Reliability of the Load-Velocity Relationship Obtained Through Linear and Polynomial Regression Models to Predict the 1-Repetition Maximum Load

Impact of Generalized Versus Individualized Load-Velocity Equations on Velocity-Loss Magnitude in Bench-Press Exercise: Mixed-Model and Equivalence Analysis

PURPOSE

This study analyzed the influence of 2 velocity-based training-load prescription strategies (general vs individual load-velocity equations) on the relationship between the magnitude of velocity loss (VL) and the percentage of repetitions completed in the bench-press exercise.

METHODS

Thirty-five subjects completed 6 sessions consisting of performing the maximum number of repetitions to failure against their 40%, 60%, and 80% of 1-repetition maximum (1RM) in the Smith machine bench-press exercise using generalized and individualized equations to adjust the training load.

RESULTS

A close relationship and acceptable error were observed between percentage of repetitions completed and the percentage of VL reached for the 3 loading magnitudes and the 2 load-prescription strategies studied (R2 from .83 to .94; standard error of the estimate from 7% to 10%). A simple main effect was observed for load and VL thresholds but not for load-prescription strategies. No significant interaction effects were revealed. The 40% and 60% 1RM showed equivalence on data sets and the most regular variation, whereas the 80% 1-repetition maximum load showed no equivalence and more irregular variation.

CONCLUSION

These results suggest that VL is a useful variable to predict percentage of repetitions completed in the bench-press exercise, regardless of the strategy selected to adjust the relative load. However, caution should be taken when using heavy loads.

Random measurement and prediction errors limit the practical relevance of two velocity sensors to estimate the 1RM back squat

Introduction

While maximum strength diagnostics are applied in several sports and rehabilitative settings, dynamic strength capacity has been determined via the one-repetition maximum (1RM) testing for decades. Because the literature concerned several limitations, such as injury risk and limited practical applicability in large populations (e.g., athletic training groups), the strength prediction via the velocity profile has received increasing attention recently. Referring to relative reliability coefficients and inappropriate interpretation of agreement statistics, several previous recommendations neglected systematic and random measurement bias.

Methods

This article explored the random measurement error arising from repeated testing (repeatability) and the agreement between two common sensors (vMaxPro and TENDO) within one repetition, using minimal velocity thresholds as well as the velocity = 0 m/s method. Furthermore, agreement analyses were applied to the estimated and measured 1RM in 25 young elite male soccer athletes.

Results

The results reported repeatability values with an intraclass correlation coefficient (ICC) = 0.66-0.80, which was accompanied by mean absolute (percentage) errors (MAE and MAPE) of up to 0.04-0.22 m/s and ≤7.5%. Agreement between the two sensors within one repetition showed a systematic lower velocity for the vMaxPro device than the Tendo, with ICCs ranging from 0.28 to 0.88, which were accompanied by an MAE/MAPE of ≤0.13 m/s (11%). Almost all estimations systematically over/ underestimated the measured 1RM, with a random scattering between 4.12% and 71.6%, depending on the velocity threshold used.

Discussion

In agreement with most actual reviews, the presented results call for caution when using velocity profiles to estimate strength. Further approaches must be explored to minimize especially the random scattering.

Optimizing resistance training intensity in supportive care for survivors of breast cancer: velocity-based approach in the row exercise

PURPOSE

Resistance training mitigates side effects during and after cancer treatment. To provide a new approach for precisely and safely assessing and prescribing the intensity of resistance training in supportive cancer care, the purpose of this study was to evaluate the load-velocity relationship during the row exercise in women survivors of breast cancer.

METHODS

Twenty women survivors of breast cancer who had undergone surgery and had completed core breast cancer treatment within the previous 10 years completed an incremental loading test until the one repetition maximum (1RM) in the row exercise. The velocity was measured during the concentric phase of each repetition with a linear velocity transducer, and their relationship with the relative load was analyzed by linear and polynomial regression models.

RESULTS

A strong relationship was observed between movement velocity and relative load for all measured velocity variables using linear and polynomial regression models (R2 > 0.90; SEE < 6.00%1RM). The mean velocity and mean propulsive velocity of 1RM was 0.40 ± 0.03 m·s-1, whereas the peak velocity at 1RM was 0.64 ± 0.07 m·s1.

CONCLUSION

In women survivors of breast cancer, monitoring movement velocity during the row exercise can facilitate precise assessment and prescription of resistance training intensity in supportive cancer care.

Sex-related differences in the load-velocity and load-power relationships of the decline bench press exercise

The purpose of this study was to analyse the load-velocity and load-power relationships of the decline bench press exercise (DBPE) and to compare sex-related differences. Twelve young healthy men and women performed a progressive loading test for the determination of 1RM strength and individual load-velocity and load-power relationship in the DBPE. A very close relationship between mean propulsive velocity (MPV) and %1RM was observed (R2 = 0.94). This relationship improved when plotting data separately by sex (R2 = 0.96-97). Individual load-velocity profiles gave an R2 = 0.99 ± 0.01. The relationship between mean propulsive power (MPP) and %1RM was R2 = 0.23. When separating data by sex, R2 = 0.64-73 were obtained. Individual load-power profiles gave an R2 of 0.93 ± 0.07. Significant sex-related differences were found for MPV, with males having faster velocities than females from 30% to 40% 1RM (p = 0.01) and for MPP, with males having greater MPP (W) than females from 30% to 95% 1RM (p < 0.001). The results of this study show that a strong correlation exists between relative load and MPV/MPP in the DBPE, allowing the possibility of using one to predict the other with great precision, especially when a sex-specific equation is used.

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.

Load-Velocity Profile and Active Drag in Young Female Swimmers: An Age-Group Comparison

PURPOSE

The present study aimed to establish differences in load-velocity profiling, active drag (AD), and drag coefficient (Cd) between 3 age groups of female swimmers.

METHODS

Thirty-three swimmers (11, 13, or 16 y old) were recruited. The individual load-velocity profile was determined for the 4 competitive swimming strokes. The maximal velocity (V0), maximal load (L0), L0 normalized to the body mass, AD, and Cd were compared between the groups. A 2-way analysis of variance and correlation analysis were conducted.

RESULTS

Compared with their younger counterparts, 16-year-old swimmers generally had larger V0, L0, and AD, which was particularly evident when comparing them with 11-year-old swimmers (P ≤ .052). The exception was breaststroke, where no differences were observed in L0 and AD and Cd was smaller in the 16-year-old group than the 11-year-old group (P = .03). There was a negative correlation between Cd and V0 for all groups in backstroke (P ≤ .038) and for the 11-year-old group and 13-year-old group in breaststroke (P ≤ .022) and front crawl (P ≤ .010). For the 16-year-old group, large correlations with V0 were observed for L0, L0 normalized to the body mass, and AD (P ≤ .010) in breaststroke and for L0 and AD with V0 in front crawl (P ≤ .042). In butterfly, large negative correlations with V0 were observed in the 13-year-old group for all parameters (P ≤ .027).

CONCLUSIONS

Greater propulsive force is likely the factor that differentiates the oldest age group from the younger groups, except for breaststroke, where a lower Cd (implying a better technique) is evident in the oldest group.

Is two-point method a valid and reliable method to predict 1RM? A systematic review

This systematic review aimed to evaluate the reliability and validity of the two-point method in predicting 1RM compared to the direct method, as well as analyze the factors influencing its accuracy. A comprehensive search of PubMed, Web of Science, Scopus, and SPORTDiscus databases was conducted. Out of the 88 initially identified studies, 16 were selected for full review, and their outcome measures were analyzed. The findings of this review indicated that the two-point method slightly overestimated 1RM (effect size = 0.203 [95%CI: 0.132, 0.275]; P < 0.001); It showed that test-retest reliability was excellent as long as the test loads were chosen reasonably (Large difference between two test loads). However, the reliability of the two-point method needs to be further verified because only three studies have tested its reliability. Factors such as exercise selection, velocity measurement device, and selection of test loads were found to influence the accuracy of predicting 1RM using the two-point method. Additionally, the choice of velocity variable, 1RM determination method, velocity feedback, and state of fatigue were identified as potential influence factors. These results provide valuable insights for practitioners in resistance training and offer directions for future research on the two-point method.

