Individual Load-velocity Profiles Research Articles

Optimal Minimum-Velocity Threshold to Predict One-repetition Maximum for the Back Squat.

The prediction of one-repetition maximum (1RM) is highly relevant for strength and conditioning. The optimal minimum-velocity threshold (MVT) was recently proposed to increase the accuracy of 1RM predictions. Individual load-velocity profiles (LVP) were obtained in 18 athletes enrolled in recreational soccer. Reliability analyses were computed for all LVP-derived variables. Estimations of 1RM were made based on general (0.3 m.s- 1), pre-individual (mean velocity at 1RM obtained in a preliminary session) and optimal MVT (velocity that eliminates the difference between actual and predicted 1RM, determined in a preliminary session). The accuracy of 1RM predictions was examined using absolute-percent error and Bland-Altman plots. Between-day reliability of the LVP and 1RM was good (intraclass-correlation coefficients - ICCs>0.9 and coefficients of variation - CVs<5%). The individual and optimal MVT reached moderate-to-good reliability (ICCs>0.9 and CVs<10%, respectively). The predictions based on the optimal MVT displayed greater accuracy than those obtained with the individual and general MVT (absolute percent error: 2.8 vs. 5.5 vs. 4.9%, respectively). However, wide limits of agreement (LoA) were found between actual and estimated 1RM using this approach (~15 kg). Data indicate that the optimal MVT provides better estimations of 1RM for the free-weight back squat than the general and the individual MVT.

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

ABSTRACT 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 (R 2 = 0.94). This relationship improved when plotting data separately by sex (R 2 = 0.96–97). Individual load-velocity profiles gave an R 2 = 0.99 ± 0.01. The relationship between mean propulsive power (MPP) and %1RM was R 2 = 0.23. When separating data by sex, R 2 = 0.64–73 were obtained. Individual load-power profiles gave an R 2 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.

The effect of high and low velocity-based training on the throwing performance of collegiate handball players.

BackgroundThe intensity of strength training exercise is generally regarded to be the most essential element in developing muscle strength and power. The exercise intensity of strength training is known as one-repetition maximum (1RM). Velocity-based training (VBT) has been proposed as a different approach for determining training intensity. VBT relies on the use of linear position transducers and inertial measurement units, providing real-time feedback to objectively adjust the exercise intensity based on an athlete’s velocity zone.MethodsThis study investigated the effects of two different training interventions based on individualized load velocity profiles (LVP) on maximal bench press strength (i.e., 1RM), maximum throwing velocity (TV), and skeletal muscle mass (SKMM). Twenty-two university handball players were randomly assigned to Group 1 (low-movement speed training) or Group 2 (high-movement speed training). Group 1 exercised with a bar speed of 0.75–0.96 m/s, which corresponds to a resistance of approximately 60% 1RM, whereas Group 2 trained at 1.03–1.20 m/s, corresponding to a resistance of approximately 40% 1RM. Both groups exercised three times a week for five weeks, with strength and throwing tests performed at baseline and post-intervention.ResultsA two-way repeated measures ANOVA was applied, and the results showed the interaction between group and time was not statistically significant for SKMM (p = 0.537), 1RM (p = 0.883), or TV (p = 0.774). However, both groups significantly improved after the five weeks of training: SKMM (3.1% and 3.5%, p < 0.01), 1RM (15.5% and 15.0%, p < 0.01), and throwing velocity (18.7% and 18.3%, p < 0.01) in Group 1 and 2 respectively. Training at both prescribed velocities in this study elicited similar changes in strength, muscle mass, and throwing velocity.

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.

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

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

Changes in sprint performance and sagittal plane kinematics after heavy resisted sprint training in professional soccer players.

