Load-velocity Profiles Research Articles

Agreement in Squat Jump Force-Time Characteristics Between Smith Machine and Free-Weight Squat Jump Force-Time Characteristics.

Kotani, Y, Lake, J, Guppy, SN, Poon, W, Nosaka, K, and Haff, GG. Agreement in squat jump force-time characteristics between Smith machine and free-weight squat jump force-time characteristics. J Strength Cond Res 37(10): 1955-1962, 2023-The purpose of this study was to determine whether squat jump (SJ) force-velocity (FV) and load-velocity (LV) profiles created using free-weights agree with profiles created with a Smith machine. Fifteen resistance-trained male subjects (age = 26.4 ± 2.5 years; height = 1.75 ± 0.09 m; body mass = 82.6 ± 13.4 kg) participated in this study. All subjects completed 2 familiarization and 2 experimental sessions using both the Smith machine and free-weight SJs each separated by 48 hours. During the experimental trials, progressively loaded SJs were performed in a quasi-randomized block order with loads between 21 kg and 100% of the subject's body mass. Agreement between exercise mode was determined with a weighted least products regression analysis. No fixed or proportional bias was noted between exercise modes when using peak velocity (PV) and mean velocity (MV) to create an FV profile. There was no fixed and proportional bias present for the LV profile when the profile was created with PV. When the LV profile was calculated from MV, fixed and proportional bias were present, indicating that MVs were significantly different between exercise modes. In addition, the free-weight FV and LV profiles exhibited poor to good relative and good to poor absolute reliability. Furthermore, when created using the Smith machine, both profiles exhibited poor to moderate relative and absolute reliability. Based on these data, caution should be used when interpreting LV and FV profiles created with these 2 methods.

Should We Use the Men Load–Velocity Profile for Women in Deadlift and Hip Thrust?

Injuries are common in team sports and can impact both team and individual performance. In particular, hamstring strain injuries are some of the most common injuries. Furthermore, hamstring injury ratios, in number of injuries and total absence days, have doubled in the last 21 seasons in professional soccer. Weakness in hip extensor strength has been identified as a risk factor in elite-level sprinters. In addition, strength imbalances of the hamstring muscle group seem to be a common cause of hamstring strain injuries. In this regard, velocity-based training has been proposed to analyze deficits in the force-velocity profile. Previous studies have shown differences between men and women, since there are biomechanical and neuromuscular differences in the lower limbs between sexes. Therefore, the aim of this study was to compare the load-velocity profile between males and females during two of the most important hip extension exercises: the hip thrust and the deadlift. Sixteen men and sixteen women were measured in an incremental loading test following standard procedures for the hip thrust and deadlift exercises. Pearson's correlation (r) was used to measure the strength of the correlation between movement velocity and load (%1RM). The differences in the load-velocity relationship between the men and the women were assessed using a 2 (sex) × 15 (load) repeated-measures ANOVA. The main findings revealed that: (I) the load-velocity relationship was always strong and linear in both exercises (R2 range: 0.88-0.94), (II) men showed higher velocities for light loads (30-50%1RM; effect size: 0.9-0.96) than women for the deadlift, but no significant differences were found for the hip thrust. Based on the results of this study, the load-velocity equations seem to be sex-specific. Therefore, we suggest that using sex-specific equations to analyze deficits in the force-velocity profile would be more effective to control intensity in the deadlift exercise.

The Use of Incremental, Decremental or a Random Order of Loads does not Affect Peak velocity Values during Bench Press Throw Load-Velocity Relationships.

