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RPE and Velocity Relationships for the Back Squat, Bench Press, and Deadlift in Powerlifters
Abstract
The purpose of this study was to compare average concentric velocity (ACV) and rating of perceived exertion (RPE) based on repetitions in reserve on the squat, bench press and deadlift. Fifteen powerlifters (3 female, 12 male, 28.4 ± 8.5 years) worked up to a one repetition maximum (1RM) on each lift. RPE was recorded on all sets and ACV was recorded for all sets performed at 80% of estimated 1RM and higher, up to 1RM. RPE at 1RM on the squat, bench press and deadlift was 9.6 ± 0.5, 9.7 ± 0.4 and 9.6 ± 0.5, respectively and were not significantly different (p > 0.05). ACV at 1RM on the squat, bench press and deadlift was 0.23 ± 0.05, 0.10 ± 0.04 and 0.14 ± 0.05 m·s-1, respectively. The squat was faster than both the bench press and deadlift (p <0.001) and the deadlift was faster than the bench press (p = 0.05). Very strong relationships (r = 0.88 to 0.91) between percentage 1RM and RPE were observed on each lift. ACV showed strong (r= -0.79 to -0.87) and very strong (r= -0.90-92) inverse relationships with RPE and percentage 1RM on each lift, respectively. We conclude that RPE may be a useful tool for prescribing intensity for the squat, bench press, and deadlift in powerlifters, in addition to traditional methods such as percentage of 1RM. Despite high correlations between percentage 1RM and ACV, a 'velocity load profile' should be developed to prescribe intensity on an individual basis with appropriate accuracy.
... Due to these limitations of the submaximal repetitions to fatigue method for estimating 1RM, others have sought to utilize load-velocity profiles. Previous findings have demonstrated a strong linear relationship (R 2 ≥ 0.9) between 1RM and average concentric velocity (ACV) [21][22][23], with early investigations suggesting ACV at 1RM is relatively stable [22,24,25]. Conversely, more recent investigations have brought into question the validity of this method due to the large variation in ACV at an athlete's 1RM [16]. ...
... Due to the inclusion of three protocols, there were six possible sequences in which participants could complete the investigation (i.e., P1-P2-SS, P1-SS-P2, P2-P1-SS, P2-SS-P1, SS-P1-P2, and SS-P2-P1). With the current sample size (24) and subgroups (12 males and 12 females), each sequence was represented four times in total and twice within each sub-group. The order in which the sequences were assigned to participants was determined using a randomized permuted block design function within the random R package [41]. ...
Background: One repetition maximum (1RM) is a vital metric for exercise professionals, but various testing protocols exist, and their impacts on the resulting 1RM, barbell kinetics, and subsequent muscular performance testing are not well understood. This study aimed to compare two previously established protocols and a novel self-led method for determining bench press 1RM, 1RM barbell kinetics, and subsequent muscular performance measures. Methods: Twenty-four resistance-trained males (n = 12, 24 ± 6.1 years) and females (n = 12, 22.5 ± 5.5 years) completed three laboratory visits in a randomized crossover fashion. During each visit, a 1RM was established using one of the three protocols followed by a single set to volitional fatigue using 80% of their 1RM. A Sex:Protocol repeated measures ANOVA was used to determine the effects of sex and differences between protocols. Results: No significant differences were observed between the protocols for any measure, except for 1RM peak power (p = 0.036). Post hoc pairwise comparisons failed to identify any differences. Males showed significantly higher 1RM, average, and peak power (ps < 0.001), while females demonstrated a greater average concentric velocity (p = 0.031) at 1RM. Conclusions: These data suggest the protocol used to establish 1RM may have minimal impact on the final 1RM, 1RM barbell kinetics, and subsequent muscular endurance in a laboratory setting.
... Mean velocity refers to the average concentric movement velocity, and peak velocity refers to the maximum instantaneous velocity reached during the concentric phase of the movement [3,4]. In the context of powerlifting exercises, mean velocity is usually the variable of choice [5]. This is due to mean velocity being more reliable compared to peak velocity [6][7][8]. ...