BackgroundSprint performance is an essential skill to target within soccer, which can be likely achieved with a variety of methods, including different on-field training options. One such method could be heavy resisted sprint training. However, the effects of such overload on sprint performance and the related kinetic changes are unknown in a professional setting. Another unknown factor is whether violating kinematic specificity via heavy resistance will lead to changes in unloaded sprinting kinematics. We investigated whether heavy resisted sled training (HS) affects sprint performance, kinetics, sagittal plane kinematics, and spatiotemporal parameters in professional male soccer players.MethodsAfter familiarization, a nine-week training protocol and a two-week taper was completed with sprint performance and force-velocity (FV) profiles compared before and after. Out of the two recruited homogenous soccer teams (N = 32, age: 24.1 ± 5.1 years: height: 180 ± 10 cm; body-mass: 76.7 ± 7.7 kg, 30-m split-time: 4.63 ± 0.13 s), one was used as a control group continuing training as normal with no systematic acceleration training (CON, N = 13), while the intervention team was matched into two HS subgroups based on their sprint performance. Subgroup one trained with a resistance that induced a 60% velocity decrement from maximal velocity (N = 10, HS60%) and subgroup two used a 50% velocity decrement resistance (N = 9, HS50%) based on individual load-velocity profiles.ResultsBoth heavy resistance subgroups improved significantly all 10–30-m split times (p < 0.05, d = − 1.25; −0.62). Post-hoc analysis showed that HS50% improved significantly more compared to CON in 0–10-m split-time (d = 1.03) and peak power (d = 1.16). Initial maximal theoretical horizontal force capacity (F0) and sprint FV-sprint profile properties showed a significant moderate relationship with F0 adaptation potential (p < 0.05). No significant differences in sprinting kinematics or spatiotemporal variables were observed that remained under the between-session minimal detectable change.ConclusionWith appropriate coaching, heavy resisted sprint training could be one pragmatic option to assist improvements in sprint performance without adverse changes in sprinting kinematics in professional soccer players. Assessing each player’s initial individual sprint FV-profile may assist in predicting adaptation potential. More studies are needed that compare heavy resisted sprinting in randomized conditions.

Validity and Reliability of Using Load-Velocity Relationship Profiles to Establish Back Squat 1 m·s-1 Load.

Elsworthy, N, Callaghan, DE, Scanlan, AT, Kertesz, AHM, Kean, CO, Dascombe, BJ, and Guy, JH. Validity and reliability of using load-velocity relationship profiles to establish back squat 1 m·s-1 load. J Strength Cond Res 35(2): 340-346, 2021-Although measuring movement velocity during resistance exercise is being increasingly used to monitor player readiness for competition in team sports, the validity and reliability of using set target velocities has not been examined. This study examined test-retest reliability of the load-velocity relationship during the back squat to predict loads corresponding to a mean velocity of 1 m·s-1 (V1Load), test-retest reliability of mean concentric velocity at V1Load, and criterion validity of mean concentric velocity at V1Load. Twenty-seven resistance-trained male rugby league players completed 2 testing sessions on separate days to establish individualized back squat load-velocity relationship profiles (30, 40, 60, and 80% estimated 1 repetition maximum). Velocity during the back squat was assessed at each load and V1Load derived using individualized linear regression equations. A subset of subjects (n = 18) also performed the back squat at predicted V1Load to examine the test-retest reliability and compare the mean concentric velocity with the predicted target of 1 m·s-1. The mean concentric velocity was consistent across all loads during load-velocity relationship testing (p > 0.05, intraclass correlation coefficient [ICC] ≥0.75, coefficient of variation [CV] ≤5.7%, effect size [ES] ≤0.27), and for predicting V1Load (p = 0.11, ICC = 0.95, CV = 3.9%, ES = 0.11). The mean concentric velocity at V1Load was reliable (ICC = 0.77; CV = 2.6%; ES = 0.39) and not significantly different (p = 0.21) to the target velocity, supporting criterion validity. Individualized load-velocity profiles for the back squat can accurately predict V1Load, and subsequent use of V1Load to assess back squat velocity is valid and reliable. Using V1Load to assess changes in back squat velocity may have application in measuring changes in strength and power or readiness to train.

