Optimal load for a torque-velocity relationship test during cycling

Krüger, Renata Lopes; Peyrard, Arthur; Domenico, Hervé di; Rupp, Thomas; Millet, Guillaume Y.; Samozino, Pierre

doi:10.1007/s00421-020-04454-x

Cited by 8 publications

(7 citation statements)

References 25 publications

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“…In this example, this difference in mean power produced during 1‐s (ie, P max ) and 15‐s ( P max (15‐ s )) is associated with (i) a decrease of the optimal pedaling rate from 124 rpm ( v opt ) to 113 rpm ( v opt (15‐ s )), which corresponds to a decrease of 9,2%, and (ii) a decrease of the optimal force from 9.0 N.kg −1 ( F opt ) to 8.1 N.kg −1 ( F opt (15‐ s )), which corresponds to a decrease of 10.2%. These findings are in agreement with the shift to the left and to the down of the power‐velocity relationship with fatigue reported by MacIntosh et al (2004) at the end of a 30‐s Wingate test, 25 and highlighted a decrease in both force and velocity capacities after a fatigue exercise, while maintaining a linear force‐velocity relationship 26 . Since the road‐racing sprints had different durations and the pedaling rates used differed between races, this profile was used as a reference to interpret the mean power data produced during the different sprints.…”

Section: Discussionsupporting

confidence: 92%

“…These findings are in agreement with the shift to the left and to the down of the power-velocity relationship with fatigue reported by MacIntosh et al ( 2004) at the end of a 30-s Wingate test, 25 and highlighted a decrease in both force and velocity capacities after a fatigue exercise, while maintaining a linear force-velocity relationship. 26 Since the road-racing sprints had different durations and the pedaling rates used differed between races, this profile was used as a reference to interpret the mean power data produced during the different sprints. Considering the average results of the eleven road-racing sprints, we noticed that the mean power output during sprinting represented 89.4 ± 9.8% of the theoretical maximal mean power output for the corresponding pedaling rate and duration (Table 1).…”

Section: Power-velocity-endurance Profile and Mean Power Output Durin...mentioning

confidence: 99%

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Analysis of elite road‐cycling sprints in relation to maximal power‐velocity‐endurance profile: a longitudinal one‐case study

Robin

Nordez

Dorel

2021

Scandinavian Med Sci Sports

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The aims of the present study were to characterize the mechanical output of final road sprints of an elite sprinter during international competitions in relation to his power-velocity-endurance characteristics and to investigate the relationship between this sprint performance and the power produced during preceding phases of the race. The sprinter performed a set of short and long sprints (5 to 15-s) on a cycle ergometer to determine his maximal power-velocity-endurance profile. Based on eleven races, the distribution of power throughout each race, peak and mean power (P peak and P mean ) and associated pedaling rates (v Ppeak and v Pmean ) during the final sprint were analyzed. The power-velocity-endurance profile of the sprinter indicated that his theoeretical mean maximal power and corresponding optimal pedaling rate ranged from 20.0 W.kg −1 (124 rpm) for a 1-s sprint to 15.0 W.kg −1 (109 rpm) for 20 s. Race data showed that final road sprints were mainly performed on the ascending limb of the power-velocity relationship (v Ppeak , 104 ± 8 and v Pmean , 101 ± 8 rpm). Additionally, P peak and P mean were lower than the theoretical maximal power determined from the power-velocityendurance profile (9.9 ± 7.0% and 10.6 ± 9.8%, respectively), which highlighted a significant state of fatigue induced by the race. Finally, sprint power exhibited a high variability between races and was strongly related to the level of power produced during the last minute before the sprint. These findings show the importance of considering both the power-velocity-endurance qualities and the power demand of the last lead-up phase before the sprint in order to optimize final sprint performance. K E Y W O R D Sfatigue, force-velocity relationship, power-time curve, record power profile in sprint cycling, road race power demand, top-level performance

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Section: Discussionsupporting

confidence: 92%

Section: Power-velocity-endurance Profile and Mean Power Output Durin...mentioning

confidence: 99%

Analysis of elite road‐cycling sprints in relation to maximal power‐velocity‐endurance profile: a longitudinal one‐case study

Robin

Nordez

Dorel

2021

Scandinavian Med Sci Sports

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“…In the previous research two different approaches were applied. Firstly, loads are set to percentage of body weight ( Vargas et al, 2015 ; Krüger et al, 2020 ). Secondly, based on absolute loads expressed in kilograms ( Jaafar et al, 2016 ; Nikolaidis and Knechtle, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

“…Interestingly, relatively less attention has been paid to the number of sprints used for FVT. For example, Kruger et al (2020) used only two sprints for this purpose. In turn, Nikolaidis and Knechtle (2021) used four 7-s sprints with a load of 2, 3, 4, and 5 kg, MacIntosh et al (2003) five 4–7-s sprints, while Rudsits et al (2018) six 6-s sprints.…”