The 2-Point Method: Theoretical Basis, Methodological Considerations, Experimental Support, and Its Application Under Field Conditions

The "2-point method," originally referred to as the "2-load method," was proposed in 2016 by Prof Slobodan Jaric to characterize the maximal mechanical capacities of the muscles to produce force, velocity, and power. Two years later, in 2018, Prof Jaric and I summarized in a review article the scientific evidence showing that the 2-point method, compared with the multiple-point method, is capable of providing the outcomes of the force-velocity (F-V) and load-velocity (L-V) relationships with similar reliability and high concurrent validity. However, a major gap of our review was that, until 2018, the feasibility of the 2-point method had only been explored through testing procedures based on multiple (more than 2) loads. This is problematic because (1) it has misled users into thinking that implementing the 2-point method inevitably requires testing more than 2 conditions and (2) obtaining the data from the same test could have artificially inflated the concurrent validity of the 2-point method. To overcome these limitations, subsequent studies have implemented in separate sessions the 2-point method under field conditions (only 2 different loads applied in the testing protocol) and the standard multiple-point method. These studies consistently demonstrate that while the outcomes of the 2-point method exhibit comparable reliability, they tend to have slightly higher magnitudes compared with the standard multiple-point method. This review article emphasizes the practical aspects that should be considered when applying the 2-point method under field conditions to obtain the main outcomes of the F-V and L-V relationships.

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.

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Repetition velocity as a measure of loading intensity in the free weight and Smith machine Bulgarian split squat

This study investigated the grouped and individualized load-velocity profile (GLVP vs. ILVP) in Bulgarian split squat using Smith machine and free weight. Seventy five recreational male lifters completed two incremental loading tests of Bulgarian split squat. Mean velocity was measured by a linear-position transducer (GymAware). Linear regression equation was applied to construct the GLVP and ILVP. The agreement of predicted %1RM and measured %1RM was assessed by a combination of intraclass correlation coefficient (ICC), coefficient of variation (CV), standard error of measurement (SEM) and Bland-Altman analysis. Acceptable validity was defined as ICC > 0.75, CV ≤ 10% and p ≥ 0.05 (a paired Wilcoxon signed-rank test). A very high level of inverse load-velocity relationships were demonstrated in Bulgarian split squat (r =  - 0.92) with free weights and a Smith machine. ILVP (ICC ≥ 0.98, CV ≤ 8.73%, p ≥ 0.56) was valid enough to predict the %1RM, but GLVP of both limbs revealed large CVs in free weights (CV: 15.4%,15.63%) and a Smith machine (CV: 11.24%, 12.25%). Cross-validation between the actual %1RM and predicted %1RM using free weights and a Smith machine ILVP was not acceptable (p ≤ 0.03, CV ≥ 14.07%). A very high level of inverse relationship were observed between %1RM and MV in Bulgarian split squat using free weights and a Smith machine, indicating individualized load velocity properties, and the ILVP showed high between-devices variability in both scenarios. Using velocity as a measure of loading intensity in Bulgarian split squat needs to consider the individualized load velocity properties, and difference between free weights and a Smith machine.

The fastest repetition in a set predicts the number of repetitions completed to failure during resistance training: The impact of individual characteristics

The aim of this study was to examine the goodness of fit and prediction accuracy of general and individual relationships between the maximum number of repetitions completed in a set (XRM) and the fastest repetition in the set (XRM-velocity relationship) in the free-weight back squat exercise. The effects of sex, training status and history, as well as personality traits, on the goodness of fit and the accuracy of these relationships were also investigated. Forty-six resistance-trained people (15 females and 31 males) performed a one-repetition maximum (1RM) test, and two repetitions to failure (RTF) tests, 72 h apart. RTF tests were performed with 70, 80, and 90% of 1RM with 10 min of inter-set rest. A greater goodness of fit was found for individual XRM-velocity relationships (R² = 0.98; residual standard error [RSE] = 1.01-1.15) compared to general XRM-velocity relationships (R² = 0.45-0.49; RSE = 3.44-3.66). Individual, but not general XRM-velocity relationships established in the first testing session yielded acceptable prediction accuracy (mean error = 1.96 vs 2.81 repetitions) of XRM in the subsequent testing session. Sex, training status and history, and personality traits did not affect the goodness of fit of general and individual XRM-velocity relationships or their prediction accuracy, suggesting the potential generalisability of those findings among resistance-trained populations. Therefore, individual XRM-velocity relationships can be used to prescribe loads matching an intended XRM by recording only the fastest velocity of the set with a given load and predicting, with reasonable accuracy, the XRM for the individual.

Testing Nonlinearity with Rényi and Tsallis Mutual Information with an Application in the EKC Hypothesis

The nature of dependence between random variables has always been the subject of many statistical problems for over a century. Yet today, there is a great deal of research on this topic, especially focusing on the analysis of nonlinearity. Shannon mutual information has been considered to be the most comprehensive measure of dependence for evaluating total dependence, and several methods have been suggested for discerning the linear and nonlinear components of dependence between two variables. We, in this study, propose employing the Rényi and Tsallis mutual information measures for measuring total dependence because of their parametric nature. We first use a residual analysis in order to remove linear dependence between the variables, and then we compare the Rényi and Tsallis mutual information measures of the original data with that the lacking linear component to determine the degree of nonlinearity. A comparison against the values of the Shannon mutual information measure is also provided. Finally, we apply our method to the environmental Kuznets curve (EKC) and demonstrate the validity of the EKC hypothesis for Eastern Asian and Asia-Pacific countries.

Comparison between Olympic Weightlifting Lifts and Derivatives for External Load and Fatigue Monitoring

Load management is an extremely important subject in fatigue control and adaptation processes in almost all sports. In Olympic Weightlifting (OW), two of the load variables are intensity and volume. However, it is not known if all exercises produce fatigue of the same magnitude. Thus, this study aimed to compare the fatigue prompted by the Clean and Jerk and the Snatch and their derivative exercises among male and female participants, respectively. We resorted to an experimental quantitative design in which fatigue was induced in adult individuals with weightlifting experience of at least two years through the execution of a set of 10 of the most used lifts and derivatives in OW (Snatch, Snatch Pull, Muscle Snatch, Power Snatch, and Back Squat; Clean and Jerk, Power Clean, Clean, High Hang Clean, and Hang Power Clean). Intensity and volume between exercises were equalized (four sets of three repetitions), after which one Snatch Pull test was performed where changes in velocity, range of motion, and mean power were assessed as fatigue measures. Nine women and twelve men participated in the study (age, 29.67 ± 5.74 years and 28.17 ± 5.06 years, respectively). The main results showed higher peak velocity values for the Snatch Pull test when compared with Power Snatch (p = 0.008; ES = 0.638), Snatch (p < 0.001; ES = 0.998), Snatch Pull (p < 0.001, ES = 0.906), and Back Squat (p < 0.001; ES = 0.906) while the differences between the Snatch Pull test and the derivatives of Clean and Jerk were almost nonexistent. It is concluded that there were differences in the induction of fatigue between most of the exercises analyzed and, therefore, coaches and athletes could improve the planning of training sessions by accounting for the fatigue induced by each lift.

Momentum-Based Load Prescriptions: Applications to Jump Squat Training

ABSTRACT

Harry, JR, Krzyszkowski, J, Harris, K, Chowning, L, Mackey, E, Bishop, C, and Barker, LA. Momentum-based load prescriptions: Applications to jump squat training. J Strength Cond Res 36(9): 2657-2662, 2022-Velocity-based training is often applied to ballistic exercises, like the barbell jump squat, to improve vertical jump performance. However, determining the ideal training load based on velocity data remains difficult because load prescriptions tend to be limited to subjective velocity loss thresholds, velocity ranges, or both. Using data from jump squats performed with 0, 15, 30, 45, and 60% of the 1-repetition maximum squat, we explored subjective and objective methods to determine the ideal training load. Specifically, we explored takeoff velocity and a related metric only recently discussed in the literature, system momentum (i.e., takeoff velocity multiplied by the mass of the athlete-load system). At the group level, an ideal training load could not be revealed objectively using takeoff velocity. With individual subjects, the process remained challenging using takeoff velocity. Conversely, an ideal training load could be revealed easily and objectively using system momentum at the group average and individual subject levels. System momentum at takeoff is well-suited to assist practitioners seeking to identify appropriate training loads for jump squat training and potentially other ballistic exercises. We suggest a pivot from velocity to system momentum when seeking to objectively establish training loads for the jump squat and related exercises.

Lifting Velocity as a Predictor of the Maximum Number of Repetitions That Can Be Performed to Failure During the Prone Bench Pull Exercise

OBJECTIVE

To explore (1) the goodness of fit of generalized and individualized relationships between the maximum number of repetitions performed to failure (RTF) and the fastest mean velocity and peak velocity of the sets (RTF-velocity relationships), (2) the between-sessions reliability of mean velocity and peak velocity values associated with different RTFs, and (3) whether the errors in the prediction of the RTF under fatigued and nonfatigued conditions differ between generalized and individualized RTF-velocity relationships.