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

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. 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 (2sessions/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. 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). 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.

Superior Changes in Jump, Sprint, and Change-of-Direction Performance but Not Maximal Strength Following 6 Weeks of Velocity-Based Training Compared With 1-Repetition-Maximum Percentage-Based Training.

To compare the effects of velocity-based training (VBT) and 1-repetition-maximum (1RM) percentage-based training (PBT) on changes in strength, loaded countermovement jump (CMJ), and sprint performance. A total of 24 resistance-trained males performed 6 weeks of full-depth free-weight back squats 3 times per week in a daily undulating format, with groups matched for sets and repetitions. The PBT group lifted with fixed relative loads varying from 59% to 85% of preintervention 1RM. The VBT group aimed for a sessional target velocity that was prescribed from pretraining individualized load-velocity profiles. Thus, real-time velocity feedback dictated the VBT set-by-set training load adjustments. Pretraining and posttraining assessments included the 1RM, peak velocity for CMJ at 30%1RM (PV-CMJ), 20-m sprint (including 5 and 10m), and 505 change-of-direction test (COD). The VBT group maintained faster (effect size [ES] = 1.25) training repetitions with less perceived difficulty (ES = 0.72) compared with the PBT group. The VBT group had likely to very likely improvements in the COD (ES = -1.20 to -1.27), 5-m sprint (ES = -1.17), 10-m sprint (ES = -0.93), 1RM (ES = 0.89), and PV-CMJ (ES = 0.79). The PBT group had almost certain improvements in the 1RM (ES = 1.41) and possibly beneficial improvements in the COD (ES = -0.86). Very likely favorable between-groups effects were observed for VBT compared to PBT in the PV-CMJ (ES = 1.81), 5-m sprint (ES = 1.35), and 20-m sprint (ES = 1.27); likely favorable between-groups effects were observed in the 10-m sprint (ES = 1.24) and nondominant-leg COD (ES = 0.96), whereas the dominant-leg COD (ES = 0.67) was possibly favorable. PBT had small (ES = 0.57), but unclear differences for 1RM improvement compared to VBT. Both training methods improved 1RM and COD times, but PBT may be slightly favorable for stronger individuals focusing on maximal strength, whereas VBT was more beneficial for PV-CMJ, sprint, and COD improvements.

Comparison of individual and group-based load-velocity profiling as a means to dictate training load over a 6-week strength and power intervention

ABSTRACT This study compared the effects of dictating load using individual (ILVP) or group (GLVP) load-velocity profiles on lower-body strength and power. Nineteen trained males (23.6 ± 3.7 years) completed a back squat one-repetition maximum (1-RM), load-velocity profiling (LVP), and countermovement (CMJ), static-squat (SSJ) and standing-broad (SBJ) jump tests before and after 6 weeks of resistance training. Participants were randomly assigned to an ILVP, or GLVP intervention with intra-session load dictated through real-time velocity monitoring and prediction of current relative performance using either the participant’s LVP (ILVP) or a LVP based on all participant data (GLVP). Training resulted in significant increases in back squat 1-RM for the ILVP and GLVP group (p < 0.01; 9.7% and 7.2%, respectively), with no group-by-time interaction identified between training groups (p = 0.06). All jump performance significantly increased for the ILVP group (p < 0.01; CMJ: 6.6%; SSJ: 4.6%; SBJ: 6.7%), with only CMJ and SSJ improving for the GLVP group (p < 0.05; 4.3%). Despite no significant group-by-time interaction across all variables, the ILVP intervention induced a greater magnitude of adaptation when compared to a GLVP approach. Additionally, an individualised approach may lead to greater positive transfer to power-based movements, specifically vertical and horizontal jumps.

