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Abstract

Velocity-based training (VBT) is gaining popularity in strength and conditioning due to multiple practical advantages for auto-regulating and individualizing training volume and load on a day-to-day basis. Because the load-velocity relationship varies among exercises, the knowledge of particular equations is indispensable to effectively implement the VBT. The aim of this study was to determine the complete load- and power-velocity profile of the deadlift exercise to provide practical equations and normative values for resistance training coaches and practitioners. Twenty strength-trained men performed a progressive loading test at maximal intended velocity to determine their one-repetition maximum (1RM). Mean (MV), mean propulsive (MPV) and peak velocity (PV) were measured during the concentric phase. Both MV and MPV showed a very close relationship to %1RM (R² = 0.971 and R² = 0.963) with a low error of estimation (SEE = 0.08 and 0.09 m·s⁻¹), which was maintained throughout the wide breadth of velocities. PV showed the poorest results (R² = 0.958, SEE = 0.15 m·s⁻¹). MV attained with the 1RM was 0.24±0.03 m·s⁻¹ and consistent between participants with different relative strengths. The load that maximized the power output was identified at ∼60% 1RM. In contrast to what was observed in velocity, power outcomes showed poor predictive capacity to estimate %1RM. Hence, the use of velocity-based equations is advisable to monitor athletes’ performance and adjust the training load in the deadlift exercise. This finding provides an alternative to the demanding, time-consuming and interfering 1RM tests, and allows the use of the deadlift exercise following the VBT principles.

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... Nevertheless, their regular assessment is not always possible due to logistical and time constraints . Accordingly, the use of the load-velocity relationship has been proposed as a feasible approach to estimating with high precision the exercise load during various resistance exercises (Conceição et al., 2016;Gantois et al., 2022;González-Badillo & Sánchez-Medina, 2010;Morán-Navarro et al., 2021). Previous studies, including the conventional deadlift, found very strong load-velocity relationships (R 2 ≥ .91; ...
... Previous studies, including the conventional deadlift, found very strong load-velocity relationships (R 2 ≥ .91; Benavides-Ubric et al., 2020;Morán-Navarro et al., 2021), supporting the use of the bar velocity as an accurate and feasible approach to prescribing and adjusting daily exercise loads. ...
... To address the research gaps mentioned above, we sought to investigate the accuracy of the load-velocity relationship in estimating 1RM in the HBD exercise in resistance-trained women. It was hypothesized that the 1RM would be predicted with high accuracy in this exercise due to the very strong relationship between bar velocity and submaximal loads in resistance exercise (Conceição et al., 2016;Gantois et al., 2022;Morán-Navarro et al., 2021). ...
Article
In this study, we examined the load-velocity relationship in the hexagonal bar deadlift exercise in women. Twenty-seven resistance-trained women were recruited. Participants performed a progressive load test up to the one-repetition maximum (1RM) load for determining the individual load-velocity relationship in the hexagonal bar deadlift exercise. Bar velocity was measured in every repetition through a linear encoder. A very strong and negative relationship was found between the %1RM and bar velocity for the linear (R 2 = .94; standard error of the estimation = 5.43% 1RM) and second-order polynomial (R 2 = .95) regression models. The individual load-velocity relationship provided even better adjustments (R 2 = .98; coefficient of variation = 1.77%) than the general equation. High agreement level and low bias were found between actual and predicted 1RM for the general load-velocity relationship (intraclass correlation coefficient = .97 and 95% confidence interval [0.90, 0.99]; bias = −2.59 kg). In conclusion, bar velocity can be used to predict 1RM with high accuracy during hexagonal bar deadlift exercise in resistance-trained women.
... Another important application of measuring bar velocity is to determine the range of loads able to maximize power output through the load-power relationship [10,17], defined as the "optimum power zone" [22]. There is evidence supporting the effectiveness of training in the "optimum power zone" to enhance strength performance [22,23]. ...
... Given the above, the aims of this study were: (i) to determine the accuracy of movement velocity to estimate 1RM through the general and individual load-velocity relationship in the free-weight BSQ and HBD exercises; and (ii) to compare the load-velocity and loadpower relationship of both exercises in resistance-trained males. Given the very strong load-velocity relationship previously reported [6,16,17], it was hypothesized that movement velocity would accurately predict 1RM for both exercises, but the load-velocity and load-power relationships would be exercise-dependent. ...
... However, loads in the range 40-80% (BSQ) and 50-70% 1RM (HBD) did not differ statistically concerning P max (Figure 3). These data strongly support previous studies showing that mechanical power output is quite similar across a range of light-moderate loads in other resistance exercises such as the bench press (20-60% 1RM) [37], bench pull (20-70% 1RM) (37), traditional deadlift (40-80% 1RM) [17], and half-BSQ (25-85% 1RM) [10]. These findings raise some questions about how much attention has been given to determining a single "optimal load" [24,37,38]. ...
