Search Results

Objective: To compare the short-term effect of power- and strength-oriented resistance-training programs on the individualized load–velocity profiles obtained during the squat (SQ) and bench-press (BP) exercises. Methods: Thirty physically active men (age = 23.4 [3.5] y; SQ 1-repetition maximum [1RM] = 126.5 [26.7] kg; BP 1RM = 81.6 [16.7] kg) were randomly assigned to a power- (exercises: countermovement jump and BP throw; sets per exercise: 4–6; repetitions per set: 5–6; load: 40% 1RM) or strength-training group (exercises: SQ and BP; sets per exercise: 4–6; repetitions per set: 2–8; load: 70%–90% 1RM). The training program lasted 4 wk (2 sessions/wk). The individualized load–velocity profiles (ie, velocity associated with the 30%–60%–90% 1RM) were assessed before and after training through an incremental loading test during the SQ and BP exercises. Results: The power-training group moderately increased the velocity associated with the full spectrum of % 1RM for the SQ (effect size [ES] range: 0.70 to 0.93) and with the 30% 1RM for the BP (ES: 0.67), while the strength-training group reported trivial/small changes across the load–velocity spectrum for both the SQ (ES range: 0.00 to 0.35) and BP (ES range: −0.06 to −0.33). The power-training group showed a higher increase in the mean velocity associated with all % 1RM compared with the strength-training group for both the SQ (ES range: 0.54 to 0.63) and BP (ES range: 0.25 to 0.53). Conclusions: The individualized load–velocity profile (ie, velocity associated with different % 1RM) of lower-body and upper-body exercises can be modified after a 4-wk resistance-training program.

Objective: To evaluate the sensitivity of the iLOAD® application to detect the changes in mean barbell velocity of complete sets following power- and strength-oriented resistance training (RT) programs. Methods: Twenty men were randomly assigned to a power training group (countermovement jump and bench press throw at 40% of the 1-repetition maximum [1RM]) or strength training group (back squat and bench press at 70% to 90% of 1RM). Single sets of 10 repetitions at 25% and 70% of 1RM during the back squat and bench press exercises were assessed before and after the 4-week RT programs simultaneously with the iLOAD® application and a linear velocity transducer. Results: The power training group showed a greater increment in velocity performance at the 25% of 1RM (effect size range = 0.66–1.53) and the 70% of 1RM (effect size range = 0.11–0.30). The percent change in mean velocity after the RT programs highly correlated between the iLOAD® application and the linear velocity transducer for the back squat (r range = .85–.88) and bench press (r range = .87–.93). However, the iLOAD® application revealed a 2% greater increase in mean velocity after training compared to the linear velocity transducer. Conclusions: The iLOAD® application is a cost-effective, portable, and easy-to-use tool which can be used to detect changes in mean barbell velocity after power- and strength-oriented RT programs.

Objective: To explore (1) the goodness of fit of generalized and individualized relationships between the maximum number of repetitions performed to failure (RTF) and the fastest mean velocity and peak velocity of the sets (RTF–velocity relationships), (2) the between-sessions reliability of mean velocity and peak velocity values associated with different RTFs, and (3) whether the errors in the prediction of the RTF under fatigued and nonfatigued conditions differ between generalized and individualized RTF–velocity relationships. Methods: Twenty-three sport-science students performed 4 testing sessions with the prone bench pull exercise in a Smith machine: a 1-repetition-maximum [1RM] session, 2 identical sessions consisting of singles sets of RTF against 4 randomized loads (60%–70%–80%–90%1RM), and 1 session consisting of 4 sets of RTF against the 75%1RM. Results: Individualized RTF–velocity relationships presented a higher goodness of fit (r ² = .96–.97 vs .67–.70) and accuracy (absolute errors = 2.1–2.9 repetitions vs 2.8–4.3 repetitions) in the prediction of the RTF than generalized RTF–velocity relationships. The reliability of the velocity values associated with different RTFs was generally high (average within-subject coefficient of variation = 4.01% for mean velocity and 3.98% for peak velocity). The error in the prediction of the RTF increased by ~1 repetition under fatigue (ie, set 1 vs sets 2–4). Conclusions: Individualized RTF–velocity relationships can be used with acceptable precision and reliability to prescribe the loads associated with a given RTF during the match a specific XRM during the prone bench pull exercise, but a lower accuracy is expected in a fatigued state.