METHODS

Twenty-three sport-science students performed 4 testing sessions with the prone bench pull exercise in a Smith machine: a 1-repetition-maximum [1RM] session, 2 identical sessions consisting of singles sets of RTF against 4 randomized loads (60%-70%-80%-90%1RM), and 1 session consisting of 4 sets of RTF against the 75%1RM.

RESULTS

Individualized RTF-velocity relationships presented a higher goodness of fit (r2 = .96-.97 vs .67-.70) and accuracy (absolute errors = 2.1-2.9 repetitions vs 2.8-4.3 repetitions) in the prediction of the RTF than generalized RTF-velocity relationships. The reliability of the velocity values associated with different RTFs was generally high (average within-subject coefficient of variation = 4.01% for mean velocity and 3.98% for peak velocity). The error in the prediction of the RTF increased by ~1 repetition under fatigue (ie, set 1 vs sets 2-4).

CONCLUSIONS

Individualized RTF-velocity relationships can be used with acceptable precision and reliability to prescribe the loads associated with a given RTF during the match a specific XRM during the prone bench pull exercise, but a lower accuracy is expected in a fatigued state.

Bar Load-Velocity Profile of Full Squat and Bench Press Exercises in Young Recreational Athletes

The purpose of this study was to determine the mean propulsive velocity (MVP) at various percentages of one repetition maximum (1RM) in the full squat and chest press exercises. A total of 96 young women and 256 young men (recreational athletes) performed an incremental test (50−60−70−80% 1RM) comprising the bench press and full squat exercises in two different sessions. The individual load and velocity ratios were established through the MPV. Data were analyzed using SPSS software version 25.0, with the significance level set at 5%. The following findings were revealed: highly linear load-velocity relationships in the group of women (r = 0.806 in the squat, and r = 0.872 in the bench press) and in the group of men (r = 0.832 and r = 0.880, respectively); significant differences (p < 0.001) in the MPV at 50−70−80% 1RM between the bench press and the full squat in men and at 70−80% 1RM in women; and a high variability in the MPV (11.49% to 22.63) in the bench press and full squat (11.58% to 25.15%) was observed in women and men (11.31% to 21.06%, and 9.26% to 24.2%) at the different percentages of 1RM evaluated. These results suggest that the load-velocity ratio in non-strength-trained subjects should be determined individually to more precisely establish the relative load to be used in a full squat and bench press training program.

Validity of Load-Velocity Relationship to Predict 1 Repetition Maximum During Deadlifts Performed With and Without Lifting Straps: The Accuracy of Six Prediction Models

ABSTRACT

Jukic, I, García-Ramos, A, Malecek, J, Omcirk, D, and Tufano, JJ. Validity of load-velocity relationship to predict 1 repetition maximum during deadlifts performed with and without lifting straps: The accuracy of six prediction models. J Strength Cond Res 36(4): 902-910, 2022-This study aimed to compare the accuracy of six 1 repetition maximum (1RM) prediction models during deadlifts performed with (DLw) and without (DLn) lifting straps. In a counterbalanced order, 18 resistance-trained men performed 2 sessions that consisted of an incremental loading test (20-40-60-80-90% of 1RM) followed by 1RM attempts during the DLn (1RM = 162.0 ± 26.9 kg) and DLw (1RM = 179.0 ± 29.9 kg). Predicted 1RMs were calculated by entering both group and individualized mean concentric velocity of the 1RM (V1RM) into an individualized linear and polynomial regression equations, which were derived from the load-velocity relationship of 5 ([20-40-60-80-90% of 1RM], i.e., multiple-point method) or 2 ([40 and 90% of 1RM] i.e., 2-point method) incremental warm-up sets. The predicted 1RMs were deemed highly valid if the following criteria were met: trivial to small effect size, practically perfect r, and low absolute errors (<5 kg). The main findings revealed that although prediction models were more accurate during the DLn than DLw, none of the models provided an accurate estimation of the 1RM during both DLn (r = 0.92-0.98; absolute errors: 6.6-8.1 kg) and DLw (r = 0.80-0.93; absolute errors: 12.4-16.3 kg) according to our criteria. Therefore, these results suggest that the 1RM for both DLn and DLw should not be estimated through the recording of movement velocity if sport professionals are not willing to accept more than 5 kg of absolute errors.

Performance indicators and functional adaptive windows in competitive cyclists: effect of one-year strength and conditioning training programme

Changes and relationships between cycling performance indicators following a one-year strength and conditioning training have not been totally clarified. The aims of this study are to investigate (i) the effect of a combined one-year strength and conditioning training programme on performance indicators and the possible relationships between these indicators, and (ii) the existence of possible endurance-functional-adaptive windows (EFAWs) linked to changes in muscular strength and body composition markers. Functional and lactate threshold power (FTP and LTP), maximal strength (1RM) and body composition (body mass index [BMI], body cell mass [BCM] and phase angle [PA]) were measured at the beginning and the end of a one-year strength and conditioning training programme of thirty cyclists. Correlations, differences, and predictive analysis were performed among parameters. Significant differences were found between pre- and post-conditioning programme results for FTP, LTP, 1RM (p < 0.0001) and BCM (p = 0.038). When expressed as power output (W), FTP and LTP were significantly correlated with 1RM (r = 0.36, p = 0.005 and r = 0.37, p = 0.004, respectively), body mass (r = 0.30 and p = 0.02), BCM (r = 0.68, p < 0.001) and PA (r = 0.42 and 0.39, respectively and p < 0.001). When expressed as W·kg-¹, these power thresholds were strongly correlated with body mass (r = -0.56 and -0.61, respectively) and BMI (r = -0.57 and -0.61 respectively) with p < 0.001. Predictive polynomial regressions revealed possible endurance and strength adaptation zones. The present findings indicated beneficial impacts of one-year strength and conditioning training on cycling performance indicators, confirmed the correlation between performance indicators, and suggested the existence of different EFAWs. Strategies aiming to improve performance should consider cyclist characteristics and performance goals to achieve EFAWs and thereby enhance cycling performance.

Analysis of the Use and Applicability of Different Variables for the Prescription of Relative Intensity in Bench Press Exercise

Simple Summary

The aim of this research is to analyze the different variables that influence the prescription of resistance training (one-repetition maximum (1RM) and number of maximal repetitions (xRM)) through the velocity of execution, with the aim of approaching the precise definition and control of intensity in bench press exercise. Fifty male physical education students were divided into four groups according to their relative strength ratio (RSR) and performed a 1RM bench press test, and two maximum number of repetitions (MNR) tests one week apart, using a relative load corresponding to 70% 1RM determined through the mean propulsive velocity (MPV) obtained from the individual load–velocity relationship. Regarding MPV, the best (fastest) repetition of the set values were similar between groups (0.62 m·s⁻¹–0.64 m·s⁻¹). The average MNR was 12.38 ± 2.51, with significant variation between groups with regards to MNR (CV:13–29%), and greater variability in the group corresponding to the lowest RSR values (CV: 29%). The use of variables such as the 1RM or a MNR do not allow an adequate degree of precision to prescribe and control the relative intensity of resistance training. Besides, execution velocity control can offer an adequate alternative to guarantee an accurate prescription of intensity with regard to resistance training.

Abstract

Background: The aim of the study was to analyze the use of variables such as % of one-repetition maximum (1RM) and number of maximal repetitions (xRM) with execution velocity to define and control the intensity of resistance training in bench press exercise. Hence, exercise professionals will achieve better control of training through a greater understanding of its variables. Methods: In this cross-sectional study, fifty male physical education students were divided into four groups according to their relative strength ratio (RSR) and performed a 1RM bench press test (T1). In the second test, participants performed repetitions to exhaustion (T2), using a relative load corresponding to 70% 1RM determined through the mean propulsive velocity (MPV) obtained from the individual load–velocity relationship. This same test was repeated a week later (T3). Tests were monitored according to the MPV of each repetition and blood lactate values (LACT). Results: Regarding MPV, the best (fastest) repetition of the set (MPVrep Best) values were similar between groups (0.62 m·s⁻¹–0.64 m·s⁻¹), with significant differences in relation to the high RSR group (p < 0.001). The average maximum number of repetitions (MNR) was 12.38 ± 2.51, with no significant differences between the RSR groups. Nonetheless, significant variation existed between groups with regards to MNR (CV: 13–29%), with greater variability in the group corresponding to the lowest RSR values (CV: 29%). The loss of velocity in the MNR test in the different groups was similar (p > 0.05). Average LACT values (5.72 mmol·L⁻¹) showed significant differences between the Medium RSR and Very Low RSR groups. No significant differences were found (p > 0.05) between T2 and T3 with regards to MNR, MPVrep Best, or MPVrep Last, with little variability seen between participants. Conclusions: The use of variables such as the 1RM, estimated using an absolute load value, or an MNR do not allow an adequate degree of precision to prescribe and control the relative intensity of resistance training. Besides, execution velocity control can offer an adequate alternative to guarantee an accurate prescription of intensity with regard to resistance training.