Article
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The aim of this study was to analyse the load-velocity and load-power relationships in the free-weight back-squat (BSQ) and hexagonal bar deadlift (HBD) exercises. Twenty-five (n = 25) resistance-trained men (age = 23.7 ± 2.8 years) performed a progressive load test at maximal intended velocity to determine their BSQ and HBD one-repetition maximum (1RM). Mean propulsive velocity (MPV) during the concentric phase of the lift was recorded through a linear encoder. Load-velocity and load-power relationships were analysed by fitting linear regression and the second-order polynomial, respectively, to the data. Maximum strength (1RM), MPV (30–80% 1RM), and power output (30–90% 1RM) were higher for HBD compared to BSQ exercise (p < 0.05). A very strong relationship between MPV and relative intensity was found for both BSQ (R2 = 0.963) and HBD (R2 = 0.967) exercises. The load that maximizes power output (Pmax) was 64.6 ± 2.9% (BSQ) and 59.6 ± 1.1% (HBD) 1RM. There was a range of loads at which power output was not different than Pmax (BSQ: 40–80% 1RM; HBD: 50–70% 1RM). In conclusion, the load-velocity and load-power relationships might assist strength and conditioning coaches to monitor and prescribe exercise intensity in the BSQ and HBD exercises using the velocity-based training approach.
... Recently, investigators have focused on assessing the loadvelocity profile during the SBD (2,14,17,19) and reported very strong relationships (R 2 $ 0.91, p , 0.05) between these variables. Moreover, the load-velocity profile during the SBD has shown to be very stable, despite the relative strength level (2,17). ...
... Recently, investigators have focused on assessing the loadvelocity profile during the SBD (2,14,17,19) and reported very strong relationships (R 2 $ 0.91, p , 0.05) between these variables. Moreover, the load-velocity profile during the SBD has shown to be very stable, despite the relative strength level (2,17). However, the biomechanical stimulus may be altered by the utilization of different variations of the deadlift (22). ...
... It has been reported that there is no difference in linear functions and second-order polynomial fits (1). However, some studies have shown that nonlinear functions can provide better fits than linear functions (9,17,21). Individualized fitting second-order polynomials equation with each load and MV was also used to predict the %1RM estimate. The individualized MV at 100% 1RM was used to predict the 1RM value. ...
Article
The aim of this study was to determine if bar velocity can be used to estimate the one-repetition maximum (1RM) on the hexagonal bar deadlift. Twenty-two NCAA Division I male ice hockey players (age= 21.0 ± 1.5 yrs, height= 182.9 ± 7.3 cm, body mass= 86.2 ± 7.3 kg) completed a progressive loading test using the hexagonal bar deadlift at maximum intended velocity to determine their 1RM. Mean concentric velocity (MV) was measured for each load via a linear position transducer. The a-priori alpha level of significance was set at p = 0.05. MV showed a very strong relationship to %1RM (R2 = 0.85). A non-significant difference and a trivial effect size (ES) were observed between actual and predicted 1RM (p = 0.90, ES = -0.08). Near-perfect correlations were also discovered between actual and predicted 1RM (R = 0.93) with low typical error and coefficient of variation (5.11 kg, 2.53%, respectively). The current study presented results that add the HBD to the list of exercises with established load-velocity relationships. The predictive ability for 1RM HBD indicates that this is a viable means of prediction of 1RM.
... To the best of our knowledge, the best way that currently exists to solve these problems resides in the use and monitoring of movement velocity during RT for determining both the relative load used and the degree of effort undertaken [5,9,42,65]. In this regard, very close relationships between movement velocity and relative load (%1RM) have been found for exercises such as the bench press [42,[70][71][72][73][74][75], prone bench pull [54,76], squat [52,55,72], deadlift [58,77], pull-up [78,79], leg press [43] and hip thrust [80], which makes it possible to determine with considerable precision the %1RM that is being used as soon as the first repetition of a set is performed with maximal intended velocity [42]. This is based on the finding that each percentage of the 1RM has its own corresponding mean velocity, and the velocity values associated with each percentage of 1RM have been found to be very stable and reliable, regardless of the subjects' performance level or the change in strength performance after a training period [42,52,54,58,78]. ...
... Monitoring movement velocity allows us to determine the relative load (%1RM) that is being used as soon as the first (fastest) repetition against a given absolute load (kg) is performed at maximal intended velocity [5,42,52,54,58,[76][77][78]. This is based on the finding that each percentage of the 1RM has its own mean velocity [5,42,52,54]. ...