... Because the average concentric velocity decreases gradually with higher percentages of the one repetition maximum (1RM) [5], a gradual loading protocol was selected to encompass a broader spectrum of velocities. A further benefit of a gradual loading protocol is that sticking points and other changes to barbell kinematics might only become apparent closer to muscular failure [23,24]. ...
The aim of this study was to determine the validity of three smartphone applications measuring barbell movement velocity in resistance training and comparing them to a commercially available linear transducer. Twenty competitive powerlifters (14 male and 6 female) completed a progressive loading protocol in the squat, bench press and deadlift (sumo or conventional) until reaching 90% of the highest load they had achieved in a recent competition. Mean velocity was concurrently recorded with three smartphone applications: Qwik VBT (QW), Metric VBT (MT), MyLift (ML), and one linear transducer: RepOne (RO). 3D motion capturing (Vicon) was used to calculate specific gold standard trajectory references for the different systems. A total of 589 repetitions were recorded with a mean velocity of (mean ± standard deviation [min-max]) 0.44 ± 0.17 [0.11–1.04] m·s⁻¹, of which MT and ML failed to identify 52 and 175 repetitions, respectively. When compared to Vicon, RO and QW consistently delivered valid measurements (standardized mean bias [SMB] = 0 to 0.21, root mean squared error [RMSE] = 0.01 to 0.04m·s⁻¹). MT and ML failed to deliver a level of validity comparable to RO (SMB = -0.28 to 0.14, RMSE = 0.04–0.14m·s⁻¹), except for MT in the bench press (SMB = 0.07, RMSE = 0.04m·s⁻¹). In conclusion, smartphone applications can be as valid as a linear transducer when assessing mean concentric barbell velocity. Out of the smartphone applications included in this investigation, QW delivered the best results.
... Therefore, researchers explored how to leverage objective barbell velocity data to estimate repetitions-in-reserve given that velocity loss was associated with metabolite accumulation in muscles (i.e., a proxy of fatigue). Indeed, it has been shown that the loss of movement velocity during a set is related to the (perceived) number of repetitions-in-reserve (41,66,100). Therefore, it should follow that monitoring velocity data to regulate the number of repetitions-in-reserve, and thus training set volume, should also be effective for improving strength-related adaptations given its potential to regulate the volume of training. ...
Hirsch, SM, Chapman, CJ, Singh, H, Baker, DG, and Frost, DM. A critical appraisal of using barbell velocity data to regulate training. J Strength Cond Res 39(3): 360-372, 2025-Practitioners must balance numerous training variables to ensure they do not impose too much nor too little training stress on their athlete. As an athlete's capacity can fluctuate based on their preparedness for training, the intended vs. actual training intensity in a fixed training program may not coincide. Similarly, the training set volume that an athlete should be exposed to may fluctuate depending on their current state. A discrepancy between intended vs. actual training intensity and volume could negatively impact subsequent training adaptations. Thus, researchers and practitioners have advocated for "autoregulation," whereby the volume and intensity of training are automatically adjusted based on the athlete's preparedness. One proposed method of autoregulating resistance training is by using barbell velocity data. However, it is unclear whether, and under which contexts, these data are appropriate for regulating resistance training. Therefore, the purpose of this literature review was to critically examine the current research on using barbell velocity data to regulate resistance training intensity and volume. After examining the relevant literature, it is the authors' belief that the current data do not support using velocity data to precisely regulate resistance training intensity. However, it is the authors' belief that the current literature does suggest that researchers and practitioners can leverage these data to regulate other aspects of resistance training, such as athlete motivation, autonomy, and focus of attention, which could also impact the resulting adaptations from training. Overall, more research is required to better understand how researchers and practitioners should use velocity data to guide training.
... Research consistently shows a linear relationship between relative load and movement velocity across resistance exercises, enabling precise predictions and monitoring of training intensity [12,13]. However, individualized approaches are essential, as velocity profiles can vary between athletes due to differences in physiological characteristics and technical execution [14][15][16][17]. ...
