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Loading intensity prediction by velocity and the OMNI-RES 0–10 scale in bench press

Loading intensity prediction by velocity and the OMNI-RES 0–10 scale in bench press

Naclerio, Fernando ORCID: 0000-0001-7405-4894 and Larumbe-Zabala, Eneko (2016) Loading intensity prediction by velocity and the OMNI-RES 0–10 scale in bench press. Journal of Strength and Conditioning Research, 31 (2). pp. 323-329. ISSN 1064-8011 (Print), 1533-4287 (Online) (doi:

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This study examined the possibility of using movement velocity and the perceived exertion as indicators of relative load in the bench press exercise. Three hundred eight young, healthy, resistance trained athletes (242 male and 66 female) performed a progressive strength test up to the one-repetition maximum for the individual determination of the full load-velocity and load-exertion relationships. 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. Load associated with the average velocity and the OMNI-RES 0-10 scale value expressed after performing a set of 1-3 repetitions were used to construct two adjusted predictive equations: Relative load = 107.75 – 62.97 × average velocity; and Relative load = 29.03 + 7.26 × OMNI-RES 0-10 scale value. The two models were capable of estimating the relative load with an accuracy of 84% and 93% respectively. These findings confirm the ability of the two calculated regression models, using load-velocity and load-exertion from the OMNI-RES 0-10 scale, to accurately predict strength performance in bench press.

Item Type: Article
Additional Information: Copyright (C) 2016 by the National Strength & Conditioning Association.
Uncontrolled Keywords: Strength assessment, Resistance training, Isoinertial estimation, Perceived exertion.
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 21:06
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None

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