The aim of this study was to assess the influence of the load order used (i. e. incremental, decremental or random loads order) during the bench press throw load-velocity profile on peak velocity achieved against four different loads (20-40-60-80% of one repetition maximum [1RM]). Both intraclass correlation coefficient (ICC) and coefficient of variation (CV) were calculated to assess the reliability of the measures. A repeated measures ANOVA was used to assess differences between protocols. A linear regression analysis was performed to assess the load-velocity relationships among the different protocols. Peak velocity showed good to high ICC values independently of the load used (ICC=0.83-0.92). CV scores showed good reliability (ranging between 2.2 and 6.2%). No significant differences in peak velocity attained at each load were found between the three testing protocols (p>0.05). In addition, peak velocity at each load was very large to almost perfect, correlated between protocols (r=0.790-0.920). The linear regression model showed a significant relationship between testing protocols (p<0.001; R2=0.94). In conclusion, due to some ICC scores below 0.9 and R2 below 0.95, the indistinct use of different load-order protocols to assess load-velocity relationships in the bench press throw exercise is not recommended.

Is there a connection between individual thigh muscle volume and squat lifting velocity

Background&#x0D; Identifying key musculoskeletal and biomechanical criteria that are associated with Resistance Training (RT) is essential to avoid injury and optimise training outcome. Biomechanically, so-called velocity-based RT is an objective approach to directly quantify training intensity and effect. From a musculoskeletal perspective, RT enhances muscle force, which is correlated to the muscle’s physiological cross-sectional area, and thus muscle volume.&#x0D; Goal&#x0D; The goal of this study is to find out whether there exists a correlation between the change in lifting velocity during a squat set-to-exhaustion and the Hamstring to Quadriceps Muscle Volume Ratio (H/Q Ratio) as indicator of muscle strength.&#x0D; Methodology&#x0D; Ethical approval for this study was given by the Kantonale Ethikkommission Bern. Recreationally trained RT athletes (age 20-40years) were recruited through the investigator’s network. The methodology is divided into three parts. Firstly, the hamstring and quadriceps muscle volumes are determined using Magnetic Resonance Imaging (MRI) as gold standard. Thereby, the open-source software SASHIMI with an automatic tracking function is utilised to segment the MRI data. The segmented data is further processed with the Python programming language to derive the H/Q ratios. Secondly, lifting velocity during the squat is determined using data from optical motion capture (Vicon Nexus System with 10 infrared cameras) as gold standard. Thereby, a set-to-exhaustion is performed at 80% of each individual one repetition maximum (1RM), whereby the subjects are asked to complete the concentric phase at maximum speed to comply with velocity-based RT recommendations. Lifting velocity is derived from the tracked position of two reflective markers on the bar using the Python programming language. Thirdly, a statistical correlation analysis is conducted between the musculoskeletal and the biomechanical parameters. Statistical analysis is performed with the statistical analysis software R, with the level of significance set at p = 0.05.&#x0D; Results&#x0D; To date, MRI data have been acquired in five athletes and the corresponding muscle volumes have been determined (Quadriceps: 0.00525 +/-0.00185 m3 and Hamstrings: 0.0019 +/-0.007 m3). In addition, the load-velocity profiles have been derived in the same group of subjects. The MRI-based muscle volumes are in a similar range to previously published data (Maeo et al., 2021). Furthermore, the results of the load-velocity profiles show the anticipated decrease according to velocity-based RT guidelines (Rodriguez-Rosell et al., 2020).&#x0D; Discussion&#x0D; In a next step further data will be acquired in more subjects for subsequent statistical analysis. The outcome of the present study is expected to provide new scientific insights into the relationship between musculoskeletal and biomechanical criteria that affect squat performance. The insight may help to optimize velocity-based RT recommendations for individual athletes to maximise performance and reduce injury risks.&#x0D; References&#x0D; Rodríguez-Rosell, D., Yáñez-García, J. M., Sánchez-Medina, L., Mora-Custodio, R., &amp; González-Badillo, J. J. (2020). Relationship between velocity loss and repetitions in Reserve in the bench press and back squat exercises. Journal of Strength and Conditioning Research, 34(9), 2537–2547. https://doi.org/10.1519/JSC.0000000000002881&#x0D; Maeo, S., Huang, M., Wu, Y., Sakurai, H., Kusagawa, Y., Sugiyama, T., Kanehisa, H., &amp; Isaka, T. (2021). Greater hamstrings muscle hypertrophy but similar damage protection after training at long versus short muscle lengths. Medicine &amp; Science in Sports &amp; Exercise, 53(4), 825–837. https://doi.org/10.1249/MSS.0000000000002523

Effects of a Velocity-Based Complex Training Program in Young Female Artistic Roller Skating Athletes.