Citation: González-Alcázar, F.J.; Jiménez-Martínez, P.; Alix-Fages, C.; Ruiz-Ariza, A.; Casuso, R.A.; Varela-Goicoechea, J.; García-Ramos, A.; Jerez-Martínez, A. Impact of Low-Load High-Volume Initial Sets vs. Traditional High-Load Low-Volume Bench Press Protocols on Functional and Structural Adaptations in Powerlifters. Appl. Sci. 2025, 15, 1974. Abstract: This study aimed to investigate the effectiveness of low-load high-volume (LL-HV) resistance training compared to traditional high-load low-volume (HL-LV) protocols in eliciting functional and structural adaptations in powerlifters. Twenty-six well-trained male powerlifters were randomly assigned to LL-HV and HL-LV groups and participated in a 12-week supervised training intervention. The LL-HV protocol involved an initial bench press set performed at 45-60% of one-repetition maximum (1RM), with very high repetitions, while the HL-LV group performed the initial set at 75-90% of 1RM, following matched total training volume for accessory exercises. Both groups trained twice weekly, with identical proximity to failure based on repetitions in reserve (RIR). Functional outcomes included changes in bench press 1RM and mean velocity (MV) measured at various submaximal loads, while structural adaptations were assessed through arm and chest circumferences. Statistical analyses were conducted using a two-factor mixed analysis of variance (ANOVA) to assess the effects of "time" and "training group" on these outcomes. Percent changes were comparable between groups for most variables, with significant improvements observed in the LL-HV group for MV at 80% of 1RM and arm circumference. These findings suggest that LL-HV, emphasizing high-repetition sets, offers an effective alternative to HL-LV protocols for enhancing performance and structural adaptations in powerlifters.
... An emerging theme when dissecting the current load and volume autoregulation literature is that each autoregulation method has different advantages and limitations; thus, an integrated approach may be plausible to maximize the advantages and minimize the limitations. Although the concept of integrating some of these profiles and relationships for RT prescription is not new (García Ramos 2023; Pareja-Blanco and Loturco 2022; González-Badillo et al. 2022;Weakley et al. 2020;Banyard et al. 2020;Helms et al. 2016Helms et al. , 2017, to our knowledge, we are unaware of a single model integrating all of these concepts; therefore, synthesizing a conceptually integrated model may be a potential avenue to explore in future directions and contextualize for practical applications as implied by Hickmott et al. (2022). We Content courtesy of Springer Nature, terms of use apply. ...
Resistance training (RT) load and volume are considered crucial variables to appropriately prescribe and manage for eliciting the targeted acute responses (i.e., minimizing neuromuscular fatigue) and chronic adaptations (i.e., maximizing neuromuscular adaptations). In traditional RT contexts, load and volume are generally pre-prescribed; thereby, potentially yielding sub-optimal outcomes. A RT concept that individualizes programming is autoregulation: a systematic two-step feedback process involving, (1) monitoring performance and its constituents (fitness, fatigue, and readiness) across multiple time frames (short-, moderate-, and long-term); and (2) adjusting programming (i.e., load and volume) to elicit the targeted goals (i.e., responses and adaptations). A growing body of load and volume autoregulation research has accelerated recently, with several meta-analyses suggesting that autoregulation may provide a small advantage over traditional RT. Nonetheless, the existing literature has typically conceptualized these current autoregulation methods as standalone practices, which has limited their extensive utility in research and applied settings. The primary purpose of this review was three-fold. Initially, we synthesized the current methods of load and volume autoregulation, while disseminating each method’s main advantages and limitations. Second, we conceptualized a theoretical Integrated Velocity Model (IVM) that integrates the current methods for a more holistic perspective of autoregulation that may potentially augment its benefits. Lastly, we illustrated how the IVM may be compared to the current methods for future directions and how it may be implemented for practical applications. We hope that this review assists to contextualize a novel autoregulation framework to help inform future investigations for researchers and practices for RT professionals.
