Using Velocity to Predict the Maximum Dynamic Strength in the Power Clean

Can the Velocity of a 1RM Hang Power Clean Be Used to Estimate a 1RM Hang High Pull?

ABSTRACT

Suchomel, TJ, Techmanski, BS, Kissick, CR, and Comfort, P. Can the velocity of a 1RM hang power clean be used to estimate a 1RM hang high pull? J Strength Cond Res 38(7): 1321-1325, 2024-The purpose of this study was to estimate the 1-repetition maximum hang high pull (1RM HHP) using the peak barbell velocity of a 1RM hang power clean (HPC). Fifteen resistance-trained men (age = 25.5 ± 4.5 years, body mass = 88.3 ± 15.4 kg, height = 176.1 ± 8.5 cm, relative 1RM HPC = 1.3 ± 0.2 kg·kg-1) with previous HPC experience participated in 2 testing sessions that included performing a 1RM HPC and HHP repetitions with 20, 40, 60, and 80% of their 1RM HPC. Peak barbell velocity was measured using a linear position transducer during the 1RM HPC and HHP repetitions performed at each load. The peak barbell velocity achieved during the 1RM HPC was determined as the criterion value for a 1RM performance. Subject-specific linear regression analyses were completed using slope-intercept equations created from the peak velocity of the 1RM HPC and the peak barbell velocities produced at each load during the HHP repetitions. The peak barbell velocity during the 1RM HPC was 1.74 ± 0.30 m·s-1. The average load-velocity profile showed that the estimated 1RM HHP of the subjects was 98.0 ± 19.3% of the 1RM HPC. Although a 1RM HHP value may be estimated using the peak barbell velocity during the HPC, strength and conditioning practitioners should avoid this method because of the considerable variation within the measurement. Additional research examining different methods of load prescription for weightlifting pulling derivatives is needed.

Optimizing exercise prescription during breast cancer rehabilitation in women: Analysis of the load-velocity relationship in the box squat exercise

The aims of this study were to assess (i) the load-velocity relationship during the box squat exercise in women survivors of breast cancer, (ii) which velocity variable (mean velocity [MV], mean propulsive velocity [MPV], or peak velocity [PV]) shows stronger relationship with the relative load (%1RM), and (iii) which regression model (linear [LA] or polynomic [PA]) provides a greater fit for predicting the velocities associated with each %1RM. Nineteen women survivors of breast cancer (age: 53.2 ± 6.9 years, weight: 70.9 ± 13.1 kg, and height: 163.5 ± 7.4 cm) completed an incremental load test up to one-repetition maximum in the box squat exercise. The MV, MPV, and the PV were measured during the concentric phase of each repetition with a linear velocity transducer. These measurements were analyzed by regression models using LA and PA. Strong correlations of MV with %1RM (R2 = 0.903/0.904; the standard error of the estimate (SEE) = 0.05 m.s-1 by LA/PA) and MPV (R2 = 0.900; SEE = 0.06 m.s-1 by LA and PA) were observed. In contrast, PV showed a weaker association with %1RM (R2 = 0.704; SEE = 0.15 m.s-1 by LA and PA). The MV and MPV of 1RM was 0.22 ± 0.04 m·s-1, whereas the PV at 1RM was 0.63 ± 0.18 m.s-1. These findings suggest that the use of MV to prescribe relative loads during resistance training, as well as LA and PA regression models, accurately predicted velocities for each %1RM. Assessing and prescribing resistance exercises during breast cancer rehabilitation can be facilitated through the monitoring of movement velocity.

Minimum velocity threshold in response to the free-weight back squat: reliability and validity of different submaximal loading schemes

PURPOSE

To explore if mean concentric velocity (MCV) of the last repetition before set failure differs between free-weight back squat protocols with greater emphasis on metabolic accumulation vs. mechanical loading. The between-set and between-day reliability of terminal MCV obtained with these different loading schemes was also determined.

METHODS

Fifteen healthy male participants (18-30 years) were included. They all were required to exhibit a relative strength ≥ 1.5 times their body mass. MCVs were obtained at one-repetition maximum (1RM) and with two submaximal protocols (metabolic emphasis: three sets of 40%1RM with blood-flow restriction vs. mechanical emphasis: three sets 80%1RM without blood-flow restriction). Participants were instructed to reach maximal intended concentric velocity in each repetition up to failure.

RESULTS

Set failure was achieved at a faster MCV with the metabolic protocol (p < 0.05). The reliability of MCV at failure reached higher values for the metabolic loading scheme. However, while the MCV achieved at failure during the metabolic protocol was systematically higher than the MCV at 1RM (p < 0.05), this was not entirely the case for the mechanical protocol (similar to 1RM MCV during the last sets in both testing days). Finally, the absolute error derived from estimating the MCV at 1RM based on the MCV obtained at set failure with the mechanical protocol was considerably high (≥ 0.05 m/s).