Article
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For more than a century, many concepts and several theories and principles pertaining to the goals, organization, methodology and evaluation of the effects of resistance training (RT) have been developed and discussed between coaches and scientists. This cumulative body of knowledge and practices has contributed substantially to the evolution of RT methodology. However, a detailed and rigorous examination of the existing literature reveals many inconsistencies that, unless resolved, could seriously hinder further progress in our field. The purpose of this review is to constructively expose, analyze and discuss a set of anomalies present in the current RT methodology, including: (a) the often inappropriate and misleading terminology used, (b) the need to clarify the aims of RT, (c) the very concept of maximal strength, (d) the control and monitoring of the resistance exercise dose, (e) the existing programming models and (f) the evaluation of training effects. A thorough and unbiased examination of these deficiencies could well lead to the adoption of a revised paradigm for RT. This new paradigm must guarantee a precise knowledge of the loads being applied, the effort they involve and their effects. To the best of our knowledge, currently this can only be achieved by monitoring repetition velocity during training. The main contribution of a velocity-based RT approach is that it provides the necessary information to know the actual training loads that induce a specific effect in each athlete. The correct adoption of this revised paradigm will provide coaches and strength and conditioning professionals with accurate and objective information concerning the applied load (relative load, level of effort and training effect). This knowledge is essential to make rational and informed decisions and to improve the training methodology itself.
... To be effectively implemented, VBT requires the athlete to lift the load at maximal intended velocity in order to describe the load-velocity relationship (i.e., the velocity attained along a spectrum of loads) for a given exercise [2,3]. Then, practitioners can determine the load-velocity relationship for a given exercise by regression analysis equations (i.e., load-velocity curve) and precisely estimate the relative intensity (%1RM) associated with the resulting velocity [4][5][6][7]. This information has relevant practical implications, mainly individualizing training prescription and load monitoring on a day-to-day basis using velocity monitoring systems [5,8,9]. ...
... The eccentric phase was performed at a controlled MPV training intervention [5,18], other authors reported modifications of this relationship after a period of training [15]. Moreover, it has been suggested that the velocity value attained at each %1RM (i.e., load-velocity curve) does not present significant differences between subjects with different strength levels, for instance, between athletes with high and low relative strength ratio (RSR = 1RM weight lifted/body mass) [5][6][7]18]. Therefore, extending the knowledge about the influence of a resistance training period or the strength level on the stability of the SP load-velocity relationship is deemed necessary. ...
Article
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The purpose of this study was threefold: i) to analyse the load-velocity relationship of the shoulder press (SP) exercise, ii) to investigate the stability (intra-individual variability) of this load-velocity relationship for athletes with different relative strength levels, and after a 10-week velocity-based resistance training (VBT), and iii) to describe the velocity-time pattern of the SP: first peak velocity [Vmax1], minimum velocity [Vmin], and second peak velocity [Vmax2]. This study involves a cross-sectional (T1, n = 48 subjects with low, medium and high strength levels) and longitudinal (T2, n = 24 subjects randomly selected from T1 sample) design. In T1, subjects completed a progressive loading test up to the 1RM in the SP exercise. The barbell mean, peak and mean propulsive velocities (MV, PV and MPV) were monitored. In T2, subjects repeated the loading test after 10 weeks of VBT. There were very close relationships between the %1RM and velocity attained in the three velocity outcomes (T1, R2 : MV = 0.970; MPV = 0.969; PV = 0.954), being even stronger at the individual level (T1, R2 = 0.973–0.997). The MPV attained at the 1RM (~0.19 m·s-1) was consistent among different strength levels. Despite the fact that 1RM increased ~17.5% after the VBT programme, average MPV along the load-velocity relationship remained unaltered between T1 and T2 (0.69 ± 0.06 vs. 0.70 ± 0.06 m·s-1). Lastly, the three key parameters of the velocity-time curve were detected from loads > 74.9% 1RM at 14.3% (Vmax1), 46.1% (Vmin), and 88.7% (Vmax2) of the concentric phase. These results may serve as a practical guideline to effectively implement the velocity-based method in the SP exercise.
... Load-velocity relationships have been extensively studied for exercises such as the bench press [16][17][18][19][20][21][22][23], squat [17,19,[24][25][26][27], deadlift [19,28,29], bench pull [13,30], shoulder press [11,15,31], hip thrust [17,32] and pullup [33,34]. However, even though the inclined leg press is a recurring exercise for lower-limb strengthening, the load-velocity relationship for this exercise has not been extensively studied. ...
Article
The objectives of this study were threefold: (i) to analyze the load-velocity relationships between mean propulsive velocity (MPV), mean velocity (MV), peak velocity (PV), and relative load during the inclined leg press exercise; (ii) to analyze the differences in the load-velocity relationships between males and females; and (iii) to determine gender-specific predictive equations for loads between 50%-100% one-repetition maximum (1RM) in a population of trained young college students. The load-velocity relationships of 15 males and 13 females were explored through a progressive loading test, up to the individual 1RM load. Gender-specific load-velocity relationships were plotted along with the individual relationships. High to very high associations were found for gender-specific load-MPV and load-MV relationships , whereas load-PV presented moderate associations. The gender-specific load-velocity relationships in males were steeper than in females for MPV, MV and PV. However, individual load-velocity relationships presented higher associations than gender-specific relationships for all subjects. Finally, the predicted velocity outcomes for each %1RM load were always significantly higher in males than in females, except for PV at 95% and 100% 1RM load. Taken collectively, the findings from the present study support the application of subject-specific and gender-specific load-velocity relationships, highlighting the disparities between male and female relationships.