Kinematic Differences Between the Front and Back Squat and Conventional and Sumo Deadlift.

fferences between the front and back squat and conventional and sumo deadlift. J Strength Cond Res 33(12): 3213-3219, 2019-The average concentric velocity (ACV) of a resistance exercise movement is inversely related to the load lifted. Previous work suggests that different resistance exercises differ in ACV at the same relative load. Currently, there is limited evidence to determine whether the style of exercise (e.g., front or back squat [BS]; sumo-style or conventional-style deadlift) also affects the load-velocity profile or other kinematic variables such as the peak concentric velocity (PCV) and linear displacement (LD). The purpose of this study was to compare the kinematics (ACV, PCV, and LD) between the front squat (FS) and BS as well as between the conventional deadlift (CD) and sumo deadlift (SD). In a randomized order, 24 men and women (22 ± 3 years) performed a 1 repetition maximum (1RM) protocol for the FS, BS, CD, and SD over 4 visits to the laboratory. Barbell kinematics were recorded during all submaximal and maximal repetitions performed during the 1RM protocol using the Open Barbell System. Kinematic data were pooled into categories based on the percentage of the 1RM lifted in 10% increments (e.g., 30-39% 1RM, 40-49% 1RM, etc.) and compared between exercises. Correlations between kinematic data for the FS and BS and for the CD and SD were examined at each relative load. No differences in kinematics were observed between the FS and BS at any load (p > 0.05). However, FS and BS ACV was weakly correlated (r < 0.4) at high (>80% 1RM) loads. Differences in LD were apparent between the SD and CD at all loads (30-100% 1RM) with the SD having a smaller LD compared with the CD (p < 0.05). Average concentric velocity was not different between the SD and CD at the 1RM (0.25 ± 0.09 vs. 0.25 ± 0.06 m·s; p = 0.962) but was different at 80-89% 1RM (0.35 ± 0.08 vs. 0.40 ± 0.07; p = 0.017), 70-79% 1RM (0.41 ± 0.08 vs. 0.46 ± 0.06; p = 0.026), and 40-49% 1RM (0.66 ± 0.09 vs. 0.77 ± 0.08; p < 0.001). In addition, SD and CD ACV values showed no relationships (p > 0.05) at any loads except at the 1RM (r = 0.433; p < 0.05). These results suggest individual load-velocity profiles for the FS and BS as well as for the CD and SD should be used for training purposes.

Sled-Pull Load-Velocity Profiling and Implications for Sprint Training Prescription in Young Male Athletes.

The purpose of this study was to examine the usefulness of individual load–velocity profiles and the between-athlete variation using the decrement in maximal velocity (Vdec) approach to prescribe training loads in resisted sled pulling in young athletes. Seventy high school, team sport, male athletes (age 16.7 ± 0.8 years) were recruited for the study. All participants performed one un-resisted and four resisted sled-pull sprints with incremental resistance of 20% BM. Maximal velocity was measured with a radar gun during each sprint and the load–velocity relationship established for each participant. A subset of 15 participants was used to examine the reliability of sled pulling on three separate occasions. For all individual participants, the load–velocity relationship was highly linear (r > 0.95). The slope of the load–velocity relationship was found to be reliable (coefficient of variation (CV) = 3.1%), with the loads that caused a decrement in velocity of 10, 25, 50, and 75% also found to be reliable (CVs = <5%). However, there was a large between-participant variation (95% confidence intervals (CIs)) in the load that caused a given Vdec, with loads of 14–21% body mass (% BM) causing a Vdec of 10%, 36–53% BM causing a Vdec of 25%, 71–107% BM causing a Vdec of 50%, and 107–160% BM causing a Vdec of 75%. The Vdec method can be reliably used to prescribe sled-pulling loads in young athletes, but practitioners should be aware that the load required to cause a given Vdec is highly individualized.

The Reliability of Individualized Load-Velocity Profiles.