CONCLUSION

This study indicates that MCV obtained at set failure is dependent on the specificity of the physiological demands of exercise. Thus, MCVs obtained at failure with submaximal loads should not be used to estimate 1RM MCV.

Resistance Training Intensity Prescription Methods Based on Lifting Velocity Monitoring

Resistance training intensity is commonly quantified as the load lifted relative to an individual's maximal dynamic strength. This approach, known as percent-based training, necessitates evaluating the one-repetition maximum (1RM) for the core exercises incorporated in a resistance training program. However, a major limitation of rigid percent-based training lies in the demanding nature of directly testing the 1RM from technical, physical, and psychological perspectives. A potential solution that has gained popularity in the last two decades to facilitate the implementation of percent-based training involves the estimation of the 1RM by recording the lifting velocity against submaximal loads. This review examines the three main methods for prescribing relative loads (%1RM) based on lifting velocity monitoring: (i) velocity zones, (ii) generalized load-velocity relationships, and (iii) individualized load-velocity relationships. The article concludes by discussing a number of factors that should be considered for simplifying the testing procedures while maintaining the accuracy of individualized L-V relationships to predict the 1RM and establish the resultant individualized %1RM-velocity relationship: (i) exercise selection, (ii) type of velocity variable, (iii) regression model, (iv) number of loads, (v) location of experimental points on the load-velocity relationship, (vi) minimal velocity threshold, (vii) provision of velocity feedback, and (viii) velocity monitoring device.

Simultaneous Upper- and Lower-Limb Postactivation Performance Enhancement After Clean and Jerk

Studies on postactivation performance enhancement (PAPE) have used different exercises as a conditioning activity to investigate potentiation, but exclusively in upper limbs (UL) or lower (LL) limbs, or contralateral potentiation. A single exercise capable of inducing PAPE in both UL and LL is currently unknown. The present study explored the effect of the clean and jerk (C&J) as a conditioning activity for simultaneously producing PAPE interlimbs at the fourth, seventh, and 12th minutes postintervention. Twelve male weightlifters with 1-repetition maximum (1RM) in the C&J equivalent to ≥1.15 × body mass were randomly submitted to 2 experimental conditions (C&J and control [CON]). The C&J condition consisted of general warm-up (running on a treadmill and self-selected preparatory exercises) and 4 sets of 3 repetitions of C&J with 2 minutes between them (30%1RM, 50%1RM, 65%1RM, and 80%1RM) followed by a countermovement jump and a bench-press throw on a Smith machine after 4, 7, and 12 minutes, to measure the magnitude of PAPE in UL and LL. No previous exercise preceded countermovement-jump and bench-press-throw tests in the CON besides general warm-up. The main finding was that, regardless of time, the C&J resulted in greater height on countermovement jump and Smith machine bench-press throw when compared with the CON, presenting a similar effect size between UL and LL (34.6 [3.9] vs 33.4 [4.1] cm [+3.66%]; P = .038; effect size = 0.30 and 30.3 [4.7] vs 29.0 [5.1] cm [+4.44%]; P = .039; effect size = 0.26), respectively. Thus, C&J can be useful to produce PAPE simultaneously among members.

The Predictive Validity of Individualised Load-Velocity Relationships for Predicting 1RM: A Systematic Review and Individual Participant Data Meta-analysis

BACKGROUND

Load-velocity relationships are commonly used to estimate one-repetition maximums (1RMs). Proponents suggest these estimates can be obtained at high frequencies and assist with manipulating loads according to session-by-session fluctuations. Given their increasing popularity and development of associated technologies, a range of load-velocity approaches have been investigated.

OBJECTIVE

This systematic review and individual participant data (IPD) meta-analysis sought to quantify the predictive validity of individualised load-velocity relationships for the purposes of 1RM prediction.

METHODS

In September 2022, a search of MEDLINE, SPORTDiscus, Web of Science and Scopus was conducted for published research, with Google Scholar, CORE and British Ethos also searched for unpublished research. Studies were eligible if they were written in English, and directly compared a measured and predicted 1RM using load-velocity relationships in the squat, bench press, deadlift, clean or snatch. IPD were obtained through requests to primary authors and through digitisation of in-text plots (e.g. Bland-Altman plots). Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and the review conducted in accordance with PRISMA-IPD guidelines and an a priori protocol. Absolute and scaled standard error of the estimates (SEE/SEE%) were calculated for two-stage aggregate analyses, with bootstrapping performed for sampling variances. Estimates were pooled using three-level hierarchical models with robust 95% confidence intervals (CIs). One-stage analyses were conducted with random intercepts to account for systematic differences across studies and prediction residuals calculated in the absolute scale (kg) and as a percentage of the measured 1RM. Moderator analyses were conducted by including a priori defined categorical variables as fixed effects.