Comparison of movement velocity and force-velocity parameters using a free video analysis software and a linear position transducer during unilateral and bilateral ballistic leg press

Study aim: This study compared movement velocity and force-velocity profile parameters measured by a free video analysis software program, with the use of a high-speed video recording, and a validated linear position transducer (LPT).

Material and methods: Ten team-sports athletes performed double-leg and single-leg ballistic lower limb extensions on a leg press machine against a wide range of resistive loads. Each repetition was recorded by the LPT a high-speed camera (300 fps), and later analysed with a free video analysis software program.

Results: Mean and peak movement velocity presented high reliability (ICC: 0.990 and 0.988, p < 0.001) and agreement between the two measuring systems (systematic bias: –0.06 ± 0.04 and –0.01 ± 0.03 m/s, respectively). Force-velocity profile parameters were also similar: maximum velocity at zero load (Vo: 1.79 ± 0.15 vs. 1.78 ± 0.12 m/s, p = 0.64), slope (b: –1585 ± 503 vs. –1562 ± 438 N · s/m, p = 0.43), maximum force at zero velocity (Fo: 2835 ± 937 vs. 2749 ± 694 N, p = 0.41) and maximum power (1274 ± 451 vs 1214 ± 285 W, p = 0.38). Both measuring systems could similarly detect the individual force or velocity deficit (p=0.91).

Conclusion: In conclusion, a free video analysis software combined with a high-speed camera was shown to be a reliable, accurate, low bias and cost-effective method in velocity-based testing.

Validity of the bench press one-repetition maximum test predicted through individualized load-velocity relationship using different repetition criteria and minimal velocity thresholds

BACKGROUND: More practical and less fatiguing strategies have been developed to accurately predict the one-repetition maximum (1RM). OBJETIVE: To compare the accuracy of the estimation of the free-weight bench press 1RM between six velocity-based 1RM prediction methods. METHODS: Sixteen men performed an incremental loading test until 1RM on two separate occasions. The first session served to determine the minimal velocity threshold (MVT). The second session was used to determine the validity of the six 1RM prediction methods based on 2 repetition criteria (fastest or average velocity) and 3 MVTs (general MVT of 0.17 m⋅s-1, individual MVT of the preliminary session, and individual MVT of the validity session). Five loads (≈ 2540557085% of 1RM) were used to assess the individualized load-velocity relationships. RESULTS: The absolute difference between the actual and predicted 1RM were low (range = 2.7–3.7%) and did not reveal a significant main effect for repetition criterion (P= 0.402), MVT (P= 0.173) or their two-way interaction (P= 0.354). Furthermore, all 1RM prediction methods accurately estimated bench press 1RM (P⩾ 0.556; ES ⩽ 0.02; r⩾ 0.99). CONCLUSIONS: The individualized load-velocity relationship provides an accurate prediction of the 1RM during the free-weight bench press exercise, while the repetition criteria and MVT do not appear to meaningfully affect the prediction accuracy.

Number of Repetitions Performed Before and After Reaching Velocity Loss Thresholds: First Repetition Versus Fastest Repetition-Mean Velocity Versus Peak Velocity

PURPOSE

To explore the effect of several methodological factors on the number of repetitions performed before and after reaching certain velocity loss thresholds (VLTs).

METHOD

Fifteen resistance-trained men (bench press 1-repetition maximum = 1.25 [0.16] kg·kg-1) performed with maximum intent a total of 182 sets (77 short sets [≤12 repetitions] and 105 long sets [>12 repetitions]) leading to failure during the Smith machine bench press exercise. Fifteen percent, 30%, and 45% VLTs were calculated, considering 2 reference repetitions (first and fastest repetitions) and 2 velocity variables (mean velocity [MV] and peak velocity [PV]).

RESULTS

The number of repetitions performed before reaching all VLTs were affected by the reference repetition and velocity variable (P ≤ .001). The fastest MV and PV during the short sets (75.3%) and PV during the long sets (72.4%) were predominantly observed during the first repetition, while the fastest MV during long sets was almost equally distributed between the first (37.1%) and second repetition (40.0%). Failure occurred before reaching the VLTs more frequently using PV (4, 8, and 33 occasions for 15%, 30%, and 45% VLTs, respectively) than MV (only 1 occasion for the 45% VLT). The participants rarely produced a velocity output above a VLT once this threshold was exceeded for the first time (≈10% and 30% of occasions during the short and long sets, respectively).

CONCLUSIONS

The reference repetition and velocity variable are important factors to consider when implementing VLTs during resistance training. The fastest repetition (instead of the first repetition) and MV (instead of PV) are recommended.

A Novel Approach to 1RM Prediction Using the Load-Velocity Profile: A Comparison of Models

The study aim was to compare different predictive models in one repetition maximum (1RM) estimation from load-velocity profile (LVP) data. Fourteen strength-trained men underwent initial 1RMs in the free-weight back squat, followed by two LVPs, over three sessions. Profiles were constructed via a combined method (jump squat (0 load, 30-60% 1RM) + back squat (70-100% 1RM)) or back squat only (0 load, 30-100% 1RM) in 10% increments. Quadratic and linear regression modeling was applied to the data to estimate 80% 1RM (kg) using 80% 1RM mean velocity identified in LVP one as the reference point, with load (kg), then extrapolated to predict 1RM. The 1RM prediction was based on LVP two data and analyzed via analysis of variance, effect size (g/ηp2), Pearson correlation coefficients (r), paired t-tests, standard error of the estimate (SEE), and limits of agreement (LOA). p < 0.05. All models reported systematic bias < 10 kg, r > 0.97, and SEE < 5 kg, however, all linear models were significantly different from measured 1RM (p = 0.015 <0.001). Significant differences were observed between quadratic and linear models for combined (p < 0.001; ηp2 = 0.90) and back squat (p = 0.004, ηp2 = 0.35) methods. Significant differences were observed between exercises when applying linear modeling (p < 0.001, ηp2 = 0.67-0.80), but not quadratic (p = 0.632-0.929, ηp2 = 0.001-0.18). Quadratic modeling employing the combined method rendered the greatest predictive validity. Practitioners should therefore utilize this method when looking to predict daily 1RMs as a means of load autoregulation.

Training for Muscular Strength: Methods for Monitoring and Adjusting Training Intensity

Linear loading, the two-for-two rule, percent of one repetition maximum (1RM), RM zones, rate of perceived exertion (RPE), repetitions in reserve, set-repetition best, autoregulatory progressive resistance exercise (APRE), and velocity-based training (VBT) are all methods of adjusting resistance training intensity. Each method has advantages and disadvantages that strength and conditioning practitioners should be aware of when measuring and monitoring strength characteristics. The linear loading and 2-for-2 methods may be beneficial for novice athletes; however, they may be limited in their capacity to provide athletes with variation and detrimental if used exclusively for long periods of time. The percent of 1RM and RM zone methods may provide athletes with more variation and greater potential for strength-power adaptations; however, they fail to account for daily changes in athlete's performance capabilities. An athlete's daily readiness can be addressed to various extents by both subjective (e.g., RPE, repetitions in reserve, set-repetition best, and APRE) and objective (e.g., VBT) load adjustment methods. Future resistance training monitoring may aim to include a combination of measures that quantify outcome (e.g., velocity, load, time, etc.) with process (e.g., variability, coordination, efficiency, etc.) relevant to the stage of learning or the task being performed. Load adjustment and monitoring methods should be used to supplement and guide the practitioner, quantify what the practitioner 'sees', and provide longitudinal data to assist in reviewing athlete development and providing baselines for the rate of expected development in resistance training when an athlete returns to sport from injury or large training load reductions.