Purpose: To explore the feasibility of the linear force–velocity (F–V) modeling approach to detect selective changes of F–V parameters (ie, maximum force [F ₀], maximum velocity [V ₀], F–V slope [a], and maximum power [P ₀]) after a sprint-training program. Methods: Twenty-seven men were randomly assigned to a heavy-load group (HLG), light-load group (LLG), or control group (CG). The training sessions (6 wk × 2 sessions/wk) comprised performing 8 maximal-effort sprints against either heavy (HLG) or light (LLG) resistances in leg cycle-ergometer exercise. Pre- and posttest consisted of the same task performed against 4 different resistances that enabled the determination of the F–V parameters through the application of the multiple-point method (4 resistances used for the F–V modeling) and the recently proposed 2-point method (only the 2 most distinctive resistances used). Results: Both the multiple-point and the 2-point methods revealed high reliability (all coefficients of variation <5% and intraclass correlation coefficients >.80) while also being able to detect the group-specific training-related changes. Large increments of F ₀, a, and P ₀ were observed in HLG compared with LLG and CG (effect size [ES] = 1.29–2.02). Moderate increments of V ₀ were observed in LLG compared with HLG and CG (ES = 0.87–1.15). Conclusions: Short-term sprint training on a leg cycle ergometer induces specific changes in F–V parameters that can be accurately monitored by applying just 2 distinctive resistances during routine testing.

This study determined the optimal resistive forces for testing muscle capacities through the standard cycle ergometer test (1 resistive force applied) and a recently developed 2-point method (2 resistive forces used for force-velocity modelling). Twenty-six men were tested twice on maximal sprints performed on a leg cycle ergometer against 5 flywheel resistive forces (R1–R5). The reliability of the cadence and maximum power measured against the 5 individual resistive forces, as well as the reliability of the force-velocity relationship parameters obtained from the selected 2-point methods (R1–R2, R1–R3, R1–R4, and R1–R5), were compared. The reliability of outcomes obtained from individual resistive forces was high except for R5. As a consequence, the combination of R1 (≈175 rpm) and R4 (≈110 rpm) provided the most reliable 2-point method (CV: 1.46%–4.04%; ICC: 0.89–0.96). Although the reliability of power capacity was similar for the R1–R4 2-point method (CV: 3.18%; ICC: 0.96) and the standard test (CV: 3.31%; ICC: 0.95), the 2-point method should be recommended because it also reveals maximum force and velocity capacities. Finally, we conclude that the 2-point method in cycling should be based on 2 distant resistive forces, but avoiding cadences below 110 rpm.

Objective: To compare the accuracy of different devices to predict the bench-press 1-repetition maximum (1RM) from the individual load–velocity relationship modeled through the multiple- and 2-point methods. Methods: Eleven men performed an incremental test on a Smith machine against 5 loads (45–55–65–75–85%1RM), followed by 1RM attempts. The mean velocity was simultaneously measured by 1 linear velocity transducer (T-Force), 2 linear position transducers (Chronojump and Speed4Lift), 1 camera-based optoelectronic system (Velowin), 2 inertial measurement units (PUSH Band and Beast Sensor), and 1 smartphone application (My Lift). The velocity recorded at the 5 loads (45–55–65–75–85%1RM), or only at the 2 most distant loads (45–85%1RM), was considered for the multiple- and 2-point methods, respectively. Results: An acceptable and comparable accuracy in the estimation of the 1RM was observed for the T-Force, Chronojump, Speed4Lift, Velowin, and My Lift when using both the multiple- and 2-point methods (effect size ≤ 0.40; Pearson correlation coefficient [r] ≥ .94; standard error of the estimate [SEE] ≤ 4.46 kg), whereas the accuracy of the PUSH (effect size = 0.70–0.83; r = .93–.94; SEE = 4.45–4.80 kg), and especially the Beast Sensor (effect size = 0.36–0.84; r = .50–.68; SEE = 9.44–11.2 kg), was lower. Conclusions: These results highlight that the accuracy of 1RM prediction methods based on movement velocity is device dependent, with the inertial measurement units providing the least accurate estimate of the 1RM.