Article
Purpose: To compare the strength and athletic adaptations induced by 4 programming models. Methods: Fifty-two men were allocated into 1 of the following models: linear programming (intensity increased while intraset volume decreased), undulating programming (intensity and intraset volume were varied in each session or set of sessions), reverse programming (intensity decreased while intraset volume increased), or constant programming (intensity and intraset volume kept constant throughout the training plan). All groups completed a 10-week resistance-training program made up of the free-weight bench press, squat, deadlift, prone bench pull, and shoulder press exercises. The 4 models used the same frequency (2 sessions per week), number of sets (3 per exercise), interset recoveries (4 min), and average intensity throughout the intervention (77.5%). The velocity-based method was used to accurately adjust the planned intensity for each model. Results: The 4 programming models exhibited significant pre-post changes in most strength variables analyzed. When considering the effect sizes for the 5 exercises trained, we observed that the undulating programming (mean effect size = 0.88-2.92) and constant programming (mean effect size = 0.61-1.65) models induced the highest and lowest strength enhancements, respectively. Moreover, the 4 programming models were found to be effective to improve performance during shorter (jump and sprint tests) and longer (upper- and lower-limb Wingate test) anaerobic tasks, with no significant differences between them. Conclusion: The linear, undulating, reverse, and constant programming models are similarly effective to improve strength and athletic performance when they are implemented in a real-context routine.
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Purpose: To compare the effects of velocity-based training (VBT) vs percentage-based training (PBT) on strength, speed, and jump performance in academy rugby league players during a 7-wk in-season mesocycle. Methods: A total of 27 rugby league players competing in the Super League U19s Championship were randomized to VBT (n = 12) or PBT (n = 15). Both groups completed a 7-wk resistance-training intervention (2×/wk) that involved the back squat. The PBT group used a fixed load based on a percentage of 1-repetition maximum (1-RM), whereas the VBT group used a modifiable load based on individualized velocity thresholds. Biomechanical and perceptual data were collected during each training session. Back-squat 1-RM, countermovement jump, reactive strength index, sprint times, and back-squat velocity at 40–90% 1-RM were assessed pretraining and posttraining. Results: The PBT group showed likely to most likely improvements in 1-RM strength and reactive strength index, whereas the VBT group showed likely to very likely improvements in 1-RM strength, countermovement jump height, and back-squat velocity at 40% and 60% 1-RM. Sessional velocity and power were most likely greater during VBT compared with PBT (standardized mean differences = 1.8–2.4), while time under tension and perceptual training stress were likely lower (standardized mean differences = 0.49–0.66). The improvement in back-squat velocity at 60% 1-RM was likely greater following VBT compared with PBT (standardized mean difference = 0.50). Conclusion: VBT can be implemented during the competitive season, instead of traditional PBT, to improve training stimuli, decrease training stress, and promote velocity-specific adaptations.
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This study aimed to analyze the agreement between five bar velocity monitoring devices, currently used in resistance training, to determine the most reliable device based on reproducibility (between-device agreement for a given trial) and repeatability (between-trial variation for each device). Seventeen resistance-trained men performed duplicate trials against seven increasing loads (20-30-40-50-60-70-80 kg) while obtaining mean, mean propulsive and peak velocity outcomes in the bench press, full squat and prone bench pull exercises. Measurements were simultaneously registered by two linear velocity transducers (LVT), two linear position transducers (LPT), two optoelectronic camera-based systems (OEC), two smartphone video-based systems (VBS) and one accelerometer (ACC). A comprehensive set of statistics for assessing reliability was used. Magnitude of errors was reported both in absolute (m s⁻¹) and relative terms (%1RM), and included the smallest detectable change (SDC) and maximum errors (MaxError). LVT was the most reliable and sensitive device (SDC 0.02–0.06 m s⁻¹, MaxError 3.4–7.1% 1RM) and the preferred reference to compare with other technologies. OEC and LPT were the second-best alternatives (SDC 0.06–0.11 m s⁻¹), always considering the particular margins of error for each exercise and velocity outcome. ACC and VBS are not recommended given their substantial errors and uncertainty of the measurements (SDC > 0.13 m s⁻¹).
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This study explored the effects of velocity-based training (VBT) on maximal strength and jump height. Sixteen trained males (22.8 ± 4.5 years) completed a countermovement jump test (CMJ), and one repetition maximum (1-RM) assessment on back squat, bench press, strict overhead press, and deadlift, before and after six weeks of resistance training. Participants were assigned to VBT, or percentage-based training (PBT) groups. The VBT group's load was dictated via real-time velocity monitoring, as opposed to pre-testing 1-RM data (PBT). No significant differences were present between groups for pre-testing data (p > 0.05). Training resulted in significant increases (p < 0.05) in maximal strength for back squat (VBT 9%, PBT 8%), bench press (VBT 8%, PBT 4%), strict overhead press (VBT 6%, PBT 6%), and deadlift (VBT 6%). Significant increases in CMJ were witnessed for the VBT group only (5%). A significant interaction effect was witnessed between training groups for bench press (p = 0.004) and CMJ (p = 0.018). Furthermore, for back squat (9%), bench press (6%), and strict overhead press (6%), a significant difference was present between the total volume lifted. The VBT intervention induced favourable adaptations in maximal strength and jump height in trained males when compared to a traditional PBT approach. Interestingly the VBT group achieved these positive outcomes despite a significant reduction in total training volume compared to the PBT group. This has potentially positive implications for the management of fatigue during resistance training.
