Load-velocity Profiles Research Articles

The relationship between maximal lactate accumulation rate and sprint performance parameters in male competitive swimmers.

This study aimed to examine the relationship between the maximal lactate accumulation rate (ċLamax) and sprint performance parameters in male competitive swimmers. Seventeen male competitive swimmers volunteered to perform a 20 m maximal front crawl sprint without pushing off the wall from a floating position. ċLamax was determined by the 20-m sprint time and blood lactate measured before and after the 20 m sprint. For the sprint performance parameter, a 50 m time trial with the front crawl swimming stroke was conducted, and the times taken from 0 to 15 m, 15-25 m, 25-35 m, and 35-45 m were analyzed. A semi-tethered swimming test was conducted to investigate the load-velocity profile of each swimmer. From the load-velocity profile, theoretical maximal velocity (V0), maximal load (L0) and relative maximal load (rL0) were examined. The slope of the load-velocity profile was also determined. According to the results, ċLamax correlated with 50 m front crawl performance (r = -.546, p < .05). Moreover, a higher ċLamax was related to faster 0-35 m section time. Furthermore, ċLamax correlated with L0 (r = .837, p < .01), rL0 (r = .820, p < .01), and load-velocity slope (r = .804, p < .01). ċLamax is a good indicator of 50 m front crawl performance in male swimmers, and higher glycolytic power contributes to the faster time at the beginning of the sprint race. ċLamax could also evaluate the ability of a swimmer to apply force to the water during high-intensity swimming.

Exploring the validity of perceived velocity in lower-limb resistance exercises with a cluster-set configuration

This feasibility study aimed to explore the relationship between mean propulsive velocity (MPV) and a scale of perceived velocity (SPV) in back-squat and deadlift exercises performed with heavy loads during a cluster-sets resistance training (CS-RT). Twelve resistance trained males (24.1[2.94] years; 80.7[9.05] kg; 172[4.7] cm; 19.1[6.17] %BF; 4.71[2.72] years of training experience) participated. Participants visited the laboratory three times, spaced 72 to 96 hours. Load-velocity profiles for each exercise were measured in first visit. During the second and third visits, participants engaged in CS-RT sessions with different intra-set rest period (20 vs 40 seconds, randomly), and consisted of three sets of squat and deadlift exercises at 80%1-RM. Each set concluded upon reaching a 10% velocity loss on two occasions. Bayesian Pearson correlation coefficients (r), 95% credible intervals (95%CrI) and Bayes factors (BF10) were computed to assess the relationship between variables. A low positive correlation was observed between MPV and SPV in deadlift (r=0.368, 95%CrI [0.144, 0.544]), with strong evidence supporting the alternative hypothesis (BF10=20.7). Interestingly, moderate correlation values were observed in the 40-second CS-RT configuration (r=0.47, 95%CrI [0.144, 0.544]) and in the first set of the deadlift (r=0.44, 95%CrI [0.118, 0.654]). Conversely, a negligible Bayesian correlation was identified for squat (r=0.101, 95%CrI [-0.132, 0.319]), with substantial evidence favoring the null hypothesis (BF10=0.208). In conclusion, a positive correlation between MPV and SPV in deadlift during a CS-RT configuration, indicating potential utility for perceived velocity. However, velocity feedback prior SPV use and validity for squatting warrants further investigation. Keywords: Strength training, Perception, Powerlifting, Physical performance, Cluster training.

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

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

Are Load-Velocity Estimates of Bench Press Maximal Strength as Accurate as Actual 1-Repetition Maximum Testing?

Cabarkapa, DV, Fry, AC, Kavadas, NG, and Cabarkapa, D. Are load-velocity estimates of bench press maximal strength as accurate as actual 1-repetition maximum testing? J Strength Cond Res XX(X): 000-000, 2024-The purpose of the present investigation was to determine if using maximal velocity measures while lifting submaximal loads as a predictor of bench press maximal strength (i.e., 1 repetition maximum [1RM]) is more accurate than the actual 1RM test and determine which specific submaximal loads best estimate 1RM bench press strength with the lowest variability when compared with actual 1RM tests. Sixteen recreationally trained subjects performed 5 testing sessions. The first and second sessions included the actual 1RM bench press testing, whereas the remaining 3 sessions consisted of performing one repetition of a bench press exercise in a series of incremental loads, starting at 20% 1RM and increasing the resistance by 10% until reaching the 90% of individual's 1RM. For each participant, linear regressions using bar velocities at each relative load were used to estimate 1RM capabilities, using the predetermined 1RM barbell velocities from actual 1RM testing. The results of the present investigation indicated the following: (a) actual bench press 1RM can be a highly reliable assessment of maximal strength; (b) having a greater number of loads included in the equations increases the accuracy of 1RM estimation; (c) practitioners should incorporate light (e.g., 20% 1RM) and heavy (e.g., 80 and/or 90% 1RM) loads when estimating 1RM from load-velocity profiles; and (d) most load-velocity regression equations for estimating strength are not as accurate as actual 1RM tests for the free-weight bench press. Those who use load-velocity testing to estimate 1RM strength must be willing to accept the accompanying error for most loading protocols.