To examine the reliability of peak velocity (PV), mean propulsive velocity (MPV), and mean velocity (MV) in the development of load-velocity profiles (LVP) in the full-depth free-weight back squat performed with maximal concentric effort. Eighteen resistance-trained men performed a baseline 1-repetition maximum (1-RM) back-squat trial and 3 subsequent 1-RM trials used for reliability analyses, with 48-h intervals between trials. 1-RM trials comprised lifts from 6 relative loads including 20%, 40%, 60%, 80%, 90%, and 100% 1-RM. Individualized LVPs for PV, MPV, or MV were derived from loads that were highly reliable based on the following criteria: intraclass correlation coefficient (ICC) >.70, coefficient of variation (CV) ≤10%, and Cohen d effect size (ES) <0.60. PV was highly reliable at all 6 loads. MPV and MV were highly reliable at 20%, 40%, 60%, 80%, and 90% but not 100% 1-RM (MPV: ICC = .66, CV = 18.0%, ES = 0.10, SEM = 0.04m·s-1; MV: ICC = .55, CV = 19.4%, ES = 0.08, SEM = 0.04m·s-1). When considering the reliable ranges, almost perfect correlations were observed for LVPs derived from PV20-100% (r = .91-.93), MPV20-90% (r = .92-.94), and MV20-90% (r = .94-.95). Furthermore, the LVPs were not significantly different (P > .05) between trials or movement velocities or between linear regression versus 2nd-order polynomial fits. PV20-100%, MPV20-90%, and MV20-90% are reliable and can be utilized to develop LVPs using linear regression. Conceptually, LVPs can be used to monitor changes in movement velocity and employed as a method for adjusting sessional training loads according to daily readiness.

Comparison of Different Minimal Velocity Thresholds to Establish Deadlift One Repetition Maximum.

The aim of this study was to compare the actual deadlift one repetition maximum (1RM) and the deadlift 1RM predicted from individualised load-velocity profiles. Twelve moderately resistance-trained men participated in three deadlift sessions. During the first, 1RM was assessed; during the second, load-velocity profiles were recorded with six loads (65% to 90% 1RM) using a linear position transducer recording at 1000 Hz; and during the third, minimal velocity thresholds (MVT) were recorded from the velocity of the last repetition during sets to volitional fatigue with 70% and 80% 1RM with a linear position transducer recording at 1000 Hz. Regression was then used to generate individualised load-velocity profiles and the MVT was used as a cut-off value from which to predict deadlift 1RM. In general, velocity reliability was poor to moderate. More importantly, predicted deadlift 1RMs were significantly and meaningfully less than actual deadlift 1RMs (p < 0.05, d = 1.03–1.75). The main practical application that should be taken from the results of this study is that individualized load-velocity profiles should not be used to predict deadlift 1RM. Practitioners should not use this method in combination with the application of MVT obtained from the last repetition of sets to volitional fatigue.

Differences in the Load-Velocity Profile Between 4 Bench-Press Variants.

To compare the load-velocity relationship between 4 variants of the bench-press (BP) exercise. The full load-velocity relationship of 30 men was evaluated by means of an incremental loading test starting at 17kg and progressing to the individual 1-repetition maximum (1RM) in 4 BP variants: concentric-only BP, concentric-only BP throw (BPT), eccentric-concentric BP, and eccentric-concentric BPT. A strong and fairly linear relationship between mean velocity (MV) and %1RM was observed for the 4 BP variants (r2 > .96 for pooled data and r2 > .98 for individual data). The MV associated with each %1RM was significantly higher in the eccentric-concentric technique than in the concentric-only technique. The only significant difference between the BP and BPT variants was the higher MV with the light to moderate loads (20-70%1RM) in the BPT using the concentric-only technique. MV was significantly and positively correlated between the 4 BP variants (r = .44-.76), which suggests that the subjects with higher velocities for each %1RM in 1 BP variant also tend to have higher velocities for each %1RM in the 3 other BP variants. These results highlight the need for obtaining specific equations for each BP variant and the existence of individual load-velocity profiles.