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Resistance training (RT) is considered the most important method to improve the athlete’s strength and rate of force development (RFD). In the last decade, the importance of monitoring velocity during RT has drastically grown, because of an increased availability of linear position transducers (LPT) and inertial measurement units (IMU). The purpose of this review is to analyze the existing literature on testing techniques and performance strategies used to enhance strength and power performance of elite athletes, by monitoring the velocity of resistance training. The authors focus in particular on the level of effort of resistance training defined by velocity; how the loss of velocity correlates with the degree of fatigue and how it can be used to enhance the performance of competitive athletes; the use of LPT as part of the daily routine of the strength and conditioning programs in competitive sport. It is therefore critical for the sports scientists to have a correct understanding of the basic concepts of the velocity-based training and their application to elite sports. The ultimate goal is to give some indications on the velocity-based resistance training integration in the programs of different sports in the high performance environment
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The aim of the present study was to verify the reliability and validity of using submaximal loads from the load-velocity relationship to predict the actual 1RM in the deadlift. Data from 11 resistance-trained athletes were analyzed performing three 1RM assessments separated by at least three days. Reliability was assessed by comparing predicted 1RMs of session 2 and 3, while for validity purposes predicted 1RMs of session 3 were compared to actual 1RMs of session 2. Mean-concentric velocity at 1RM (v@1RM) was entered in individualized linear-regression equations, derived from the load-velocity relationship for three (20-60, 40-80% and 60-90% of 1RM), four (20-80% and 40-90% of 1RM), and five (20-90% of 1RM) incremental loads to predict 1RMs. There were trivial changes for all predicted 1RMs between sessions with 20-90% of 1RM being the most reliable model. Similarly, the actual 1RM was very stable (ES = 0.04, 90% CL [-0.03; 0.12], TE = 3.4 kg [2.5; 5.4], ICC = 0.99 [0.96; 0.996], CV = 1.9% [1.4; 3.0]), while the v@1RM was unreliable between trials (ES = -0.30, 90% CL [-0.78; 0.17], TE = 0.029 m.s-1 [0.022; 0.047], ICC = 0.63 [0.19; 0.86], CV = 15.7% [11.7; 26.1]). However, predicted 1RMs computed from all submaximal load ranges substantially overestimated the actual 1RM with considerable differences between athletes. Although 1RM predictions showed high reliability, they all overestimated the actual 1RM, which was stable between sessions. Therefore, it is not recommended to apply the prediction models used in this study to compute daily 1RMs.
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Purpose: This study examined 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. Methods: Eighteen resistance-trained men performed a baseline one-repetition maximum (1RM) back squat trial and three subsequent 1RM trials used for reliability analyses, with 48-hours interval between trials. 1RM trials comprised lifts from six relative loads including 20, 40, 60, 80, 90, and 100% 1RM. Individualized LVPs for PV, MPV, or MV were derived from loads that were highly reliable based on the following criteria: intra-class correlation coefficient (ICC) >0.70, coefficient of variation (CV) ≤10%, and Cohen's d effect size (ES) <0.60. Results: PV was highly reliable at all six loads. Importantly, MPV and MV were highly reliable at 20, 40, 60, 80 and 90% but not 100% 1RM (MPV: ICC=0.66, CV=18.0%, ES=0.10, standard error of the estimate [SEM]=0.04m·s(-1); MV: ICC=0.55, 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=0.91-0.93), MPV20-90% (r=0.92-0.94) and MV20-90% (r=0.94-0.95). Furthermore, the LVPs were not significantly different (p>0.05) between trials, movement velocities, or between linear regression versus second order polynomial fits. Conclusions: 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.
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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 cutoff 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.
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This study compared typical mechanical variables of interest obtained directly from barbell motion during deadlift performance with a conventional (CBD) and a hexagonal barbell (HBD). Eleven men, proficient with both deadlift variations, volunteered to participate in the study (age: 20.3 ± 0.6 years; height: 175.5 ± 8.5 m; mass: 88.7 ± 19.0 kg; CBD 1RM: 183 ± 22 kg; HBD 1RM: 194 ± 20 kg). During the first session, CBD and HBD 1RM was assessed; during the second session, they performed 3 sets of 1 CBD repetition with 90% 1RM; and in session three, they repeated this process with the HBD. Barbell displacement was recorded at 1000 Hz and mechanical parameters derived from this. Significantly heavier loads were lifted during HBD (6%, p = 0.003). There were no significant differences between barbell displacement (p = 0.216). However, HBD was performed significantly faster (15%, p = 0.012), HBD load was accelerated for significantly longer (36%, p = 0.004), and significantly larger mean forces underpinned this (6%, p < 0.001), with more work having been performed (7%, p < 0.001) at greater power outputs (28%, p < 0.001). The results of this study showed that heavier HBD loads can be lifted through the same range of motion faster, and that this load is accelerated for significantly longer. The strategies used to achieve these differences could have a significant effect on training outcomes.