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

ABSTRACT The purpose of this study was to analyse the load-velocity and load-power relationships of the decline bench press exercise (DBPE) and to compare sex-related differences. Twelve young healthy men and women performed a progressive loading test for the determination of 1RM strength and individual load-velocity and load-power relationship in the DBPE. A very close relationship between mean propulsive velocity (MPV) and %1RM was observed (R 2 = 0.94). This relationship improved when plotting data separately by sex (R 2 = 0.96–97). Individual load-velocity profiles gave an R 2 = 0.99 ± 0.01. The relationship between mean propulsive power (MPP) and %1RM was R 2 = 0.23. When separating data by sex, R 2 = 0.64–73 were obtained. Individual load-power profiles gave an R 2 of 0.93 ± 0.07. Significant sex-related differences were found for MPV, with males having faster velocities than females from 30% to 40% 1RM (p = 0.01) and for MPP, with males having greater MPP (W) than females from 30% to 95% 1RM (p < 0.001). The results of this study show that a strong correlation exists between relative load and MPV/MPP in the DBPE, allowing the possibility of using one to predict the other with great precision, especially when a sex-specific equation is used.

Can the Velocity of a 1RM Hang Power Clean Be Used to Estimate a 1RM Hang High Pull?

Suchomel, TJ, Techmanski, BS, Kissick, CR, and Comfort, P. Can the velocity of a 1RM hang power clean be used to estimate a 1RM hang high pull? J Strength Cond Res 38(7): 1321-1325, 2024-The purpose of this study was to estimate the 1-repetition maximum hang high pull (1RM HHP) using the peak barbell velocity of a 1RM hang power clean (HPC). Fifteen resistance-trained men (age = 25.5 ± 4.5 years, body mass = 88.3 ± 15.4 kg, height = 176.1 ± 8.5 cm, relative 1RM HPC = 1.3 ± 0.2 kg·kg-1) with previous HPC experience participated in 2 testing sessions that included performing a 1RM HPC and HHP repetitions with 20, 40, 60, and 80% of their 1RM HPC. Peak barbell velocity was measured using a linear position transducer during the 1RM HPC and HHP repetitions performed at each load. The peak barbell velocity achieved during the 1RM HPC was determined as the criterion value for a 1RM performance. Subject-specific linear regression analyses were completed using slope-intercept equations created from the peak velocity of the 1RM HPC and the peak barbell velocities produced at each load during the HHP repetitions. The peak barbell velocity during the 1RM HPC was 1.74 ± 0.30 m·s-1. The average load-velocity profile showed that the estimated 1RM HHP of the subjects was 98.0 ± 19.3% of the 1RM HPC. Although a 1RM HHP value may be estimated using the peak barbell velocity during the HPC, strength and conditioning practitioners should avoid this method because of the considerable variation within the measurement. Additional research examining different methods of load prescription for weightlifting pulling derivatives is needed.

Load-Velocity Profiles Before and After Heated Resistance Exercise.