The Implementation of Velocity-Based Training Paradigm for Team Sports: Framework, Technologies, Practical Recommendations and Challenges

While velocity-based training is currently a very popular paradigm to designing and monitoring resistance training programs, its implementation remains a challenge in team sports, where there are still some confusion and misinterpretations of its applications. In addition, in contexts with large squads, it is paramount to understand how to best use movement velocity in different exercises in a useful and time-efficient way. This manuscript aims to provide clarifications on the velocity-based training paradigm, movement velocity tracking technologies, assessment procedures and practical recommendations for its application during resistance training sessions, with the purpose of increasing performance, managing fatigue and preventing injuries. Guidelines to combine velocity metrics with subjective scales to prescribe training loads are presented, as well as methods to estimate 1-Repetition Maximum (1RM) on a daily basis using individual load–velocity profiles. Additionally, monitoring strategies to detect and evaluate changes in performance over time are discussed. Finally, limitations regarding the use of velocity of execution tracking devices and metrics such as “muscle power” are commented upon.

Bench Press 1-Repetition Maximum Estimation Through the Individualized Load-Velocity Relationship: Comparison of Different Regression Models and Minimal Velocity Thresholds

PURPOSE

To compare the accuracy of nine 1-repetition maximum (1RM) prediction methods during the paused and touch-and-go bench press exercises performed in a Smith machine.

METHOD

A total of 86 men performed 2 identical sessions (incremental loading test until reaching the 1RM followed by a set to failure) in a randomized order during the paused and touch-and-go bench press exercises. Individualized load-velocity relationships were modeled by linear and polynomial regression models considering 4 loads (45%-60%-75%-90% of 1RM) (multiple-point methods) and considering only 2 loads (45%-90% of 1RM) by a linear regression (2-point method). Three minimal velocity thresholds were used: the general velocity of 0.17 m·s-1 (general velocity of the 1RM [V1RM]), the velocity obtained when lifting the 1RM load (individual V1RM), and the velocity obtained during the last repetition of a set to failure.

RESULTS

The 1RM prediction methods were generally valid (range: r = .96-.99, standard error of the estimate = 2.8-4.9 kg or 4.6%-8.0% of 1RM). The multiple-point linear method (2.79 [2.29] kg) was more precise than the multiple-point polynomial method (3.54 [3.31] kg; P = .013), but no significant differences were observed when compared with the 2-point method (3.09 [2.66] kg, P = .136). The velocity of the last repetition of a set to failure (3.47 [2.97] kg) was significantly less precise than the individual V1RM (2.91 [2.75] kg, P = .009) and general V1RM (3.00 [2.65] kg, P = .010).

CONCLUSIONS

Linear regression models and a general minimal velocity threshold of 0.17 m·s-1 should be recommended to obtain a quick and precise estimation of the 1RM during the bench press exercise performed in a Smith machine.

Use of Machine-Learning and Load-Velocity Profiling to Estimate 1-Repetition Maximums for Two Variations of the Bench-Press Exercise

The purpose of the current study was to compare the ability of five different methods to estimate eccentric–concentric and concentric-only bench-press 1RM from load–velocity profile data. Smith machine bench-press tests were performed in an eccentric–concentric (n = 192) and a concentric-only manner (n = 176) while mean concentric velocity was registered using a linear position transducer. Load–velocity profiles were derived from incremental submaximal load (40–80% 1RM) tests. Five different methods were used to calculate 1RM using the slope, intercept, and velocity at 1RM (minimum velocity threshold—MVT) from the load–velocity profiles: calculation with individual MVT, calculation with group average MVT, multilinear regression without MVT, regularized regression without MVT, and an artificial neural network without MVT. Mean average errors for all methods ranged from 2.7 to 3.3 kg. Calculations with individual or group MVT resulted in significant overprediction of eccentric–concentric 1RM (individual MVT: difference = 0.76 kg, p = 0.020, d = 0.17; group MVT: difference = 0.72 kg, p = 0.023, d = 0.17). The multilinear and regularized regression both resulted in the lowest errors and highest correlations. The results demonstrated that bench-press 1RM can be accurately estimated from load–velocity data derived from submaximal loads and without MVT. In addition, results showed that multilinear regression can be used to estimate bench-press 1RM. Collectively, the findings and resulting equations should be helpful for strength and conditioning coaches as they would help estimating 1RM without MVT data.

Effects of Four Different Velocity-Based Training Programming Models on Strength Gains and Physical Performance

ABSTRACT

Riscart-López, J, Rendeiro-Pinho, G, Mil-Homens, P, Costa, RS-d, Loturco, I, Pareja-Blanco, F, and León-Prados, JA. Effects of Four different velocity-based training programming models on strength gains and physical performance. J Strength Cond Res 35(3): 596-603, 2021-The aim of this study was to compare the effects of 4 velocity-based training (VBT) programming models (linear programming [LP], undulating programming [UP], reverse programming [RP], and constant programming [CP]) on the physical performance of moderately strength-trained men. Forty-three young (age: 22.9 ± 4.8 years; body mass [BM]: 71.7 ± 7.6; full squat [SQ] relative strength 1.32 ± 0.29) subjects were randomly assigned to LP (gradually increase training intensity and decrease volume), UP (volume and intensity increase or decrease repeatedly), RP (gradually increases volume and decrease intensity), and CP (maintains constant volume and intensity) groups and followed an 8-week VBT intervention using the SQ exercise and monitoring movement velocity for every repetition. All groups trained with similar relative average intensity (67.5% 1 repetition maximum [1RM]), magnitude of velocity loss within the set (20%), number of sets (3), and interset recoveries (4 minutes) throughout the training program. Pre-training and post-training measurements included predicted SQ (1RM), average velocity attained for all loads common to pre-tests and post-tests (AV), average velocity for those loads that were moved faster (AV > 1) and slower (AV < 1) than 1 m·s-1 at pre-tests, countermovement jump height (CMJ), and 20-m sprint time (T20). No significant group × time interactions were observed for any of the variables analyzed. All groups obtained similar increases (shown in effect size values) in 1RM strength (LP: 0.88; UP: 0.54; RP: 0.62; CP: 0.51), velocity-load-related variables (LP: 0.74-4.15; UP: 0.46-5.04; RP: 0.36-3.71; CP: 0.74-3.23), CMJ height (LP: 0.35; UP: 0.53; RP: 0.49; CP: 0.34), and sprint performance (LP: 0.34; UP: 0.35; RP: 0.32; CP: 0.30). These results suggest that different VBT programming models induced similar physical performance gains in moderately strength-trained subjects.

Concentric-Only Versus Touch-and-Go Bench Press One-Repetition Maximum in Men and Women

BACKGROUND

One-repetition maximum (1RM) tests are time-consuming, and they might not always be logistically possible or warranted due to increased risk of injury when performed incorrectly or by novice athletes. Repetitions-to-failure tests are a widespread method of predicting the 1RM, but its accuracy may be compromised by several factors such as the type of exercise, sex, training history, and the number of repetitions completed in the test.

HYPOTHESIS

The touch-and-go bench press would provide a higher 1RM than the concentric-only bench press for both genders regardless of whether the 1RM was obtained by the direct or repetitions-to-failure method and the error in the 1RM prediction would be positively correlated with the number of repetitions performed to failure and negatively correlated with the 1RM strength and resistance training experience.

STUDY DESIGN

Cross-sectional study.

LEVEL OF EVIDENCE

Level 3.

METHODS

A total of 113 adults (87 men and 26 women) were tested on 2 sessions during the concentric-only and touch-and-go bench press. Each session consisted of an incremental loading test until reaching the 1RM load, followed by a repetitions-to-failure test.

RESULTS

The 1RM was higher for the touch-and-go bench press using both the direct (men, 7.80%; women, 7.62%) and repetitions-to-failure method (men, 8.29%; women, 7.49%). A significant, although small, correlation was observed between the error in the estimation of the 1RM and the number of repetitions performed (r = 0.222; P < 0.01), 1RM strength (r = -0.169; P = 0.01), and resistance training experience (r = -0.136; P = 0.05).

CONCLUSION

The repetitions-to-failure test is a valid method of predicting the 1RM during the concentric-only and touch-and-go bench press variants. However, the accuracy of the prediction could be compromised with weaker and less experienced individuals and if more than 10 repetitions are completed during the repetitions-to-failure test.

CLINICAL RELEVANCE

The repetitions-to-failure test does not require any sophisticated equipment and enables a widespread use in different training environments.

Pooled Versus Individualized Load-Velocity Profiling in the Free-Weight Back Squat and Power Clean

PURPOSE

This study compared pooled against individualized load-velocity profiles (LVPs) in the free-weight back squat and power clean.

