. Within-session reliability was evaluated via coefficient of variation (CV) and intraclass correlation coefficient (ICC). Results: For within-training assessment, significant main effects emerged for protocol ( P = 0.006), value type ( P = 0.031), and set number ( P < 0.001), with lower values (greater fatigue) observed when using average values and in the final 2 sets. All protocols demonstrated acceptable within-session reliability (CV ≤ 5.29%, ICC ≥ 0.745). For post-training assessment, time point significantly affected fatigue ( P < 0.001), with the lowest values at 30 to 40 minutes. ICC was low during 0 to 15 minutes post-training; acceptable reliability was achieved only with maximum values from 10 minutes onward (CV ≤ 7.29%, ICC ≥ 0.750). Conclusion: Both within-training and post-training fatigue assessments are feasible for monitoring resistance training fatigue, but methodological choices substantially influence measured values and reliability. Clinical Relevance: For within-training fatigue monitoring, average values provide greater sensitivity in detecting fatigue; for post-training assessment, maximum values should be used after ≥10 minutes of rest to ensure acceptable reliability.

Chen, W, Zhang, H, Li, R, Chen, Z, Zheng, J, Zhang, X, and Li, Z. Velocity-based monitoring lacks both accuracy and applicability for estimating repetitions in reserve during the hexagonal bar deadlift. J Strength Cond Res 40(11): 1335–1341, 2026—This study evaluated the accuracy and applicability of using barbell mean velocity (MV) to predict repetitions in reserve (RIR) in the hexagonal bar deadlift (HBD). After RIR-MV relationship modeling, 19 well-trained males completed 2 randomized sessions consisting of 4 sets to failure at either 65% or 85% of their 1-repetition maximum load. Methodological accuracy and applicability were rigorously defined by 3 concurrent criteria: a median absolute error ≤2 repetitions, and a minimum probability of 75% for both acceptable estimation (absolute error ≤2 repetitions) and the occurrence of expected velocity thresholds (i.e., the probability that the target RIR-specific MV actually manifests). Our results demonstrate that no experimental condition successfully satisfied all 3 criteria. Predictive performance was significantly compromised by load intensity, the RIR-MV relationship model, and the set number. Crucially, the low probability of threshold occurrence across most conditions renders these models functionally obsolete for real-time monitoring. Therefore, despite the prevalence of velocity-based training, the RIR-MV relationship lacks the requisite precision and reliability for monitoring HBD training. Practitioners should exercise caution when applying general or even individual velocity thresholds to HBD.

This study examined whether load–velocity relationship variables are sensitive indicators of maximal dynamic strength changes across introductory, overload, and taper microcycles. Twenty resistance-trained men completed a 3-week back squat and bench press program. One-repetition maximum and the two-point load–velocity relationship (20 kg and ~85% one-repetition maximum) were assessed before the introductory microcycle and after each microcycle. The results showed that (i) one-repetition maximum, area under the load–velocity relationship line (A line), and velocity–axis intercept (v 0) derived from peak velocity generally increased after the taper microcycle compared to earlier post-microcycle assessments (p≤0.046, differences=2.3–7.8%); (ii) the overload (coefficient of variation of individual response=5.1%) and taper (3.8%) microcycles exhibited greater variability compared with the introductory microcycle (2.0%). Among the monitored variables, variability was lowest for v 0 (0%), followed by one-repetition maximum and L 0 (4.1%), and highest for A line (5.3%); and (iii) A line derived from mean velocity was the only variable that maintained a significant association with changes in one-repetition maximum across exercises and microcycles (r=0.648–0.765, p≤0.003). These findings indicate that while short mesocycles elicit substantial inter-individual variability in adaptations, A line derived from mean velocity offers a practical, fatigue-free method to monitor meaningful maximal strength changes throughout a training cycle.

This study aimed to validate the feasibility of a repetition-based method for adjusting inter-set rest intervals during superset resistance training (RT). Twenty young adults completed three protocols—fixed (3-min rest), self-selected (participant-determined rest), and repetition-based (rest adjusted by prior repetition count)—in randomized order, performing five agonist–antagonist supersets (bench press and bench pull) at 75% 1RM. Results indicated greater repetition volume in later supersets for the self-selected and repetition-based protocols compared to the fixed protocol (F = 2.1, p = 0.037, ωp ² = 0.056). However, blood lactate concentrations were significantly higher in the repetition-based protocol compared to both the self-selected and fixed protocols (F = 7.3, p = 0.002, ωp ² = 0.252). No differences among protocols were found in fastest set velocity or perceived exertion (F = 0.3–2.8, p = 0.077–0.782). Regarding time efficiency, the fixed protocol was most efficient, followed by the repetition-based, with the self-selected protocol being least efficient. These findings indicate that the repetition-based approach offers an effective complementary strategy to traditional fixed and self-selected rest methods, especially in contexts emphasizing mechanical performance and session efficiency.