Complex training consists of a near maximal strength effort followed by a biomechanically similar explosive exercise. One of many complex training methods that have been proposed is the French Contrast Method. The aim of this study was to analyze the effects of the French Contrast Method on maximal strength and power of young female artistic roller skating athletes with the help of velocity-based training to prescribe the intervention program. Eighteen female artistic roller skating athletes, divided into an experimental group (EG) and a control group (CG), participated in this study. The EG performed complex training via the French Contrast Method. The CG did not perform any additional training besides their regular roller skating practices. All participants were tested on the 1-RM back squat and hip thrust, the load-velocity profile assessment of both exercises previously stated, the countermovement jump, and the drop jump. A significant increase in mean concentric velocity (MCV) of the hip thrust exercise from 10 to 60% of 1-RM in the EG was observed. Significant differences between groups were observed for the MCV of the hip thrust from 10 to 90% of 1-RM. There were also significant increases in the 1-RM back squat and 1-RM hip thrust over time in the EG. For the vertical jump variables, there were significant differences between groups for both contact time and the reactive strength index with and without an arm swing. The results of this study suggest that a 6-week training intervention with the use of the French Contrast Method can significantly improve maximal strength and power.

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.

Use of concentric linear velocity to monitor flywheel exercise load.

Purpose: To propose the concentric linear velocity measurement as a valid method to quantify load and individualise the prescription of flywheel training, we investigated the relationship between inertial load and mean concentric linear velocity (MCLV) during the flywheel squat exercise in a wide spectrum of intensities. In addition, we compared MCLV and subjective rating of perceived exertion (RPE) after each load. Methods: Twenty-five physically active men volunteered for this study (26.5 ± 2.9 years, 179.5 ± 4.2 cm, 81.6 ± 8.6 kg). After familiarization, all participants performed two inertial progressive load tests on separated days to determine the flywheel load-velocity profile and its reliability. Each participant performed 5 set of 6 repetitions of the flywheel squat exercise with different inertial loads (0.047, 0.104, 0.161, 0.245, 0.321 kg m2) selected in a counterbalanced and randomized order for each testing day. Average MCLV and RPE for each load were compared. Results: The inter-session intraclass correlation coefficient (ICC) showed values above 0.9 in all the included outcomes (MCLV: ICC = 0.91; RPE: ICC = 0.93). A significant correlation (p < 0.01, R 2 = 0.80) between inertial load and MCLV was found. Similarly, significant correlation models (p < 0.01) were observed between RPE and load (R 2 = 0.87) and (R 2 = 0.71) between RPE and MCLV. Conclusion: The control of MCLV during flywheel exercise can be proposed as a valid method to quantify load and to individualize the prescription of flywheel training. In addition, RPE responses have demonstrated significant correlations with load and velocity. Therefore, RPE has been proposed as a valid and reliable alternative to control flywheel training.

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

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

Reliability and validity of velocity measures and regression methods to predict maximal strength ability in the back-squat using a novel linear position transducer