Sweet, DK, Qiao, J, Rosbrook, P, and Pryor, JL. Load-velocity profiles before and after heated resistance exercise. J Strength Cond Res 38(6): 1019-1024, 2024-This study examined neuromuscular performance using load-velocity (L-V) profiles in men and women before and after resistance exercise (RE) in hot (HOT; 40° C) and temperate (TEMP; 21° C) environments. Sixteen (f = 8, m = 8) resistance-trained individuals completed a single 70-minute whole-body high-volume load (6 exercises, 4 sets of 10 repetitions) RE bout in HOT and TEMP. Before and after RE, rectal temperature (TRE), muscle temperature of the vastus lateralis (TVL) and triceps brachii (TTB), and an L-V profile for the deadlift and bench press were recorded. Thermoregulatory and L-V data were analyzed using separate 2-way repeated measures analysis of variances (ANOVAs; condition [hot, temperate] and time [pre, post]) with significance level set at p ≤ 0.05. Deadlift peak velocity was reduced at 60% 1 repetition maximum (1RM) after RE in HOT but not TEMP. Peak velocity of 40% 1RM bench press was lower in TEMP vs. HOT pre-RE (p < 0.01). Peak velocity was decreased at all loads in the deadlift L-V profile after RE, regardless of condition. Despite elevated TRE (TEMP; 37.58 ± 0.35, HOT; 38.20 ± 0.39° C), TVL (TEMP; 35.24 ± 0.62, HOT; 37.92 ± 0.55° C), and TTB (TEMP; 35.05 ± 0.78, HOT; 38.00 ± 0.16° C) after RE in HOT vs. TEMP (p < 0.01), RE in HOT did not broadly affect L-V profiles. This indicates heated resistance exercise can be performed with high-volume load and high ambient temperature with minimal performance impairment.

Validation of a Single-Session Protocol to Determine the Load-Velocity Profile and One-Repetition Maximum for the Back Squat Exercise.

Gomes, M, Fitas, A, Santos, P, Pezarat-Correia, P, and Mendonca, GV. Validation of a single session protocol to determine the load-velocity profile and one-repetition maximum for the back squat exercise. J Strength Cond Res 38(6): 1013-1018, 2024-We investigated whether a single session of absolute incremental loading is valid to obtain the individual load-velocity profile (LVP) and 1 repetition maximum (1RM) for the free-weight parallel back squat. Twenty strength-trained male subjects completed 3 testing sessions, including a baseline 1RM session and 2 LVP sessions (LVP rel based on incremental relative loads and LVP abs based on absolute load increments until 1RM). The 1RM load was compared between the baseline and LVP abs . The load at zero velocity (load-axis intercept [L 0 ]), maximal velocity capacity (velocity-axis intercept [V 0 ]), slope, and area under the load-velocity relationship line (A line ) were compared between the LVP rel and LVP abs using equivalence testing through 2 one-sided t -tests. Measurement accuracy was calculated using the absolute percent error. The 1RM measured at baseline and LVP abs was equivalent and presented a low absolute percent error (1.2%). The following LVP parameters were equivalent between LVP rel and LVP abs : 1RM, L 0 , and A line because the mean difference between sessions was close to zero and the Bland-Altman limits of agreement (1RM:5.3 kg; L 0 :6.8 kg; A line : 9.5 kg·m -1 ·s -1 ) were contained within the a priori defined ± equivalent margins (5% for 1RM and L 0 and 10% for A line ). The aforementioned variables presented a low absolute percent error. However, slope and V 0 were not equivalent between sessions. In conclusion, a single session of absolute incremental loading is a valid approach to obtain the L 0 and A line of the individual LVP and 1RM, and can be used to efficiently track the magnitude of neuromuscular adaptations throughout the training cycles for the free-weight back squat.

Lower and upper extremity contributions to propulsion and resistance during semi-tethered load-velocity profiling in front crawl swimming

ABSTRACT The study estimated lower and upper extremity contributions to whole-body front crawl swimming using semi-tethered load-velocity profiling. Nine female and 11 male (inter)national-level swimmers performed 20 m semi-tethered sprints, each with five progressive loads for lower (leg kicking), upper (arm stroke), and whole-body front crawl movements. The theoretical maximal speed (v0) and load (L0), and active drag (Da) were expressed as a percentage of the sum of both extremities for the movements of each extremity to calculate their contributions. The difference of whole-body values minus the sum of both extremities was used to estimate whole-body reserves. Lower (upper) body contributions were 43.8 ± 2.8% (56.2%) for v0, 37.3 ± 7.1% (62.7%) for L0, and 39.6 ± 5.6% (60.4%) for Da. Statistically significant whole-body reserves were found for v0 (−30.9 ± 3.9%, p < 0.001) and Da (−5.7 ± 11.7%, p = 0.04). V0 reserves correlated very highly with whole-body v0 in males (r = 0.71, p = 0.014) and moderately in females (r = 0.47, p = 0.21). The lower extremities contribute substantially to front crawl load-velocity profiles of highly trained swimmers. Higher sprint swimming speeds are associated with an efficient speed transfer from lower- and upper- to whole-body movement.