... RPE may also be based on perceived bar speed, meaning that the coach will watch the lift in real time or on video or the client will watch their own video and rate how fast the barbell moved through the lift (Helms et al. 2017). Some coaches will get as granular as clipping videos to compare the length in seconds of how long sets or lifts took. ...
The perspectives of skilled experts who help learners develop and improve bodily skills has not often been examined in sociology. In this paper, I focus on the role of selective sensory engagement with moving bodies as an important way of knowing through which skilled experts assess and intervene upon the skill acquisition of learners. I bring together literature on body pedagogics with literature on sociology of the senses to understand how embodied, sensorial knowledge is an inter-corporeal and intersubjective resource for the transmission of bodily skills. I use interviews with barbell coaches and yoga teachers in the United States to understand the processes by which these skilled experts construct ‘good form’ and its normative limits and I outline three interrelated processes of sensorial knowing through which barbell coaches and yoga teachers help their paying clients achieve ‘good form’ when learning to engage in these fitness cultures. I argue that that the use and training of the senses in body pedagogics is an understudied but essential way of knowing to understand the transmission and embodiment of culture.
... In addition, it has shown excellent test-retest reliability in our laboratory (ICC (3,1) = 0.932; SEM = 0.019; MD = 0.054; n = 12). The device was used according to manufacturer's instructions such that the device was attached to the barbell with a perpendicular angle being achieved throughout the entirety of lifts (23). Although some participants completed their trials in the morning and some in the evening, each participant completed all trails within 2 hours of the same time of day. ...
... Several factors may have influenced the present study results that do not agree with the tendencies reported in previous studies [5,16,22,39]. First, the participants in the present study were a relatively heterogeneous group compared to previous studies examining homogeneous groups such as trained [12,19] and elderly [25]. Indeed, the relationship between the velocity recorded during a single repetition and the %1RM may be influenced by the execution technique and sex [41]. ...
Purpose
This study aims to evaluate the accuracy of predicting one-repetition-maximum (1RM) using the load-velocity relationship and different repetition-to-failure estimation equations for ten lower-extremity exercises.
Methods
A total of 22 healthy participants were recruited. The tested exercises included ankle, knee, and hip joint flexion and extension, as well as hip abduction, hip adduction, and leg press. Velocity during the concentric phase was measured using a linear transducer, and individual linear regression models were established using incremental submaximal loads (40–80% 1RM) and velocity to estimate the 1RM. Repetition-to-failure estimations of 1 RM were assessed with eleven different regression equations, among them the Lombardi equation. Intraclass correlation coefficient (ICC), Bland and Altman plots, and normalized mean absolute error (NMAE) were used to compare the estimations with a measured 1RM.
Results
Predictions based on the load-velocity relationship exhibited NMAE values ranging from 8.6% to 35.2%, ICC values from 0.35 to 0.87, and substantial limits of agreement across all exercises, in contrast to the measured 1RM values. Among the fatigue estimation equations, the Lombardi equation demonstrated the lowest NMAE across all exercises (5.8%), with an excellent ICC of 0.99 and narrow limits of agreement.
Conclusion
The load-velocity relationship proved inadequate for predicting 1RM in lower-extremity single-joint exercises. However, the Lombardi estimation equations showcased favorable predictive performance with a consistently low average NMAE across all exercises studied.