The purpose of this study was to examine the reliability of load-velocity profiles (LVPs) and validity of 1-repetition maximum (1-RM) prediction methods in the back-squat using the novel Vitruve linear position transducer (LPT). Twenty-five men completed a back-squat 1-RM assessment followed by 2 LVP trials using five incremental loads (20%–40%–60%–80%–90% 1-RM). Mean propulsive velocity (MPV), mean velocity (MV) and peak velocity (PV) were measured via a (LPT). Linear and polynomial regression models were applied to the data. The reliability and validity criteria were defined a priori as intraclass correlation coefficient (ICC) or Pearson correlation coefficient ( r) &gt; 0.70, coefficient of variation (CV) ≤10%, and effect size ( ES) &lt;0.60. Bland-Altman analysis and heteroscedasticity of errors ( r2) were also assessed. The main findings indicated MPV, MV and PV were reliable across 20%–90% 1-RM (CV &lt; 8.8%). The secondary findings inferred all prediction models had acceptable reliability (CV &lt; 8.0%). While the MPV linear and MV linear models demonstrated the best estimation of 1-RM (CV &lt; 5.9%), all prediction models displayed unacceptable validity and a tendency to overestimate or underestimate 1-RM. Mean systematic bias (−7.29 to 2.83 kg) was detected for all prediction models, along with little to no heteroscedasticity of errors for linear ( r2 &lt; 0.04) and polynomial models ( r2 &lt; 0.08). Furthermore, all 1-RM estimations were significantly different from each other ( p &lt; 0.03). Concludingly, MPV, MV and PV can provide reliable LVPs and repeatable 1-RM predictions. However, prediction methods may not be sensitive enough to replace direct assessment of 1-RM. Polynomial regression is not suitable for 1-RM prediction.

Using the Load-Velocity Profile for Predicting the 1RM of the Hexagonal Barbell Deadlift Exercise.

Lopes dos Santos, M, Mann, JB, Berton, R, Alvar, B, Lockie, RG, and Dawes, JJ. Using the load-velocity profile for predicting the 1RM of the hexagonal barbell deadlift exercise. J Strength Cond Res 37(1): 220-223, 2023-The aim of this study was to determine whether bar velocity can be used to estimate the 1 repetition maximum (1RM) on the hexagonal bar deadlift (HBD). Twenty-two National Collegiate Athletic Association Division I male ice hockey players (age = 21.0 ± 1.5 years, height = 182.9 ± 7.3 cm, and body mass = 86.2 ± 7.3 kg) completed a progressive loading test using the HBD at maximum intended velocity to determine their 1RM. The mean concentric velocity was measured for each load through a linear position transducer. The a priori alpha level of significance was set at p = 0.05. The mean concentric velocity showed a very strong relationship to %1RM (R2 = 0.85). A nonsignificant difference and a trivial effect size (ES) were observed between the actual and predicted 1RM (p = 0.90, ES = -0.08). Near-perfect correlations were also discovered between the actual and predicted 1RM (R = 0.93) with low typical error and coefficient of variation (5.11 kg and 2.53%, respectively). This 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.

Uphill sprinting load– and force–velocity profiling: Assessment and potential applications

ABSTRACT This study aimed to quantify the validity and reliability of load–velocity (LV) relationship of hill sprinting using a range of different hill gradients and to describe the effect of hill gradient on sprint performance. Twenty-four collegiate-level athletes performed a series of maximal sprints on either flat terrain or hills of gradients 5.2, 8.8 and 17.6%. Velocity–time curves were recorded using a radar device. LV relationships were established using the maximal velocity achieved in each sprinting condition, whilst force–velocity–power (FVP) profiles were established using only the flat terrain sprint. LV profiles were shown to be valid (R2 = 0.99) and reliable (TE < 4.4%). For every 1-degree increase in slope, subjects’ velocity decreased by 1.7 ± 0.1% on average. All the slopes used represented low resistance relative to the entire LV spectrum (<25% velocity loss). Subjects who exhibited greater horizontal force output at higher velocities on flat terrain were most affected by the gradient of the hill. Hills of gradients up to 17.6% do not provide sufficient resistance to optimize power development. However, such hills could be used to develop late-stage technical ability, due to the prolonged horizontally oriented body position that occurs as subjects attempt to overcome the acceleration due to gravity.