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

The present study aimed to establish differences in load-velocity profiling, active drag (AD), and drag coefficient (Cd) between 3 age groups of female swimmers. Thirty-three swimmers (11, 13, or 16y old) were recruited. The individual load-velocity profile was determined for the 4 competitive swimming strokes. The maximal velocity (V0), maximal load (L0), L0 normalized to the body mass, AD, and Cd were compared between the groups. A 2-way analysis of variance and correlation analysis were conducted. Compared with their younger counterparts, 16-year-old swimmers generally had larger V0, L0, and AD, which was particularly evident when comparing them with 11-year-old swimmers (P ≤ .052). The exception was breaststroke, where no differences were observed in L0 and AD and Cd was smaller in the 16-year-old group than the 11-year-old group (P = .03). There was a negative correlation between Cd and V0 for all groups in backstroke (P ≤ .038) and for the 11-year-old group and 13-year-old group in breaststroke (P ≤ .022) and front crawl (P ≤ .010). For the 16-year-old group, large correlations with V0 were observed for L0, L0 normalized to the body mass, and AD (P ≤ .010) in breaststroke and for L0 and AD with V0 in front crawl (P ≤ .042). In butterfly, large negative correlations with V0 were observed in the 13-year-old group for all parameters (P ≤ .027). Greater propulsive force is likely the factor that differentiates the oldest age group from the younger groups, except for breaststroke, where a lower Cd (implying a better technique) is evident in the oldest group.

Reliability of Three Landmine-Punch-Throw Variations and Their Load-Velocity Relationships Performed With the Dominant and Nondominant Hands.

This study assessed the reliability and load-velocity profiles of 3 different landmine-punch-throw variations (seated without trunk rotation, seated with trunk rotation, and standing whole body) with different loads (20, 22.5, and 25.0kg), all with the dominant hand and nondominant hand. In a quasi-randomized order, 14 boxers (24.1 [4.3]y, 72.6 [10.1]kg) performed 3 repetitions of each variation with their dominant hand and theirnondominant hand, with maximal effort and 3minutes of interset rest. Peak velocity was measured via the GymAware Power Tool (Kinetic Performance Technologies). The interclass correlation coefficients and their 95% CIs were used to determine the intrasession reliability of each variation × load × hand combination. Additionally, a 2 (hand) × 3 (variation) repeated-measures analysis of variance assessed the load-velocity profile slope, and a 3 (variation) × 2 (hand) × 3 (load) repeated-measures analysis of variance assessed the peak velocity of each variation. Most variations were highly reliable (intraclass correlation coefficient > .91), with the nondominant hand being as reliable or more reliable than the dominant hand. Very strong linear relationships were observed for the group average for each variation (R2 ≥ .96). However, there was no variation × hand interaction for the slope, and there was no main effect for variation or hand. Additionally, there was no interaction for the peak velocity, but there were main effects for variation, hand, and load (P < .01). Each variation was reliable and can be used to create upper-body ballistic unilateral load-velocity profiles. However, as with other research onload-velocity profile, individual data allowed for more accurate profiling than group average data.

Associations between load-velocity profiling and race parameters of elite swimmers in the 100 and 200m freestyle events.

Improving swimming performance involves assessments of biomechanical variables of the stroke, and it can be achieved using semi-tethered swimming tests. The aim of this study was thus to investigate the associations between load-velocity (L-V) profiles, from a semi-tethered swimming protocol and race variables in the 100 m and 200 m freestyle events. Eight swimmers completed a L-V profiling protocol consisting of four sprints (25 m, 25 m, 20 m, 15 m) against increasing loads (0.1, 2.0, 4.0, 6.0 kg respectively) with complete recovery between repetitions (>5 min). The L-V linear regression was used to estimate maximal velocity (V0) and body mass normalized load (rL0). Race variables such as clean swimming speed (V), stroke rate (SR), distance per cycle (SL) and stroke index (SI) were assessed from video analysis of 100 m and 200 m freestyle events taking place 3-4 days after the L-V protocol. L-V results showed high levels of speed (mean ± SD: 1.87 ± 0.04 m/s) and heavy maximal relative loads (mean ± SD: 38.5 ± 6.51 as % of body mass). Swimmers also achieved high-level performances in the 100 m (mean ± SD time: 51.95 ± 0.75 s) and the 200 m (mean ± SD time: 113.85 ± 2.67 s). For the 100 m events, the maximal relative load showed strong correlation with performance (r = 0.63) whereas trivial correlation was observed for the 200 m events (r = 0.12). SR on the 100 m and the 200 m also showed very strong association with rL0 (r = 0.83) and a strong association with V0 (r = 0.68) respectively. The relationships between L-V variables and race variables depend on the distance of the event. However, L-V variables seem to be less related to SR and SL evolutions for the 100 m than in the 200 m event. Moreover, L-V profiles tend to be more related to the 100 m than 200 m freestyle performance. L-V profile should be interpreted taking into consideration the specific physiological and biomechanical constraints of the main events of the swimmer.