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THE PURPOSE OF THIS PAPER IS TO DISCUSS THE POTENTIAL BENEFITS OF VELOCITY-BASED TRAINING (VBT) AND HOW IT CAN BE USED TO TRAIN VARIOUS PERFORMANCE FACTORS SPECIFIC TO American FOOTBALL. THE ADVANTAGES OF VBT ARE ITS ABILITY TO IDENTIFY PROPER TRAINING LOADS WHEN DAY-TO-DAY FLUCTUATIONS IN MUSCLE PERFORMANCE OCCUR, THE ABILITY TO ENHANCE SPECIFICITY OF TRAINING, AND THE ABILITY TO PROVIDE IMMEDIATE FEEDBACK TO IMPROVE MOTIVATION AND PERFORMANCE. USING VBT HAS RESULTED IN TANGIBLE IMPROVEMENTS IN COLLEGIATE FOOTBALL PLAYERS' POWER PRODUCTION, WHICH IS A KEY TO IMPROVING ON-FIELD PERFORMANCE.
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Strength training is a critical exercise stimulus for inducing changes in muscular strength, size and power (6). Recently, linear position transducers have gained in popularity as a means to monitor velocity in strength training exercises. The measurement error of such devices has been shown to be low and both relative and absolute reliability have been shown to be acceptable (2, 7, 11). The purpose of this article is to provide the overview and benefits of monitoring movement velocity in strength training exercises, along with providing the basis for novel “velocity-based” strength training prescription. We have covered the following practical applications: Guidelines to develop a velocity/load profile for athletes; Using the velocity load/profile to predict and monitor changes to maximal strength; Using velocity monitoring to control fatigue effects of strength training; Using velocity monitoring as an immediate performance feedback to promote the highest level of effort in specific training exercises and stronger adaptive stimuli. Linear position transducers are reliable and valid tools to help strength and conditioning practitioners monitor and optimize their strength training programs.
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Abstract The purpose of this study was to compare the effect on strength gains of two isoinertial resistance training (RT) programmes that only differed in actual concentric velocity: maximal (MaxV) vs. half-maximal (HalfV) velocity. Twenty participants were assigned to a MaxV (n = 9) or HalfV (n = 11) group and trained 3 times per week during 6 weeks using the bench press (BP). Repetition velocity was controlled using a linear velocity transducer. A complementary study (n = 10) aimed to analyse whether the acute metabolic (blood lactate and ammonia) and mechanical response (velocity loss) was different between the MaxV and HalfV protocols used. Both groups improved strength performance from pre- to post-training, but MaxV resulted in significantly greater gains than HalfV in all variables analysed: one-repetition maximum (1RM) strength (18.2 vs. 9.7%), velocity developed against all (20.8 vs. 10.0%), light (11.5 vs. 4.5%) and heavy (36.2 vs. 17.3%) loads common to pre- and post-tests. Light and heavy loads were identified with those moved faster or slower than 0.80 m·s(-1) (∼60% 1RM in BP). Lactate tended to be significantly higher for MaxV vs. HalfV, with no differences observed for ammonia which was within resting values. Both groups obtained the greatest improvements at the training velocities (≤0.80 m·s(-1)). Movement velocity can be considered a fundamental component of RT intensity, since, for a given %1RM, the velocity at which loads are lifted largely determines the resulting training effect. BP strength gains can be maximised when repetitions are performed at maximal intended velocity.
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The use of bar velocity to estimate relative load in the back squat exercise was examined. Eighty strength-trained men performed a progressive loading test to determine their one-repetition maximum (1RM) and load-velocity relationship. Mean (MV), mean propulsive (MPV) and peak (PV) velocity measures of the concentric phase were analyzed. Both MV and MPV showed a very close relationship to %1RM (R2 = 0.96), whereas a weaker association (R2 = 0.79) and larger SEE (0.14 vs. 0.06 m•s-1) was found for PV. Prediction equations to estimate load from velocity were obtained. When dividing the sample into three groups of different relative strength (1RM/body mass), no differences were found between groups for the MPV attained against each %1RM. MV attained with the 1RM was 0.32 ± 0.03 m•s-1. The propulsive phase accounted for 82% of concentric duration at 40% 1RM, and progressively increased until reaching 100% at 1RM. Provided that repetitions are performed at maximal intended velocity, a good estimation of load (%1RM) can be obtained from mean velocity as soon as the first repetition is completed. This finding provides an alternative to the often demanding, time-consuming and interfering 1RM or nRM tests and allows to implement a velocity-based resistance training approach.
