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Relative load prediction by velocity and the omni-res 0-10 scale in parallel squat

Relative load prediction by velocity and the omni-res 0-10 scale in parallel squat

Naclerio, Fernando ORCID: 0000-0001-7405-4894 and Larumbe-Zabala, Eneko (2016) Relative load prediction by velocity and the omni-res 0-10 scale in parallel squat. Journal of Strength and Conditioning Research, 31 (6). pp. 1585-1591. ISSN 1064-8011 (Print), 1533-4287 (Online) (doi:

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This study analyzed the possibility of using movement velocity and the perceived exertion as predictors of relative load in the parallel squat exercise. In order to determine the full load-velocity and load-perceived exertion relationships, 290 young, resistance-trained athletes (209 male and 81 female) performed a progressive strength test up to the one-repetition maximum. Longitudinal regression models were used to predict the relative load from the average velocity and the OMNI-RES 0-10 scale, considering sets as the time-related variable. Two adjusted predictive equations were developed from the association between the relative load and the average velocity or the perceived exertion expressed after performing several sets of 1 to 3 repetitions during the progressive test. The resulting two models were capable of estimating the relative load with an accuracy of 79% and 86% for the average velocity [Relative load (% 1RM) = 120.15 – 83.54 (AV)] and the exertion [Relative load (% 1RM) = 5.07 + 9.63 (RPE)] respectively. The strong association between relative load with average velocity and the perceived exertion support the use of both predictive variables to estimate strength performance in parallel squat.

Item Type: Article
Additional Information: Copyright (C) 2016 by the National Strength & Conditioning Association.
Uncontrolled Keywords: Strength test; Perceived exertion; Resistance training; One repetition maximum (1RM).
Subjects: Q Science > QP Physiology
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Centre for Science and Medicine in Sport and Exercise
Faculty of Engineering & Science > Department of Life & Sports Sciences
Last Modified: 17 Sep 2017 21:04
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None

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