RESULTS

One hundred and thirty-seven models from 26 studies were included with each identified as having low, unclear or high risk of bias. Twenty studies comprising 434 participants provided sufficient data for meta-analyses, with raw data obtained for 8 (32%) studies. Two-stage analyses identified moderate predictive validity [SEE% 9.8, 95% CI 7.4% to 12.2%, with moderator analyses demonstrating limited differences based on the number of loads (β2Loads:>2Loads = 0.006, 95% CI - 1.6 to 1.6%) or the use of individual or group data to determine 1RM velocity thresholds (βGroup:Individualised =  - 0.4, 95% CI - 1.9 to 1.0%)]. One-stage analyses identified that predictions tended to be overestimations (4.5, 95% CI 1.5 to 7.4 kg), which expressed as a percentage of measured 1RM was equal to 3.7 (95% CI 0.5 to 6.9% 1RM). Moderator analyses were consistent with those conducted for two-stage analyses.

CONCLUSIONS

Load-velocity relationships tend to overestimate 1RMs irrespective of the modelling approach selected. On the basis of the findings from this review, practitioners should incorporate direct assessment of 1RM wherever possible. However, load-velocity relationships may still prove useful for general monitoring purposes (e.g. assessing trends across a training cycle), by providing high-frequency estimates of 1RM when direct assessment may not be logistically feasible. Given limited differences in predictions across popular load-velocity approaches, it is recommended that practitioners opting to incorporate this practice select the modelling approach that best suits their practical requirements.

REGISTRATION

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Toward a New Paradigm in Resistance Training by Means of Velocity Monitoring: A Critical and Challenging Narrative

For more than a century, many concepts and several theories and principles pertaining to the goals, organization, methodology and evaluation of the effects of resistance training (RT) have been developed and discussed between coaches and scientists. This cumulative body of knowledge and practices has contributed substantially to the evolution of RT methodology. However, a detailed and rigorous examination of the existing literature reveals many inconsistencies that, unless resolved, could seriously hinder further progress in our field. The purpose of this review is to constructively expose, analyze and discuss a set of anomalies present in the current RT methodology, including: (a) the often inappropriate and misleading terminology used, (b) the need to clarify the aims of RT, (c) the very concept of maximal strength, (d) the control and monitoring of the resistance exercise dose, (e) the existing programming models and (f) the evaluation of training effects. A thorough and unbiased examination of these deficiencies could well lead to the adoption of a revised paradigm for RT. This new paradigm must guarantee a precise knowledge of the loads being applied, the effort they involve and their effects. To the best of our knowledge, currently this can only be achieved by monitoring repetition velocity during training. The main contribution of a velocity-based RT approach is that it provides the necessary information to know the actual training loads that induce a specific effect in each athlete. The correct adoption of this revised paradigm will provide coaches and strength and conditioning professionals with accurate and objective information concerning the applied load (relative load, level of effort and training effect). This knowledge is essential to make rational and informed decisions and to improve the training methodology itself.

Predictive Validity of the Snatch Pull Force-Velocity Profile to Determine the Snatch One Repetition-Maximum in Male and Female Elite Weightlifters

BACKGROUND

The prediction of one repetition-maximum (1RM) performance from specific tests is highly relevant for the monitoring of training in weightlifting. Therefore, this study aimed at examining the predictive validity of the theoretical 1RM snatch (snatch_th) computed from the two-point snatch pull force-velocity relationship (FvR₂) to determine actual snatch 1RM performance in elite weightlifters.

METHODS

Eight (three female, five male) elite weightlifters carried out a 1RM snatch test followed by a snatch pull test with loads of 80% and 110% of the previously determined 1RM snatch. Barbell kinematics were determined for all lifts using video-tracking. From the snatch pull barbell kinematics, the snatch pull FvR₂ was modeled and the snatch_th was calculated.

RESULTS

The main findings indicated a non-significant (p = 0.706) and trivial (d = 0.01) mean difference between the actual 1RM snatch performance and the snatch_th. Both measures showed an extremely large correlation (r = 0.99). The prediction accuracy of the actual 1RM snatch from snatch_th was 0.2 ± 1.5 kg (systematic bias ± standard deviation of differences).

CONCLUSIONS

This study provides a new approach to estimate 1RM snatch performance in elite weightlifters using the snatch pull FvR₂. The results demonstrate that the snatch_th-model accurately predicts 1RM snatch performance.