METHODS

A total of 10 competitive weightlifters completed baseline 1-repetition maximum assessments in the back squat and power clean. Three incremental LVPs were completed, separated by 48 to 72 hours. Mean and peak velocity were measured via a linear-position transducer (GymAware). Linear and nonlinear (second-order polynomial) regression models were applied to all pooled and individualized LVP data. A combination of coefficient of variation (CV), intraclass correlation coefficient, typical error of measurement, and limits of agreement assessed between-subject variability and within-subject reliability. Acceptable reliability was defined a priori as intraclass correlation coefficient > .7 and CV < 10%.

RESULTS

Very high to practically perfect inverse relationships were evident in the back squat (r = .83-.96) and power clean (r = .83-.89) for both regression models; however, stronger correlations were observed in the individualized LVPs for both exercises (r = .85-.99). Between-subject variability was moderate to large across all relative loads in the back squat (CV = 8.2%-27.8%) but smaller in the power clean (CV = 4.6%-8.5%). The power clean met our criteria for acceptable reliability across all relative loads; however, the back squat revealed large CVs in loads ≥90% of 1-repetition maximum (13.1%-20.5%).

CONCLUSIONS

Evidently, load-velocity characteristics are highly individualized, with acceptable levels of reliability observed in the power clean but not in the back squat (≥90% of 1-repetition maximum). If practitioners want to adopt load-velocity profiling as part of their testing and monitoring procedures, an individualized LVP should be utilized over pooled LVPs.

Monitoring Training Volume Through Maximal Number of Repetitions or Velocity-Based Approach

PURPOSE

This study aimed (1) to analyze the interindividual variability in the maximal number of repetitions (MNR) performed against a given relative load (percentage of 1-repetition maximum [%1RM]) and (2) to examine the relationship between the velocity loss (VL) magnitude and the percentage of completed repetitions with regard to the MNR (%Rep), when the %1RM is based on individual load-velocity relationships.

METHODS

Following an assessment of 1RM strength and individual load-velocity relationships, 14 resistance-trained men completed 5 MNR tests against loads of 50%, 60%, 70%, 80%, and 90% 1RM in the Smith machine bench-press exercise. The relative loads were determined from the individual load-velocity relationship.

RESULTS

Individual relationships between load and velocity displayed coefficients of determination (R2) ranging from .986 to .998. The MNR showed an interindividual coefficient of variation ranging from 8.6% to 33.1%, increasing as the %1RM increased. The relationship between %Rep and the magnitude of VL showed a general R2 of .92 to .94 between 50% and 80% 1RM, which decreased to .80 for 90% 1RM. The mean individual R2 values were between .97 and .99 for all loading conditions. The %Rep when a given percentage of VL was reached showed interindividual coefficient of variation values ranging from 5% to 20%, decreasing as the %Rep increased in each load condition.

CONCLUSIONS

Setting a number of repetitions had acceptable interindividual variability, with moderate relative loads being adjusted based on the individual load-velocity relationship. However, to provide a more homogeneous level of effort between athletes, the VL approach should be considered, mainly when using individual VL-%Rep relationships.

Load-velocity Profiles Change after Training Programs with Different Set Configurations

This study explored the changes in load-velocity relationship of bench press and parallel squat exercises following two programs differing in the set configuration. A randomized controlled trial was carried out in a sample of 39 physically active individuals. Participants were assigned to rest redistribution set configuration, traditional set configuration, or control groups. Over 5 weeks, the experimental groups completed 10 sessions with the 10 repetitions maximum load of both exercises. Rest redistribution sets consisted in 16 sets of 2 repetitions with 60 s of rest between sets, and 5 min between exercises, whereas traditional sets entailed 4 sets of 8 repetitions with 5 min of rest between sets and exercises. The load-velocity relationships of both exercises were obtained before and after the training period. For bench press, an increase of the velocity axis intercept, and a decrease of the slope at post-test were observed in both rest redistribution (p<0.001, G=1.264; p<0.001; G=0.997) and traditional set (p=0.01, G=0.654; p=0.001; G=0.593) groups. For squat, the slope decreased (p<0.001; G=0.588) and the velocity axis intercept increased (p<0.001; G=0.727) only in the rest redistribution group. These results show that rest redistribution sets were particularly efficient for inducing changes in the load-velocity relationship.

Changes in the Load-Velocity Profile Following Power- and Strength-Oriented Resistance-Training Programs

OBJECTIVE

To compare the short-term effect of power- and strength-oriented resistance-training programs on the individualized load-velocity profiles obtained during the squat (SQ) and bench-press (BP) exercises.

METHODS

Thirty physically active men (age = 23.4 [3.5] y; SQ 1-repetition maximum [1RM] = 126.5 [26.7] kg; BP 1RM = 81.6 [16.7] kg) were randomly assigned to a power- (exercises: countermovement jump and BP throw; sets per exercise: 4-6; repetitions per set: 5-6; load: 40% 1RM) or strength-training group (exercises: SQ and BP; sets per exercise: 4-6; repetitions per set: 2-8; load: 70%-90% 1RM). The training program lasted 4 wk (2 sessions/wk). The individualized load-velocity profiles (ie, velocity associated with the 30%-60%-90% 1RM) were assessed before and after training through an incremental loading test during the SQ and BP exercises.

RESULTS

The power-training group moderately increased the velocity associated with the full spectrum of % 1RM for the SQ (effect size [ES] range: 0.70 to 0.93) and with the 30% 1RM for the BP (ES: 0.67), while the strength-training group reported trivial/small changes across the load-velocity spectrum for both the SQ (ES range: 0.00 to 0.35) and BP (ES range: -0.06 to -0.33). The power-training group showed a higher increase in the mean velocity associated with all % 1RM compared with the strength-training group for both the SQ (ES range: 0.54 to 0.63) and BP (ES range: 0.25 to 0.53).

CONCLUSIONS

The individualized load-velocity profile (ie, velocity associated with different % 1RM) of lower-body and upper-body exercises can be modified after a 4-wk resistance-training program.

Effects of velocity loss in the bench press exercise on strength gains, neuromuscular adaptations, and muscle hypertrophy

OBJECTIVE

This study aimed to compare the effects of four velocity-based training (VBT) programs in bench press (BP) between a wide range of velocity loss (VL) thresholds-0% (VL0), 15% (VL15), 25% (VL25), and 50% (VL50)-on strength gains, neuromuscular adaptations, and muscle hypertrophy.

METHODS

Sixty-four resistance-trained young men were randomly assigned into four groups (VL0, VL15, VL25, and VL50) that differed in the VL allowed in each set. Subjects followed a VBT program for 8-weeks using the BP exercise. Before and after the VBT program the following tests were performed: (a) cross-sectional area (CSA) measurements of pectoralis major (PM) muscle; (b) maximal isometric test; (c) progressive loading test; and (d) fatigue test.

RESULTS

Significant group x time interactions were observed for CSA (P < .01) and peak root mean square in PM (peak RMS-PM, P < .05). VL50 showed significantly greater gains in CSA than VL0 (P < .05). Only the VL15 group showed significant increases in peak RMS-PM (P < .01). Moreover, only VL0 showed significant gains in the early rate of force development (RFD, P = .05), while VL25 and VL50 improved in the late RFD (P ≤ .01-.05). No significant group × time interactions were found for any of the dynamic strength variables analyzed, although all groups showed significant improvements in all these parameters.

CONCLUSION

Higher VL thresholds allowed for a greater volume load which maximized muscle hypertrophy, whereas lower VL thresholds evoked positive neuromuscular-related adaptations. No significant differences were found between groups for strength gains, despite the wide differences in the total volume accumulated by each group.

Influence of Grip Width and Anthropometric Characteristics on the Bench-Press Load-Velocity Relationship

PURPOSE

To compare the load-velocity (L-V) relationship between bench-press exercises performed using 4 different grip widths, to determine the association between the anthropometric characteristics and L-V profile, and to explore whether a multiple linear-regression model with movement velocity and subjects' anthropometric characteristics as predictor variables could increase the goodness of fit of the individualized L-V relationship.

METHODS

The individual L-V relationship of 20 men was evaluated by means of an incremental loading test during the bench-press exercise performed on a Smith machine using narrow, medium, wide, and self-selected grip widths. Simple and multiple linear-regression models were performed.