This study investigates the association between internal (IL), external load (EL) and performance measures (PM) in vertical jump sessions, employing two set structure methods: traditional (TSS) and cluster (CSS). The study involved 16 (M = 8; W = 8) physically active participants. Vertical jump sessions comprised 144 jumps divided into 12 sets, with a fixed number of 12 jumps per set for the TSS and varying for the CSS (from 6 to 18 jumps). EL variables (i.e., number of jumps, total vertical distance, average jump height relative to the maximum height, 88% of body mass, and sum of maximal power), subjective IL (i.e., rate perceived exertion for legs and breath, rating of perceived recovery), objective IL (i.e., heart rate) and PM (i.e., percentage of decreasing of maximal jumping height) assessments were employed. Subjective variables of the IL achieved a very high association with EL variables for both applied set structures (r=0.71-0.92). In contrast, objective variable of IL generally displayed a weaker relationship, ranging from small (r=0.11-0.20) for the CSS to moderate and high (r=0.69-0.75) for TSS. Finally, PM demonstrated trivial to moderate relationship with EL and IL variables (r=0.00-0.36), independent of the set structure. The results highlight the intricate relationship between IL, EL, and PM during bodyweight power training, with subjective variables showing exceptionally high associations across set structures. The choice of set structure significantly influences the correlation between IL, EL and PM, emphasizing the need for coaches to consider set structure when optimizing training strategies.
This study examined the relationship between velocity-loss and different squat exercise intensities. Twenty-two male college students who were actively exercising performed repetitive lifting in squatting exercises at five different velocity-zones. The result showed that the slope of the velocity decrease was large in the medium-intensity-high-intensity range. At low intensities, the velocity decrease was moderate. It was also clear that the degree of fatigue varied greatly depending on the number of repetitions and the duration of exercise at the same rate of velocity-loss. In addition, higher exerted power was observed at low and medium intensities. The minimum power relative to the maximum peak power was approximately 70% of the peak power. This suggests that the acceptable range of the rate of velocity reduction relative to intensity may differ in order to control excessive fatigue in velocity-based training and to obtain the desired training effect.
Resistance exercise is difficult to quantify owing to its inherent complexity with numerous training variables contributing to the training dose (type of exercise, load lifted, training volume, inter-set rest periods, and repetition velocity). In addition, the intensity of resistance training is often inadequately determined as the relative load lifted (% 1-repetition maximum), which does not account for the effects of inter-set recovery periods, repetition velocity, or the number of repetitions performed in each set at a given load. Methods to calculate the volume load associated with resistance training, as well as the perceived intensity of individual sets and entire training sessions have been shown to provide useful information regarding the actual training stimulus. In addition, questionnaires to subjectively assess how athletes are coping with the stressors of training and portable technologies to quantify performance variables such as concentric velocity may also be valuable. However, while several methods have been proposed to quantify resistance training, there is not yet a consensus regarding how these methods can be best implemented and integrated to complement each other. Therefore, the purpose of this review is to provide practical information for strength coaches to highlight effective methods to assess resistance training, and how they can be integrated into a comprehensive monitoring program.
Objective. To compare resistance bouts performed to failure atlow (60% 1RM) and high (90% 1RM) workloads for acute rate of perceived exertion (RPE) (per exercise), session RPE (S-RPE) (30 min post), HR (per exercise) and total work (per session, and per exercise).Background. RPE is a convenient method for quantifying intensityin aerobic exercise. However, RPE has recently been extended to exercise modalities dominated by anaerobic pathways such as resistance training (RT). Method. Subjects (N=12) were assessed using an exercise-specific1 repetition maximum (1RM) for 6 exercises. On separate days in a counterbalanced order, subjects performed 3 sets of each exercise to volitional failure at a low intensity (LI) and a high intensity (HI) with 2 minutes rest between sets and exercises. At the end of each set, subjects estimated acute RPE for that set using a 10-point numerical scale. Thirty minutes after the end of the exercise session subjects estimated their S-RPE for the entire workout. HR, total work, and acute RPE were compared (HI v. LI) using repeated measures ANOVA.Results. A paired samples t-test showed LI was significantly higher(p=0.039) than HI for session RPE (LI=8.8±0.8, HI=6.3±1.2) andtotal work (LI=17461±4419, HI=8659±2256) (p=0.043). Per exercise,total work and acute RPE were significantly greater (p=0.01) for LI for all exercises. Peak HR was significantly higher per exercise during LI for leg press (p=0.041), bench press (p=0.031), lat pull-down (p=0.037) and shoulder press (p=0.046).