Section: Introductionmentioning

confidence: 99%

Determination of optimal load in the Wingate Anaerobic Test is not depend on number of sprints included in mathematical models

et al. 2023

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Determining the optimal load (OPTLOAD) in measuring mechanical peak power output (PPO) is important in assessment of anaerobic fitness. The main goals of this study were: 1) to examine estimated optimal load and PPO based on a force-velocity test and 2) to compare the PPO from the previous method with the Wingate Anaerobic Test (WAnT). The study involved 15 academic male athletes, aged 22.4 ± 2.3 (years), height 178.9 ± 6.8 (cm), and body weight 77.9 ± 12.2 (kg). They performed the 30-s WAnT (7.5% of body weight) during the first visit to the laboratory. Second to fourth session included a force-velocity test (FVT) involving three, 10-s all-out sprints. A randomized load ranging from 3 to 11 kg was used in each session for FVT. The OPTLOAD and PPO were computed using quadratic relationships based on power-velocity (P-v) and power-percent of body weight (P-%BM) and including three, four, five and nine sprints from FVT. The results showed non-difference in OPTLOAD [13.8 ± 3.2 (%BM); 14.1 ± 3.5 (%BM); 13.5 ± 2.8 (%BM); 13.4 ± 2.6 (%BM)] executed at three, four, five, and nine sprints (F3,56 = 0.174, p = 0.91, η2 = 0.01). The two-way ANOVA revealed that PPO were similar between tested models (P-%BM vs. P-v) independently from the numbers of sprints (F3,112 = 0.08, p = 0.99, η2 = 0.000). Moreover, the PPO measured in the WAnT (870.6 ± 179.1 W) was significantly lower compared with in P-v model (1,102.9 ± 242.5–1,134.2 ± 285.4 W) (F4,70 = 3.044, p = 0.02, η2 = 0.148). In addition, the PPO derived from P-%BM model (1,105.2 ± 245.5–1,138.7 ± 285.3 W) was significantly higher compared with the WAnT (F4,70 = 2.976, p = 0.02, η2 = 0.145). The findings suggest the potential utility of FVT for assessment of anaerobic capacity.

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“…The assessment of F-v and P-v relationships in lower limb extensions can be done using cyclic movements, such as sprint running (4) or cycling (5). Although this method is advantageous to test individuals using sport-specific movements and in a safe, accessible, and reliable way (6,7), it nonetheless includes movement-specific force orientation technique, which is less transferable from one physical activity to another. In contrast, during acyclic movements, such as vertical (2,8) and horizontal (9) jumping or lower limb extensions on an inclined or horizontal leg press devices (1,10) the quasi-total external force developed by lower limbs is considered and so refer more to a non-specific strength index.…”

Section: Introductionmentioning

confidence: 99%

Exploring the velocity end of the force-velocity relationship in acyclic lower limb extensions

Rivière¹,

Morin²,

Bowen³

et al. 2021

Preprint

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Purpose: compare linear and curvilinear models to describe the force-velocity relationship, especially on the velocity end, in lower limb acyclic extensions. Methods: nine athletes performed horizontal lower limb extensions on a leg press ergometer allowing to set from very high to very low (i.e. horizontal assisted extensions) resistive and inertial conditions. Lower limb force and velocity were continuously measured over the push-off in six resistive conditions to determine individual force-velocity relationships. Goodness of fit (GoF) of the linear model on force and velocity data (using the basic first order polynomial function), was compared to that of curvilinear models (using the basic second order polynomial function, Fenn and Marsh’s, and Hill’s equations) based on the Akaike Information Criterion (AIC). Results: When expressed relative to the theoretical maximal force (F0-L) and velocity (v0) obtained from the linear model, force and velocity data ranged from 26.6±6.6 to 96.0±3.6%F0-L and from 8.3±1.9 to 76.6±7.0%v0-L (range of individual values=5-86%v0-L and 16-99%F0-L). Curvilinear and linear models showed very high GoF (r2=0.951-0.999; SEE=17-159N). Despite higher GoF for curvilinear models, AIC showed that the linear model presented ~99-100 % chance to be the best one to describe the force-velocity relationship. Conclusion: the force-velocity relationship in acyclic lower limb extensions can be considered as linear from its force (from 5%v0-L) to its velocity end (up to 86%v0-L). Assisted horizontal acyclic lower limb extensions allowed achievement of movement velocities very close to v0, representing an interesting modality for training or testing force production capabilities at high velocities, notably for weaker populations.

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Optimal load for a torque-velocity relationship test during cycling

Cited by 8 publications

References 25 publications

Analysis of elite road‐cycling sprints in relation to maximal power‐velocity‐endurance profile: a longitudinal one‐case study

Analysis of elite road‐cycling sprints in relation to maximal power‐velocity‐endurance profile: a longitudinal one‐case study

Determination of optimal load in the Wingate Anaerobic Test is not depend on number of sprints included in mathematical models

Exploring the velocity end of the force-velocity relationship in acyclic lower limb extensions

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