RESULTS

The mean velocity associated with each relative load did not differ among the 4 grip widths (P ≥ .130). Only body height and total arm length were correlated with the mean velocity associated with light and medium loads (r ≥ .464). A slightly higher variance of the velocity attained at each relative load was explained when some anthropometric characteristics were used as predictor variables along with the movement velocity (r2 = .969 [.965-.973]) in comparison with the movement velocity alone (r2 = .966 [.955-.968]). However, the amount of variance explained by the individual L-V relationships was always higher than with the multiple linear-regression models (r2 = .995 [.985-1.000]).

CONCLUSIONS

These results indicate that the individual determination of the L-V relationship using a self-selected grip width could be recommended to monitor relative loads in the Smith machine bench-press exercise.

Movement Velocity as A Measure of Exercise Intensity in Persons with Multiple Sclerosis: A Validity Study

Objectives: This study aims to analyse the validity (agreement between two methods) of the movement propulsive velocity (MPV) as an indicator of relative load in leg press (LP) and bench press (BP) exercises in persons with multiple sclerosis (MS). Methods: 18 persons with MS (sex = 55% male; age (mean ± SD) = 44.88 ± 10.62 years; body mass = 67.19 ± 10.63 kg; height = 1.66 ± 0.07 m; Expanded Disability Status Scale (EDSS) = 3.12 ± 1.73) performed an incremental loading test in BP and LP exercises in two separate sessions. Individual determination of the one-repetition maximum (1RM) and full load-velocity profile were obtained for each participant. Results: a significant linear relationship was observed between the %1RM load and the MPV in LP (%1RM = −133.58 × MPV + 117.44; r² = 0.84; standard error of the estimate (SEE) = 9.38%1RM) and BP (%1RM = −95.66 × MPV + 115.26; r² = 0.86; SEE = 9.82%1RM). In addition, no significant differences were found between the %1RM achieved directly and the %1RM obtained by the equation calculated from the linear regression (LP, p = 0.996; BP, p = 0.749). Conclusions: these results indicate that movement velocity can estimate the relative load in bench press and leg press exercises in persons MS.

Validity of Using Velocity to Estimate Intensity in Resistance Exercises in Men and Women

This study aimed to examine the validity of using bar velocity to estimate relative load in squat and bench-press exercises for both young men and women. Twenty-five men and 25 women performed a progressive loading test up to 1-RM in the squat and bench-press exercises, which were repeated after 2-weeks. Relationships between mean propulsive velocity and%1-RM were analysed. A second-order polynomial equation for predicting the corresponding velocity of each percentage of 1-RM was developed for men (validation). This equation was then applied in women (cross-validation). Moreover, a specific equation for women was developed (validation) and was also applied in a sub-sample of women (cross-validation). Close relationships (R²: 0.91-0.95) between bar velocity and relative load were observed in both sexes for squat and bench press. Men's equation applied to women showed a high level of agreement, although lower bias and higher level of agreement was observed when a sex-specific equation was applied in women, both validation and cross-validation samples. In conclusion, lifting velocity can be used to accurately prescribe the relative load regardless of sex in both upper-body and lower-body exercises, although when estimating load from velocity measures it will be necessary to use the sex-specific equation for each exercise.

Reliability of the velocity achieved during the last repetition of sets to failure and its association with the velocity of the 1-repetition maximum

Background

This study aimed to determine the reliability of the velocity achieved during the last repetition of sets to failure (V_last) and the association of V_last with the velocity of the 1-repetition maximum (V_1RM) during the paused and touch-and-go bench press (BP) exercises performed in a Smith machine.

Methods

A total of 96 healthy men participated in this study that consisted of two testing sessions. A single BP variant (paused BP or touch-and-go BP) was evaluated on each session in a randomized order. Each session consisted of an incremental loading test until reaching the 1RM, followed by two sets of repetitions to failure against a load ranging from 75% to 90% of 1RM.

Results

The reliability of V_last was unacceptable for both BP variants (CV > 18.3%, ICC < 0.60). The correlations between V_1RM and V_last were small for the paused BP (r = 0.18) and moderate for the touch-and-go BP (r = 0.37).

Conclusions

Although these results suggest that V_last could be a better indicator of the minimal velocity threshold than V_1RM, the low reliability of V_last and the similar values of V_last for both BP variants suggest that a standard V_1RM should be used to estimate the 1RM from the individualized load-velocity relationship.

Movement velocity can be used to estimate the relative load during the bench press and leg press exercises in older women

Background

Movement velocity has been proposed as an effective tool to prescribe the load during resistance training in young healthy adults. This study aimed to elucidate whether movement velocity could also be used to estimate the relative load (i.e., % of the one-repetition maximum (1RM)) in older women.

Methods

A total of 22 older women (age = 68.2 ± 3.6 years, bench press 1RM = 22.3 ± 4.7 kg, leg press 1RM = 114.6 ± 15.9 kg) performed an incremental loading test during the free-weight bench press and the leg press exercises on two separate sessions. The mean velocity (MV) was collected with a linear position transducer.

Results

A strong linear relationship between MV and the relative load was observed for the bench press (%1RM = −130.4 MV + 119.3; r² = 0.827, standard error of the estimate (SEE) = 6.10%1RM, p < 0.001) and leg press exercises (%1RM = −158.3 MV + 131.4; r² = 0.913, SEE = 5.63%1RM, p < 0.001). No significant differences were observed between the bench press and leg press exercises for the MV attained against light-medium relative loads (≤70%1RM), while the MV associated with heavy loads (≥80%1RM) was significantly higher for the leg press.

Conclusions

These results suggest that the monitoring of MV could be useful to prescribe the loads during resistance training in older women. However, it should be noted that the MV associated with a given %1RM is significantly lower in older women compared to young healthy individuals.

Reliability of force-velocity parameters obtained from linear and curvilinear regressions for the bench press and squat exercises

This study aimed to compare the goodness of fit and the reliability of different regression models for fitting the force-velocity relationship (FV) of bench press (BP) and squat (SQ). Additionally, the reliability of the position on FV of the velocity (V_1RM) and the force performed with the 1RM (F_1RM) was explored. Nine rugby players and 12 judokas participated in this study. The FV of BP and SQ were obtained twice by a protocol performed until the 1RM. Individual FV were fitted by linear (LM), quadratic polynomial (PM), and exponential models (EM). Adjusted coefficients of determination of LM and PM (medians higher than 0.919) were higher than for EM. The reliability was higher for LM in comparison with PM. The reliability of V_1RM was not acceptable (CV% = 19 and 18% for BP and SQ). High reliability was observed for F_1RM (CV% = 3 and 2% for BP and SQ) and for the ratio between F_1RM and the force-axis intercept of FV (CV% = 2 and 4% for BP and SQ). The reliability of the relative values of F_1RM around 92 and 87% of F₀ for BP and SQ suggests the use of these values for monitoring resistance training programmes.

The load-velocity profiles of three upper-body pushing exercises in men and women

This study aimed to explore the differences in the load-velocity profile between three upper-body pushing exercises in men and women. The load-velocity profiles (from 20% to 100% of the one-repetition maximum [1RM] in 5% increments) of 24 sport sciences students (12 men) were randomly tested during the horizontal bench press (HBP), inclined bench press (IBP) and seated military press (SMP) exercises. The 1RM was higher for the HBP (men: 94.2 ± 14.6 kg; women: 45.4 ± 10.1 kg), followed by the IBP (men: 87.7 ± 11.6 kg; women: 41.6 ± 6.6 kg), and finally the SMP (men: 67.4 ± 7.8 kg; women: 37.3 ± 6.3 kg). The mean test velocity (i.e., averaged velocity of all loads) and the slope of the load-velocity profile were significantly different between exercises (SMP > IBP > HBP) and sexes (men > women). The correlations between the exercises were very high for the individual 1RM values (r range = 0.714-0.982), but lower correlations were observed for the mean test velocity (r range = 0.237-0.766) slope of the load-velocity profile (r range = 0.018-0.721), and velocity of the 1RM (r range = 0.004-0.446). These results confirm that men present higher velocities at low relative loads (i.e., %1RM) compared to women during upper-body pushing exercises.

