Literature DB >> 30312197

Repetitions in Reserve and Rate of Perceived Exertion Increase the Prediction Capabilities of the Load-Velocity Relationship.

Carlos Balsalobre-Fernández1,2,3, Mario Muñoz-López3, David Marchante3, Amador García-Ramos4,5.   

Abstract

ABSTRACT: Balsalobre-Fernández, C, Muñoz-López, M, Marchante, D, and García-Ramos, A. Repetitions in reserve and rate of perceived exertion increase the prediction capabilities of the load-velocity relationship. J Strength Cond Res 35(3): 724-730, 2021-This study aimed to (a) analyze the relationships between relative load (i.e., %1 repetition maximum; 1RM) and movement velocity, repetitions in reserve (RIR) and rate of perceived exertion (RPE) in competitive powerlifters and (b) examine whether a multiple linear regression model with the movement velocity, RIR, and RPE as predictor variables could improve the goodness of fit of the load-velocity relationship. Ten competitive powerlifters performed an incremental loading test (from 50 to 100% 1RM) on the full-squat, hip-thrust, and bench press exercises. Barbell velocity was measured using a linear position transducer, while RIR and RPE were registered immediately after each set. Velocity (r2: 0.747-0.887), RIR (r2: 0.857-0.928), and RPE (r2: 0.908-0.933) were moderately to highly related to relative load. A higher amount of variance of the relative load was explained when the RIR and RPE were added to velocity in a multiple regression model in comparison with the load-velocity relationship (r2: 0.924-0.947). Moreover, it was observed that, in all cases, individual load-velocity, load-RIR, and load-RPE relationships had higher r2 scores than the generalized load-velocity relationship. Incorporating the RIR and RPE as predictors of the relative load along with movement velocity into a multiple linear regression was shown to provide better estimations of the %1RM than using a linear load-velocity relationship.
Copyright © 2018 National Strength and Conditioning Association.

Entities:  

Mesh:

Year:  2021        PMID: 30312197     DOI: 10.1519/JSC.0000000000002818

Source DB:  PubMed          Journal:  J Strength Cond Res        ISSN: 1064-8011            Impact factor:   4.415


  3 in total

1.  Bilateral and unilateral load-velocity profiling in a machine-based, single-joint, lower body exercise.

Authors:  Carlos Balsalobre-Fernández; Mario Cardiel-García; Sergio L Jiménez
Journal:  PLoS One       Date:  2019-09-16       Impact factor: 3.240

2.  Use of Machine-Learning and Load-Velocity Profiling to Estimate 1-Repetition Maximums for Two Variations of the Bench-Press Exercise.

Authors:  Carlos Balsalobre-Fernández; Kristof Kipp
Journal:  Sports (Basel)       Date:  2021-03-16

Review 3.  The Implementation of Velocity-Based Training Paradigm for Team Sports: Framework, Technologies, Practical Recommendations and Challenges.

Authors:  Carlos Balsalobre-Fernández; Lorena Torres-Ronda
Journal:  Sports (Basel)       Date:  2021-03-30
  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.