Prediction of One Repetition Maximum in Free-Weight Back Squat Using a Mixed Approach: The Combination of the Individual Load-Velocity Profile and Generalized Equations.

Fitas, A, Santos, P, Gomes, M, Pezarat-Correia, P, Schoenfeld, BJ, and Mendonca, GV. Prediction of one repetition maximum in free-weight back squat using a mixed approach: the combination of the individual load-velocity profile and generalized equations. J Strength Cond Res 38(2): 228-235, 2024-We aimed to develop a mixed methods approach for 1 repetition maximum (1RM) prediction based on the development of generalized equations and the individual load-velocity profile (LVP), and to explore the validity of such equations for 1RM prediction. Fifty-seven young men volunteered to participate. The submaximal load-velocity relationship was obtained for the free-weight parallel back squat. The estimated load at 0 velocity (LD0) was used as a single predictor, and in combination with the slope of the individual LVP, to develop equations predictive of 1RM. Prediction accuracy was determined through the mean absolute percent error and Bland-Altman plots. LD0 was predictive of 1RM ( p < 0.0001), explaining 70.2% of its variance. Adding the slope of the LVP to the model increased the prediction power of 1RM to 84.4% ( p < 0.0001). The absolute percent error between actual and predicted 1RM was lower for the predictions combining LD0 and slope (6.9 vs. 9.6%). The mean difference between actual and estimated 1RM was nearly zero and showed heteroscedasticity for the LD0 model, but not for the combined model. The limits of agreement error were of 31.9 and 23.5 kg for LD0 and LD0 combined with slope, respectively. In conclusion, the slope of the individual LVP adds predictive value to LD0 in 1RM estimation on a group level and avoids error trends in the estimation of 1RM over the entire spectrum of muscle strength. However, the use of mixed methods does not reach acceptable accuracy for 1RM prediction of the free-weight back squat on an individual basis.

Influence of Body Composition, Load-Velocity Profiles, and Sex-Related Differences on Army Combat Fitness Test Performance.

Boffey, D, DiPrima, JA, Kendall, KL, Hill, EC, Stout, JR, and Fukuda, DH. Influence of body composition, load-velocity profiles, and sex-related differences on army combat fitness test performance. J Strength Cond Res 37(12): 2467-2476, 2023-The Army Combat Fitness Test (ACFT) became the U.S. Army's mandatory physical fitness test in April of 2022. The purpose of this study was to determine the relationship between ACFT performance and both body composition and velocity profiles and to determine sex differences for these variables. Data were collected at 2 timepoints 4 months apart, from male (n = 55) and female (n = 17) Army Reserve Officers' Training Corps (ROTC) cadets. Body composition was assessed with a bioelectrical impedance spectroscopy device, and cadets completed a hex bar deadlift load-velocity profile (LVP) and ACFT on separate days. Stepwise multiple regressions were used to explain the amount of variance in ACFT total score and individual event performance. Significance for statistical tests was defined as an alpha level of p ≤ 0.05. Muscle mass and body fat percentage accounted for 49% of shared variance of total ACFT score, and deadlift maximal power and maximal velocity accounted for 67% of shared variance of total ACFT score. The 3 repetition maximum deadlift, standing power throw, hand-release push-up, and sprint-drag-carry events favored cadets with more muscle mass, whereas the leg tuck was influenced by the body fat percentage and the 2-mile run was affected by fat mass. Sex had greater predictive capability for the 2-mile run than body composition. Men outperformed women on all individual events, with the greatest differences on standing power throw and sprint-drag-carry. It is recommended that Army ROTC cadets taking the ACFT maximize lower-body power production and increase muscle mass.

Development and Interplay of Metabolic and Mechanical Performance Determinants Over an Annual Training Period in Adolescent National-Level Squad Swimmers.