This study examined the reliability and accuracy of various velocity-based methods for predicting the snatch one-repetition maximum (1RM), with the purpose of evaluating the potential of movement velocity as an objective indicator for attempt selection in competitive weightlifting. Fourteen competitive adolescent male weightlifters (age: 15.9 ± 0.9 years; training experience: 5.1 ± 0.9 years) completed two testing sessions, each involving an incremental loading test with attempts at 50%, 70%, 80%, and 90% of their best snatch record from the past 30 days, followed by load increases until reaching their actual 1RM. Peak velocity (PV) was recorded for all lifts with a linear position transducer. The 1RM in the second session was predicted using the load-PV relationship derived from four loads, combined with either the actual or optimal minimal velocity threshold (MVT) obtained in the first session. Additionally, 1RM was estimated from PV recorded at single loads (50%, 70%, 80%, and 90% 1RM), using the individual %1RM-PV relationship established during the first session. Acceptable between-sessions reliability was observed for the actual 1RM, PV tested at single loads (50-90% 1RM), actual MVT, and optimal MVT (intraclass correlation coefficient = 0.76-0.90, coefficient of variation = 1.82-3.31%). The actual MVT, optimal MVT and individual %1RM-PV relationship using 80% and 90% 1RM yielded acceptable and lower absolute errors (2.6-4.1 kg) compared to individual %1RM-PV relationship using 50% and 70% 1RM (6.2-9.9 kg). However, these methods exhibited proportional bias (p = 0.002-0.018). Furthermore, heteroscedasticity was observed for the actual MVT and 90% 1RM methods (p = 0.022-0.026). These results suggest that recording PV during warm-up sets prior to competition may serve as a complementary variable to support and refine opener selection in weightlifting competitions. However, weightlifting coaches should use this approach with caution, as it may not provide accurate snatch performance predictions for all athletes.

This study aimed to investigate the feasibility of using deadlift load-velocity (L-V) relationship variables, specifically the load-intercept (L0), to monitor resistance training intensity. Fifteen well-trained male and fifteen well-trained female athletes completed two incremental load tests, recording movement mean velocity (MV) until reaching one repetition maximum (1RM) in two sessions. Although L0 (CV = 4.98%, ICC = 0.974) demonstrated lower between-session reliability than 1RM (CV = 3.48%, ICC = 0.989), its reliability was still at an acceptable level. Furthermore, the 1RM/L0 ratio showed acceptable between-subjects variability (CV = 6.39%). Consequently, L0 could serve as an alternative reference for prescribing training intensity in place of the 1RM. Both the %1RM-MV and %L0-MV relationships were found to be valid for monitoring training intensity in the high-intensity range (absolute error ≤ 4.05%, at around 80% and 90%1RM) but not in the low-intensity range (absolute error ≥ 6.31%, from 40% to 70%1RM). Although not a complete replacement for the 1RM, the %L0 - MV relationship still offers a practical and convenient method for monitoring deadlift training in high-intensity range (above 80%1RM), particularly in settings where frequent assessments are required.

Introduction: The purpose of this study was to assess whether bench press load-velocity (L-V) relationship variables serving as indicators of maximal theoretical force capacity ( L ₀ ), maximal theoretical velocity capacity ( v ₀ ), and maximal theoretical power capacity (A _line ), as well as the softball-specific performance metrics (hit and throw distance), could be used to effectively monitor the selective fatigue induced by two different bench press training protocols. Methods: The bench press L-V relationship variables and softball-specific performance metrics of 12 professional female softball players were measured on three separate occasions: (I) following passive rest (non-fatigue condition), (II) after light-load ballistic bench press throw (LLB), and (III) after heavy-load traditional bench press (HLT). Additionally, blood lactate and rating of perceived exertion (RPE) were assessed after LLB and HLT training protocols. Results: A significantly lower v ₀ , A _line and hit distance were found after both training protocols ( p ≤ 0.008), with the LLB protocol revealing a higher fatigue compared with the HLT protocol. However, the change of L-V relationship variables and softball-specific performance metrics (0.15 ≤ ES ≤ 1.05) were not as sensitive as that of blood lactate and RPE (1.30 ≤ ES ≤ 1.78). Discussion: Hence, changes in mechanical performance could be applied as a supplementary monitoring tool to be integrated into athletes’ daily routines, but should not be considered replacements for traditional fatigue indicators.

This study investigated how different weekly resistance training frequencies affect mechanical output and perceived exertion under velocity loss (VL) monitoring when the total number of sets per week was fixed. Fourteen well-trained male subjects participated in a repeated-measures design in which all participants performed three training frequency conditions (four, three, or two sessions per week) in randomized order. Each condition involved 12 total sets of back squats at 80% one repetition maximum (1RM) with a 20% VL, the total training sets evenly distributed across sessions. The results indicated that increasing training frequency allowed for lower perceived exertion (p = 0.005). Furthermore, the frequency of four sessions per week did not compromise velocity output or repetition performed in the following sets for within-session comparison (p ≥ 0.125), whereas the other two frequencies resulted in compromised performance with increasing numbers of sets performed. The frequency of three sessions per week resulted in a significant decrease in the following set, as shown in the number of repetitions performed per set (Nset) (Δ = 8%,p = 0.003) and the average set mean velocity (MVaverage) (Δ = 4%, p = 0.013) in the last set compared with the first set. The frequency of four sessions per week resulted in lower Nset (Δ = 17%), MVaverage (Δ = 8%), and fastest mean velocity (Δ = 7%) and last mean velocity (Δ = 9%) of the set in the last set compared with the first set (0.001 ≤ p ≤ 0.033). All training frequencies allowed adequate recovery from the frequency arrangement and did not impact performance in the subsequent session. Overall, under a fixed number of sets performed weekly, increasing training frequency helped preserve velocity output and repetitions performed within-session when using 80% 1RM and 20% VL, while also reducing perceived exertion. It is recommended that athletes distribute strength training more evenly across the week rather than concentrating it into fewer days when pursuing better mechanical output and lower perceived exertion.