The primary aim of this study was to compare rating of perceived exertion (RPE) values measuring repetitions in reserve (RIR) at particular intensities of 1RM in experienced (ES) and novice squatters (NS). Further, this investigation compared average velocity between ES and NS at the same intensities. Twenty-nine individuals (24.0±3.4yrs.) performed a one-repetition maximum (1RM) squat followed by a single repetition with loads corresponding to 60, 75, and 90% of 1RM and an 8-repetition set at 70% 1RM. Average velocity was recorded at 60, 75, and 90% 1RM and on the first and last repetitions of the 8-repetition set. Subjects reported an RPE value that corresponded to an RIR value (RPE-10 = 0-RIR, RPE-9 = 1-RIR, and so forth). Subjects were assigned to one of two groups: 1) ES (n=15, training age: 5.2±3.5yrs.), 2) NS (n=14, training age: 0.4±0.6yrs.). The mean of the average velocities for ES were slower (P<0.05) than NS at 100% and 90% 1RM. However, there were no differences (P>0.05) between groups at 60%, 75%, or for the 1st and 8th repetitions at 70% 1RM. Additionally, ES recorded greater RPE at 1RM than NS (P=0.023). In ES there was a strong inverse relationship between average velocity and RPE at all percentages (r= -0.88, P<0.001), and a strong inverse correlation in NS between average velocity and RPE at all intensities (r=-0.77,P=0.001). Our findings demonstrate an inverse relationship between average velocity and RPE/RIR. ES exhibited slower average velocity and higher RPE at 1RM than NS, signaling greater efficiency at high intensities. The RIR-based RPE scale is a practical method to regulate daily training load and provide feedback during a 1RM test.
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.
Resistance exercise intensity is commonly prescribed as a percent of 1 repetition maximum (1RM). However, the relationship between percent 1RM and the number of repetitions allowed remains poorly studied, especially using free weight exercises. The purpose of this study was to determine the maximal number of repetitions that trained (T) and untrained (UT) men can perform during free weight exercises at various percentages of 1RM. Eight T and 8 UT men were tested for 1RM strength. Then, subjects performed 1 set to failure at 60, 80, and 90% of 1RM in the back squat, bench press, and arm curl in a randomized, balanced design. There was a significant (p < 0.05) intensity x exercise interaction. More repetitions were performed during the back squat than the bench press or arm curl at 60% 1RM for T and UT. At 80 and 90% 1RM, there were significant differences between the back squat and other exercises; however, differences were much less pronounced. No differences in number of repetitions performed at a given exercise intensity were noted between T and UT (except during bench press at 90% 1RM). In conclusion, the number of repetitions performed at a given percent of 1RM is influenced by the amount of muscle mass used during the exercise, as more repetitions can be performed during the back squat than either the bench press or arm curl. Training status of the individual has a minimal impact on the number of repetitions performed at relative exercise intensity.
Abstract In this study, we examined the validity of a novel subjective scale for assessing resistance-exercise effort. Seventeen male bodybuilders performed five sets of 10 repetitions at 70% of one-repetition maximum, for the bench press and squat. At the completion of each set, participants quantified their effort via the rating of perceived exertion (RPE) and novel estimated-repetitions-to-failure scales, and continued repetitions to volitional exhaustion to determine actual-repetitions-to-failure. There were high correlations between estimated- and actual-repetitions-to-failure across sets for the bench press and squat (r ≥ 0.93; P < 0.05). During sets 3, 4, and 5, estimated-repetitions-to-failure predicted the number of repetitions to failure for the bench press and squat, as indicated by smaller effect sizes for differences (ES = 0.37-0.0). The estimated-repetitions-to-failure scale was reliable as indicated by high intraclass correlation coefficients (≥0.92) and narrow 95% limits of agreement (≤0.63 repetitions) for both the bench press and squat. Despite high correlations between RPE and actual-repetitions-to-failure (P < 0.05), RPE at volitional fatigue was less than maximal for both exercises. Our results suggest that the estimated-repetitions-to-failure scale is valid for predicting onset of muscular failure, and can be used for the assessment and prescription of resistance exercise.
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.