Purpose: To compare the load–velocity relationship between 4 variants of the bench-press (BP) exercise. Methods: The full load–velocity relationship of 30 men was evaluated by means of an incremental loading test starting at 17 kg and progressing to the individual 1-repetition maximum (1RM) in 4 BP variants: concentric-only BP, concentric-only BP throw (BPT), eccentric-concentric BP, and eccentric-concentric BPT. Results: A strong and fairly linear relationship between mean velocity (MV) and %1RM was observed for the 4 BP variants (r ² > .96 for pooled data and r ² > .98 for individual data). The MV associated with each %1RM was significantly higher in the eccentric-concentric technique than in the concentric-only technique. The only significant difference between the BP and BPT variants was the higher MV with the light to moderate loads (20–70%1RM) in the BPT using the concentric-only technique. MV was significantly and positively correlated between the 4 BP variants (r = .44–.76), which suggests that the subjects with higher velocities for each %1RM in 1 BP variant also tend to have higher velocities for each %1RM in the 3 other BP variants. Conclusions: These results highlight the need for obtaining specific equations for each BP variant and the existence of individual load–velocity profiles.

This study aimed to examine the validity of mechanical variables obtained by a linear velocity transducer from the unconstrained and constrained squat jump (SJ). Twenty-three men were tested on the unconstrained SJ and the SJ constrained by a Smith machine. Maximum values of force, velocity, and power were simultaneously recorded both by a linear velocity transducer attached to a bar of mass of 17, 30, 45, 60, and 75 kg and by a force plate. Linear velocity transducer generally overestimated the outcomes measured as compared to the force plate, particularly in unconstrained SJ. Bland-Altman plots revealed that heteroscedasticity of errors was mainly observed for velocity variables (r ² = .26–.58) where the differences were negatively associated with the load magnitude. However, exceptionally high correlations were observed between the same outcomes recorded with the 2 methods in both unconstrained (median r = .89 [.71–.95]) and constrained SJ (r = .90 [.65–.95]). Although the systematic and proportional bias needs to be acknowledged, the high correlations between the variables obtained by 2 methods suggest that the linear velocity transducer could provide valid values of the force, velocity, and power outputs from both unconstrained and constrained SJ.

This study examined the effect of different coaching conditions on the magnitude and reliability of drop jump height in men and women. Nineteen collegiate sport sciences students (10 men) performed two sets of 10 drop jumps under four different coaching conditions: neutral, augmented feedback, external focus of attention, and a combination of augmented feedback and external focus of attention. The augmented feedback condition revealed a significantly higher jump height than the neutral condition (p = .002), while no significant differences were observed for the remaining conditions (p ≥ .38). The external focus of attention condition was more reliable than the neutral and augmented feedback conditions (coefficient of variation_ratio ≥ 1.15), while no differences were observed between the remaining conditions. These results suggest that both the magnitude and reliability of the drop jump height performance are influenced by the coaching condition.

This study aimed to compare the between-session reliability of the load–velocity relationship between (1) linear versus polynomial regression models, (2) concentric-only versus eccentric–concentric bench press variants, as well as (3) the within-participants versus the between-participants variability of the velocity attained at each percentage of the 1-repetition maximum. The load–velocity relationship of 30 men (age: 21.2 [3.8] y; height: 1.78 [0.07] m, body mass: 72.3 [7.3] kg; bench press 1-repetition maximum: 78.8 [13.2] kg) were evaluated by means of linear and polynomial regression models in the concentric-only and eccentric–concentric bench press variants in a Smith machine. Two sessions were performed with each bench press variant. The main findings were: (1) first-order polynomials (coefficient of variation: 4.39%–4.70%) provided the load–velocity relationship with higher reliability than the second-order polynomials (coefficient of variation: 4.68%–5.04%); (2) the reliability of the load–velocity relationship did not differ between the concentric-only and eccentric–concentric bench press variants; and (3) the within-participants variability of the velocity attained at each percentage of the 1-repetition maximum was markedly lower than the between-participants variability. Taken together, these results highlight that, regardless of the bench press variant considered, the individual determination of the load–velocity relationship by a linear regression model could be recommended to monitor and prescribe the relative load in the Smith machine bench press exercise.