Reliability and validity of different methods of estimating the one-repetition maximum during the free-weight prone bench pull exercise

This study examined the reliability and validity of three methods of estimating the one-repetition maximum (1RM) during the free-weight prone bench pull exercise. Twenty-six men (22 rowers and four weightlifters) performed an incremental loading test until reaching their 1RM, followed by a set of repetitions-to-failure. Eighteen participants were re-tested to conduct the reliability analysis. The 1RM was estimated through the lifts-to-failure equations proposed by Lombardi and O'Connor, general load-velocity (L-V) relationships proposed by Sánchez-Medina and Loturco and the individual L-V relationships modelled using four (multiple-point method) or only two loads (two-point method). The direct method provided the highest reliability (coefficient of variation [CV] = 2.45% and intraclass correlation coefficient [ICC] = 0.97), followed by the Lombardi's equation (CV = 3.44% and ICC = 0.94), and no meaningful differences were observed between the remaining methods (CV range = 4.95-6.89% and ICC range = 0.81-0.91). The lifts-to-failure equations overestimated the 1RM (3.43-4.08%), the general L-V relationship proposed by Sánchez-Medina underestimated the 1RM (-3.77%), and no significant differences were observed for the remaining prediction methods (-0.40-0.86%). The individual L-V relationship could be recommended as the most accurate method for predicting the 1RM during the free-weight prone bench pull exercise.

Acute effects of different set configurations during a strength-oriented resistance training session on barbell velocity and the force-velocity relationship in resistance-trained males and females

PURPOSE

This study explored the acute effects of strength-oriented resistance training sessions performed using three different set configurations on barbell velocity and the force-velocity (F-v) relationship of upper-body muscles in men and women.

METHOD

Thirteen men (age: 23.8 ± 2.5 years; 6-repetition maximum [6RM] load: 73.4 ± 15.6 kg) and 13 women (age: 21.5 ± 1.4 years; 6RM load: 32.8 ± 5.2 kg) performed 24 repetitions with a 6RM load during the bench press exercise using traditional (TR: 6 sets of 4 repetitions with 3 min of rest between sets), cluster (CL: 6 sets of 4 repetitions with 15 s of intra-set rest every two repetitions and 2 min and 45 s of rest between sets) and inter-repetition rest (IRR: 1 set of 24 repetitions with 39 s of rest between repetitions) set configurations. The F-v relationship parameters [maximum force (F₀), maximum velocity (v₀) and maximum power (P_max)] were determined before and after each training session.

RESULTS

The average training velocity did not differ between the three set configurations (p = 0.234), but the IRR set configuration generally provided higher velocities during the last repetition of each set. Significant decreases in F₀ (p = 0.001) and P_max (p = 0.024) but not in v₀ (p = 0.669) were observed after the training sessions. Comparable velocity loss was observed for men and women (- 12.1% vs. - 11.3%; p = 0.699).

CONCLUSIONS

The administration of very short intra-set rest periods does not allow for the attainment of higher velocities than traditional set configurations during strength-oriented resistance training sessions conducted with the bench press exercise when the work-to-rest ratio is equated.

Assessment of the load-velocity profile in the free-weight prone bench pull exercise through different velocity variables and regression models

This aims of this study were (I) to determine the velocity variable and regression model which best fit the load-velocity relationship during the free-weight prone bench pull exercise, (II) to compare the reliability of the velocity attained at each percentage of the one-repetition maximum (1RM) between different velocity variables and regression models, and (III) to compare the within- and between-subject variability of the velocity attained at each %1RM. Eighteen men (14 rowers and four weightlifters) performed an incremental test during the free-weight prone bench pull exercise in two different sessions. General and individual load-velocity relationships were modelled through three velocity variables (mean velocity [MV], mean propulsive velocity [MPV] and peak velocity [PV]) and two regression models (linear and second-order polynomial). The main findings revealed that (I) the general (Pearson's correlation coefficient [r] range = 0.964–0.973) and individual (median r = 0.986 for MV, 0.989 for MPV, and 0.984 for PV) load-velocity relationships were highly linear, (II) the reliability of the velocity attained at each %1RM did not meaningfully differ between the velocity variables (coefficient of variation [CV] range = 2.55–7.61% for MV, 2.84–7.72% for MPV and 3.50–6.03% for PV) neither between the regression models (CV range = 2.55–7.72% and 2.73–5.25% for the linear and polynomial regressions, respectively), and (III) the within-subject variability of the velocity attained at each %1RM was lower than the between-subject variability for the light-moderate loads. No meaningful differences between the within- and between-subject CVs were observed for the MV of the 1RM trial (6.02% vs. 6.60%; CV_ratio = 1.10), while the within-subject CV was lower for PV (6.36% vs. 7.56%; CV_ratio = 1.19). These results suggest that the individual load-MV relationship should be determined with a linear regression model to obtain the most accurate prescription of the relative load during the free-weight prone bench pull exercise.

Prediction of power output at different running velocities through the two-point method with the Stryd™ power meter

BACKGROUND

The force- and power-velocity (F-V and P-V, respectively) relationships have been extensively studied in recent years. However, its use and application in endurance running events is limited.

RESEARCH QUESTION

This study aimed to determine if the P-V relationship in endurance runners fits a linear model when running at submaximal velocities, as well as to examine the feasibility of the "two-point method" for estimating power values at different running velocities.

METHODS

Eighteen endurance runners performed, on a motorized treadmill, an incremental running protocol to exhaustion. Power output was obtained at each stage with the Stryd™ power meter. The P-V relationship was determined from a multiple-point method (10, 12, 14, and 17 km·h^-1) as well as from three two-point methods based on proximal (10 and 12 km·h^-1), intermediate (10 and 14 km·h^-1) and distal (10 and 17 km·h^-1) velocities.

RESULTS

The P-V relationship was highly linear ( r = 0.999). The ANOVAs revealed significant, although generally trivial (effect size < 0.20), differences between measured and estimated power values at all the velocities tested. Very high correlations ( r = 0.92) were observed between measured and estimated power values from the 4 methods, while only the multiple-point method ( r² = 0.091) and two-point method distal ( r² = 0.092) did not show heteroscedasticity of the error.

SIGNIFICANCE

The two-point method based on distant velocities (i.e., 10 and 17 km·h^-1) is able to provide power output with the same accuracy than the multiple-point method. Therefore, since the two-point method is quicker and less prone to fatigue, we recommend the assessment of power output under only two distant velocities to obtain an accurate estimation of power under a wide range of submaximal running velocities.

Assessment of the loaded squat jump and countermovement jump exercises with a linear velocity transducer: which velocity variable provides the highest reliability?

This study aimed to compare the between-session reliability of three typically measured velocity variables (mean velocity [MV], mean propulsive velocity [MPV], and maximum velocity [Vmax]) to assess vertical jump performance. Totally, 23 men had their squat jump (SJ) and countermovement jump (CMJ) tested against five different loading conditions (17, 30, 45, 60 and 75 kg) during two consecutive weeks. The two sessions of each jump type were performed within the same week separated by 48-72 h. The main finding was a significant difference in reliability between the variables, which were ranked from the highest to the lowest reliable as follows (median coefficient of variation [CV] and range): Vmax (CV = 2.35% [1.85%-3.23%]) >MV (CV = 3.29% [2.18%-4.40%]) >MPV (CV = 3.69% [2.08%-5.17%]). A significant variable × exercise interaction was also observed showing that the differences in reliability between the variables were meaningful during the SJ (MV: CV = 3.93% [3.06%-4.40%], MPV: CV = 4.61% [4.07%-5.17%], and Vmax: CV = 2.14% [1.85%-2.71%]), while no significant differences were observed for the CMJ (MV: CV = 2.43% [2.18%-3.70%], MPV: CV = 2.71% [2.08%-3.63%], and Vmax: CV = 2.40% [1.97%-3.23%]). These results suggest that the Vmax should be the recommended variable for obtaining a reproducible measure of lower-body ballistic performance, especially during the SJ exercise.

Inter-individual variability in the load-velocity relationship is detected by multilevel mixed regression models

The main aim of this study was to explore the variability in the load-velocity relationship through the use of multilevel mixed regression models. The relationship between relative load (% of one repetition maximum: %1RM) and velocity was obtained in a sample of high-level judokas and rugby players (8 women and 13 men) for the bench press (BP) and parallel squat (SQ). The load-velocity relationship for the squat was obtained for the external load (barbell load) and for the system mass (barbell plus body mass). The data were fitted by different multilevel mixed regression models. Including the sex factor in the models improved the goodness of fit for the BP but not for the squat exercises. All the models detected significant inter-individual variability in both intercepts and slopes (p < 0.05 in all the cases). A decrease of 0.15, 0.10 and 0.16 m/s of velocity for each 10% of increment in the relative load were estimated for BP and squat considering the external load and the system mass, respectively. The multilevel mixed regression models detected significant inter-individual variability in the slope and intercept of the load-velocity relationship what entails differences in the velocity associated with a fixed percentage (%) of the one-repetition maximum load.