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Abstract This study analysed the effect of imposing a pause between the eccentric and concentric phases on the biological within-subject variation of velocity- and power-load isoinertial assessments. Seventeen resistance-trained athletes undertook a progressive loading test in the bench press (BP) and squat (SQ) exercises. Two trials at each load up to the one-repetition maximum (1RM) were performed using 2 techniques executed in random order: with (stop) and without (standard) a 2-s pause between the eccentric and concentric phases of each repetition. The stop technique resulted in a significantly lower coefficient of variation for the whole load-velocity relationship compared to the standard one, in both BP (2.9% vs. 4.1%; P = 0.02) and SQ (2.9% vs. 3.9%; P = 0.01). Test-retest intraclass correlation coefficients (ICCs) were r = 0.61-0.98 for the standard and r = 0.76-0.98 for the stop technique. Bland-Altman analysis showed that the error associated with the standard technique was 37.9% (BP) and 57.5% higher (SQ) than that associated with the stop technique. The biological within-subject variation is significantly reduced when a pause is imposed between the eccentric and concentric phases. Other relevant variables associated to the load-velocity and load-power relationships such as the contribution of the propulsive phase and the load that maximises power output remained basically unchanged.
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This study compared the velocity- and power-load relationships of the antagonistic upper-body exercises of prone bench pull (PBP) and bench press (BP). 75 resistance-trained athletes performed a progressive loading test in each exercise up to the one-repetition maximum (1RM) in random order. Velocity and power output across the 30-100% 1RM were significantly higher for PBP, whereas 1RM strength was greater for BP. A very close relationship was observed between relative load and mean propulsive velocity for both BP (R2=0.97) and PBP (R2=0.94) which enables us to estimate %1RM from velocity using the obtained prediction equations. Important differences in the load that maximizes power output (Pmax) and the power profiles of both exercises were found according to the outcome variable used: mean (MP), peak (PP) or mean propulsive power (MPP). When MP was considered, the Pmax load was higher (56% BP, 70% PBP) than when PP (37% BP, 41% PBP) or MPP (37% BP, 46% PBP) were used. For each variable there was a broad range of loads at which power output was not significantly different. The differing velocity- and power-load relationships between PBP and BP seem attributable to the distinct muscle architecture and moment arm levers involved in these exercises.
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The dead lift (DL) and its variations are widely accepted by strength and conditioning coaches as one of the "Big 3'' exercises prescribed to develop "total body strength,'' specifically the hip and knee extensors, spinal erectors, quadratus lumborum, core abdominal musculature, and back and forearm muscles. Therefore, the purpose of this column is to introduce strength and conditioning coaches to the many sport-specific applications for common DL variations used in strength training program design, with specific emphasis on the romanian DL, for its potential use in the teaching progression of the power clean.
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The purpose of this study was to investigate whether the deadlift could be effectively incorporated with explosive resistance training (ERT) and to investigate whether the inclusion of chains enhanced the suitability of the deadlift for ERT. Twenty-three resistance trained athletes performed the deadlift with 30, 50, and 70% 1-repetition maximum (1RM) loads at submaximal velocity, maximal velocity (MAX), and MAX with the inclusion of 2 chain loads equal to 20 or 40% of the subjects' 1RM. All trials were performed on force platforms with markers attached to the barbell to calculate velocity and acceleration using a motion capture system. Significant increases in force, velocity, power, rate of force development, and length of the acceleration phase (p < 0.05) were obtained when repetition velocity increased from submaximal to maximal. During MAX repetitions with a constant resistance, the mean length of the acceleration phase ranged from 73.2 (±7.2%) to 84.9 (±12.2%) of the overall movement. Compared to using a constant resistance, the inclusion of chains enabled greater force to be maintained to the end of the concentric action and significantly increased peak force and impulse (p < 0.05), while concurrently decreasing velocity, power, and rate of force development (p < 0.05). The effects of chains were influenced by the magnitude of the chain and barbell resistance, with greater increases and decreases in mechanical variables obtained when heavier chain and barbell loads were used. The results of the investigation suggest that the deadlift can be incorporated effectively in ERT programs. Coaches and athletes should be aware that the inclusion of heavy chains may have both positive and negative effects on kinematics and kinetics of an exercise.
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This study aimed to analyze the acute mechanical and metabolic response to resistance exercise protocols (REP) differing in the number of repetitions (R) performed in each set (S) with respect to the maximum predicted number (P). Over 21 exercise sessions separated by 48-72 h, 18 strength-trained males (10 in bench press (BP) and 8 in squat (SQ)) performed 1) a progressive test for one-repetition maximum (1RM) and load-velocity profile determination, 2) tests of maximal number of repetitions to failure (12RM, 10RM, 8RM, 6RM, and 4RM), and 3) 15 REP (S × R[P]: 3 × 6[12], 3 × 8[12], 3 × 10[12], 3 × 12[12], 3 × 6[10], 3 × 8[10], 3 × 10[10], 3 × 4[8], 3 × 6[8], 3 × 8[8], 3 × 3[6], 3 × 4[6], 3 × 6[6], 3 × 2[4], 3 × 4[4]), with 5-min interset rests. Kinematic data were registered by a linear velocity transducer. Blood lactate and ammonia were measured before and after exercise. Mean repetition velocity loss after three sets, loss of velocity pre-post exercise against the 1-m·s load, and countermovement jump height loss (SQ group) were significant for all REP and were highly correlated to each other (r = 0.91-0.97). Velocity loss was significantly greater for BP compared with SQ and strongly correlated to peak postexercise lactate (r = 0.93-0.97) for both SQ and BP. Unlike lactate, ammonia showed a curvilinear response to loss of velocity, only increasing above resting levels when R was at least two repetitions higher than 50% of P. Velocity loss and metabolic stress clearly differs when manipulating the number of repetitions actually performed in each training set. The high correlations found between mechanical (velocity and countermovement jump height losses) and metabolic (lactate, ammonia) measures of fatigue support the validity of using velocity loss to objectively quantify neuromuscular fatigue during resistance training.