The influence of calculation method and relative strength on the load-velocity relationship in bench press exercise

Purpose The objective of this study was: (1) to compare the effect of the calculation method using average (Avalue), and best value (Bvalue) of Mean Propulsive Velocity (MPV) on the Load Velocity Profile (LVprofile) during the barbell bench press exercise in elite handball players. In addition, (2) to analyze the relationship between the individual coefficient of variation (CVind) of an incremental load protocol in the bench press exercise with relative strength (Frel) in professional handball players. Methods Nineteen elite international handball players (age 18 [±1] y; body mass 93 [±14] kg; height 191 [±6] cm) performed an incremental test during the barbell bench press exercise. General and individual LVprofile, Frel, and CVind were modelled through MPV and two calculation models (Avalue, vs Bvalue). Results There were significant differences (p &lt; 0.05) between both conditions (Bvalue vs. Avalue). There was an inverse relationship between the Frel and the CVind (r = −0.66, p &lt; 0.001). When CVind was &gt;10% significant differences were found between the two calculations method (Avalue, vs Bvalue). However, no significant differences were found between Avalue, vs Bvalue when the CVind was &lt;10%. Conclusions The calculation method used to assess the barbell bench press LVprofile impacts the nature of the relationship. Regardless CVind values, Avalue is a good choice to determine the LVprofile when the CVind is &gt;10%, if the CVind is &lt;10%, Bvalue will be the better option to determine the LVprofile.

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

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

Reliability of the Squat Jump Force-Velocity and Load-Velocity Profiles.

Kotani, Y, Lake, J, Guppy, SN, Poon, W, Nosaka, K, Hori, N, and Haff, GG. The reliability of the squat jump force-velocity and load-velocity profiles. J Strength Cond Res 36(11): 3000-3007, 2022-The purpose of this study was to investigate the between-session reliability of the squat jump force-velocity (FV) and load-velocity (LV) profiles. Eighteen subjects (age = 28.1 ± 4.8 years; height = 1.7 ± 9.7; body mass = 74.7 ± 12.8) who could back squat >1.5 times body mass participated in this study. Each subject completed a familiarization session, followed by 2 experimental sessions each separated by 72 hours. Subjects performed a series of squat jumps on a force plate against external loads between 0 and 100% of their body mass in a quasi-randomized block order. Intraclass correlation coefficient (ICC) and coefficient of variation (CV) were used to examine the between-session reliability. Peak velocity (PV) and mean velocity (MV) at each load were highly reliable (ICC >0.80, CV% <7.41, SEM <0.13 m·s -1 , smallest detectable difference [SDD] <0.31 m·s -1 , ES <0.21). Force-velocity profiles created with peak force and relative peak force resulted in poor to excellent reliability (ICC = 0.34-0.92, CV% = 11.9-26.3). When mean and relative mean forces were used to create FV profiles, there was poor to good reliability (ICC = 0.03-0.85, CV% = 18.1-39.4). When the LV profile was calculated with PV (ICC = 0.60-0.90, CV% = 7.9-16.9) or MV (ICC = 0.49-0.91, CV% = 11.1-23.4), there was poor to excellent reliability. There was no time effect found between sessions for both FV and LV profiles. The squat jump FV and LV profiles established with a force plate are not reliable. Therefore, these profiles are not recommended to be used to inform programming decisions.

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.

Differences between adjusted vs. non-adjusted loads in velocity-based training: consequences for strength training control and programming

Strength and conditioning specialists commonly deal with the quantification and selection the setting of protocols regarding resistance training intensities. Although the one repetition maximum (1RM) method has been widely used to prescribe exercise intensity, the velocity-based training (VBT) method may enable a more optimal tool for better monitoring and planning of resistance training (RT) programs. The aim of this study was to compare the effects of two RT programs only differing in the training load prescription strategy (adjusting or not daily via VBT) with loads from 50 to 80% 1RM on 1RM, countermovement (CMJ) and sprint. Twenty-four male students with previous experience in RT were randomly assigned to two groups: adjusted loads (AL) (n = 13) and non-adjusted loads (NAL) (n = 11) and carried out an 8-week (16 sessions) RT program. The performance assessment pre- and post-training program included estimated 1RM and full load-velocity profile in the squat exercise; countermovement jump (CMJ); and 20-m sprint (T20). Relative intensity (RI) and mean propulsive velocity attained during each training session (Vsession) was monitored. Subjects in the NAL group trained at a significantly faster Vsession than those in AL (p < 0.001) (0.88–0.91 vs. 0.67–0.68 m/s, with a ∼15% RM gap between groups for the last sessions), and did not achieve the maximum programmed intensity (80% RM). Significant differences were detected in sessions 3–4, showing differences between programmed and performed Vsession and lower RI and velocity loss (VL) for the NAL compared to the AL group (p < 0.05). Although both groups improved 1RM, CMJ and T20, NAL experienced greater and significant changes than AL (28.90 vs.12.70%, 16.10 vs. 7.90% and −1.99 vs. −0.95%, respectively). Load adjustment based on movement velocity is a useful way to control for highly individualised responses to training and improve the implementation of RT programs.