The study examined the longitudinal interplay of anthropometric, metabolic, and neuromuscular development related to performance in adolescent national-level swimmers over 12months. Seven male and 12 female swimmers (14.8 [1.3]y, FINA [International Swimming Federation] points 716 [51]) were tested before (T0) and after the preparation period (T1), at the season's peak (T2), and before the next season (T3). Anthropometric (eg, fat percentage) and neuromuscular parameters (squat and bench-press load-velocity profile) were assessed on dry land. Metabolic (cost of swimming [C], maximal oxygen uptake [V˙O2peak], and peak blood lactate [bLapeak]) and performance (sprinting speed [vsprint] and lactate thresholds [LT1 and 2]) factorswere determined using a 500-m submaximal, 200-m all-out, 20-second sprint, and incremental test (+0.03m·s-1, 3min), respectively, in front-crawl swimming. vsprint (+0.6%) and LT1 and 2 (+1.9-2.4%) increased trivially and slightly, respectively, from T0 to T2 following small to moderate strength increases (≥+10.2%) from T0 to T1 and V˙O2peak (+6.0%) from T1 to T2. Bench-press maximal strength and peak power correlated with vsprint from T0 to T2 (r ≥ .54, P < .05) and LT2 at T1 (r ≥ .47, P < .05). Changes in fat percentage and V˙O2peak (T2-T1 and T3-T2, r ≤ -.67, P < .01) and C and LT2 (T2-T0, r = -.52, P = .047) were also correlated. Increases in strength and V˙O2peak from preparation to the competition period resulted in improved sprint and endurance performance. Across the season, upper-body strength was associated with vsprint and LT2, although their changes were unrelated.

Sex Differences in the Load-Velocity Profiles of Three Different Row Exercises.

This study examined the force-velocity profile differences between men and women in three variations of row exercises. Twenty-eight participants (14 men and 14 women) underwent maximum dynamic strength assessments in the free prone bench row (PBR), bent-over barbell row (BBOR), and Smith machine bent-over row (SMBOR) in a randomized order. Subjects performed a progressive loading test from 30 to 100% of 1-RM (repetition maximum), and the mean propulsive velocity was measured in all attempts. Linear regression analyses were conducted to establish the relationships between the different measures of bar velocity and % 1-RM. The ANOVAs applied to the mean velocity achieved in each % 1-RM tested revealed significantly higher velocity values for loads < 65% 1-RM in SMBOR compared to BBOR (p < 0.05) and higher velocities for loads < 90% 1-RM in SMBOR compared to PBR (p < 0.05) for both sexes. Furthermore, men provided significantly higher velocity values than women (PBR 55-100% 1-RM; BBOR and SMBOR < 85% 1-RM; p < 0.05) and significant differences were found between exercises and sex for 30-40% 1-RM. These results confirm that men have higher velocities at different relative loads (i.e., % 1-RM) compared to women during upper-body rowing exercises.

Velocity-Based Training—A Critical Review

ABSTRACT Velocity-based training (VBT) is an increasingly popular programming strategy used by strength and conditioning professionals to develop their athlete's ability to express force rapidly. To implement the varying forms of VBT effectively within their training regimes, strength and conditioning professionals need to understand the strengths and weaknesses of strategies, such as predicting 1 repetition maximum using the load-velocity profile, modulating training loads using the load-velocity profile, and controlling training volume using the magnitude of velocity-loss. The aim of this review was to highlight these strengths and weaknesses and then provide practical examples of when each programming strategy may be most effectively implemented.

Load-velocity Profile Responses To Heated Resistance Exercise In Males

Load-velocity Profile Responses To Heated Resistance Exercise In Males

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

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

The physical determinants of maximal jumping time of flight in elite trampolining

ABSTRACT Time of flight (ToF) is an objective scoring component of elite trampolining, assessed in training by maximal jump tests. The aim of this study was to assess the relationship between physical floor based performance meaures and 20-maximum ToF. Thirty-two elite level gymnasts (13 senior; 19 junior) performed a battery of floor based tests and a 20-maximum jump test. Floor based tests included cycling peak power output, reactive strength index (RSI), unloaded countermovement jumps (CMJ), and loaded CMJ’s to construct a load-velocity profile for prediction of theoretical maximum force (CMJ F0). Very large and large, positive bivariate relationships were observed between CMJ F0 and ToF for the seniors (r = 0.85) and juniors (r = 0.56), respectively. Very large, positive bivariate relationships were observed between CMJ height and total ToF for both seniors (r = 0.74) and juniors (r = 0.77). Step-wise multiple regression analyses revealed CMJ F0 predicted 72% of ToF variability between seniors, and CMJ height (59%), 10 to 5 RSI (13%), and CMJ F0 (10%) predicting 82% of ToF variability between juniors. This suggests CMJ F0, lower limb maximal isometric capabilities, and CMJ height are important floor based predictors of maximal ToF in elite gymnasts.