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This study analyzed the contribution of the propulsive and braking phases among different percentages of the one-repetition maximum (1RM) in the concentric bench press exercise. One hundred strength-trained men performed a test with increasing loads up to the 1RM for the individual determination of the load-power relationship. The relative load that maximized the mechanical power output (P(max)) was determined using three different parameters: mean concentric power (MP), mean power of the propulsive phase (MPP) and peak power (PP). The load at which the braking phase no longer existed was 76.1+/-7.4% 1RM. P(max) was dependent on the parameter used: MP (54.2%), MPP (36.5%) or PP (37.4%). No significant differences were found for loads between 40-65% 1RM (MP) or 20-55% 1RM (MPP and PP), nor between P(max) (% 1RM) when using MPP or PP. P(max) was independent of relative strength, although certain tendency towards slightly lower loads was detected for the strongest subjects. These results highlight the importance of considering the contribution of the propulsive and braking phases in isoinertial strength and power assessments. Referring the mean mechanical values to the propulsive phase avoids underestimating an individual's true neuromuscular potential when lifting light and medium loads.
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This study examined the possibility of using movement velocity as an indicator of relative load in the bench press (BP) exercise. One hundred and twenty strength-trained males performed a test (T1) with increasing loads for the individual determination of the one-repetition maximum (1RM) and full load-velocity profile. Fifty-six subjects performed the test on a second occasion (T2) following 6 weeks of training. A very close relationship between mean propulsive velocity (MPV) and load (%1RM) was observed (R (2)=0.98). Mean velocity attained with 1RM was 0.16+/-0.04 m x s(-1) and was found to influence the MPV attained with each %1RM. Despite a mean increase of 9.3% in 1RM from T1 to T2, MPV for each %1RM remained stable. Stability in the load-velocity relationship was also confirmed regardless of individual relative strength. These results confirm an inextricable relationship between relative load and MPV in the BP that makes it possible to: 1) evaluate maximal strength without the need to perform a 1RM test, or test of maximum number of repetitions to failure (XRM); 2) determine the %1RM that is being used as soon as the first repetition with any given load is performed; 3) prescribe and monitor training load according to velocity, instead of percentages of 1RM or XRM.
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Three studies that used rugby league players experienced in power training methods as subjects were performed to investigate the resistance (percentage of 1 repetition maximum [1RM]) that maximized the average mechanical power output (Pmax) during the jump squat exercise. Maximum strength was assessed via 1RM (studies 2 and 3) or 3RM (study 1) during the full-squat exercise. Pmax was assessed during barbell jump squats, using resistances of 40, 60, 80, and 100 kg within the Plyometric Power System. All studies found that power output was maximized by resistances averaging circa 85-95 kg, representing 55-59% of 1RM full-squat strength. However, loads in the range of 47-63% of 1RM were often similarly effective in maximizing power output. The results of this investigation suggest that athletes specifically trained via both maximal strength and power training methods may generate their maximal power outputs at higher percentages of 1RM than those previously reported for solely strength-trained athletes and that there would appear to be an effective range of resistances for maximizing power output.
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The aim of this study was to compare the acute neuromuscular responses during resistance exercise performed with different loading protocols. Thirteen (N=13) college-aged male subjects experienced in weight training completed two different weight training protocols involving a single elbow flexion exercise. During both protocols subjects performed three sets of the exercise with a 3-min rest between each set. One protocol required the subjects to perform each set to failure using 100% 10 repetition maximum (RM) load whereas the second protocol required the subjects to perform 10 repetitions for the first two sets using 90% 10RM load and only go to failure on the third set. Maximal voluntary isometric contraction (MVIC), integrated EMG recording (iEMG) of the biceps brachii, and blood lactate were measured before and upon completion of the two training protocols. Subjects were able to perform a significantly greater volume of work (total repetitionsxload) in the 90% 10RM protocol compared to the 100% 10RM protocol. Both protocols elicited similar cumulative levels of fatigue as reflected by a decrease in MVIC and iEMG(max) and an increase in blood lactate (p< or =0.05). As a result of the drop in repetitions performed in successive sets, it was concluded that training with 100% 10RM while exercising to failure in each set may not optimise the training volume, which may have implications for chronic muscle adaptation.