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

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

Short-Term Creatine Loading Improves Total Work and Repetitions to Failure but Not Load-Velocity Characteristics in Strength-Trained Men.

This study assessed the effects of a 7-day creatine (CRE) supplementation on the load–velocity profile and repeated sub-maximal bouts in the deep squat using mean propulsive velocity (MPV) and mean propulsive power (MPP). Eleven strength-trained men (31.4 ± 5.4 years) supplemented 0.3 g·kg−1·d−1 CRE or a placebo (PLA, maltodextrin) for seven days in a randomized order, separated by a 30-day washout period. Prior to and after the supplementation, the subjects performed an incremental maximal strength (1RM) test, as well as 3 × 10 repetitions and a repetitions-to-failure test (RFT), all at 70% 1RM. Maximal strength remained statistically unaltered in CRE (p = 0.107) and PLA (p = 0.568). No statistical main effect for time (p = 0.780) or interaction (p = 0.737) was observed for the load–velocity profile. The number of repetitions during RFT remained statistically unaltered in both conditions (CRE: +16.8 ± 32.8%, p = 0.112; PLA: +8.2 ± 47.2%, p = 0.370), but the effect size was larger in creatine compared to placebo (g = 0.51 vs. g = 0.01). The total work during RFT increased following creatine supplementation (+23.1 ± 35.9%, p = 0.043, g = 0.70) but remained statistically unaltered in the placebo condition (+15.0 ± 60.8%, p = 0.801, g = 0.08; between conditions: p = 0.410, g = 0.25). We showed that CRE loading over seven days did not affect load–velocity characteristics but may have increased total work and power output during submaximal deep squat protocols, as was indicated by moderate effect sizes.

The Relationship Between Selected Load-Velocity Profile Parameters and 50 m Front Crawl Swimming Performance.

The purpose of the present study was to establish relationships between sprint front crawl performance and a swimming load-velocity profile. Fourteen male national-level swimmers performed 50 m front crawl and semi-tethered swimming with three progressive loads. The 50 m performance was recorded with a multi-camera system, with which two-dimensional head displacement and the beginning of each arm-stroke motion were quantified. Forward velocity (V50m), stroke length (SL) and frequency (SF) were quantified for each cycle, and the mean value of all cycles, excluding the first and last cycles, was used for the analysis. From the semi-tethered swimming test, the mean velocity during three stroke cycles in mid-pool was calculated and plotted as a function of the external load, and a linear regression line expressing the relationship between the load and velocity was established for each swimmer. The intercepts between the established line and the axes of the plot were defined as theoretical maximum velocity (V0) and load (L0). Large to very large correlations were observed between V50m and all variables derived from the load-velocity profiling; L0 (R = 0.632, p = 0.015), L0 normalized by body mass (R = 0.743, p = 0.002), V0 (R = 0.698, p = 0.006), and the slope (R = 0.541, p < 0.046). No significant relationships of SL and SL with V50m and the load-velocity variables were observed, suggesting that each swimmer has his own strategy to achieve the highest swimming velocity. The findings suggest that load-velocity profiling can be used to assess swimming-specific strength and velocity capabilities related to sprint front crawl performance.

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.