McBurnie Alistair J.
The Benefits and Limitations of Predicting One Repetition Maximum Using the Load-Velocity Relationship
Alistair J., McBurnie; Kieran P., Allen; Maybanks, Garry; McDwyer, Martin; Dos'Santos, Thomas; Paul A., Jones; Paul, Comfort; John J., McMahon
Authors
Allen Kieran P.
Garry Maybanks
Martin McDwyer
Thomas Dos'Santos
Jones Paul A.
Prof Paul Comfort P.Comfort@salford.ac.uk
Professor of Strength & Conditioning
McMahon John J.
Abstract
This review aims to provide an overview of the current load-velocity (L-V) approaches and their ability to estimate one-repetition maximum (1RM). The bench press exercise appears to be the most valid and reliable when applying this approach. The ability for L-V relationship to predict 1RM for lower-body lifts remains questionable. Individualized regression equations should be used alongside mean velocity when utilizing this method during the bench press. The 2-point method (2 distinguishable loads, as opposed to multiple loads) and normative velocity data (minimal velocity thresholds at 1RM) may provide a novel and practical way to assess athletes' 1RM.
Citation
Alistair J., M., Kieran P., A., Maybanks, G., McDwyer, M., Dos'Santos, T., Paul A., J., …John J., M. (2019). The Benefits and Limitations of Predicting One Repetition Maximum Using the Load-Velocity Relationship. Strength and Conditioning Journal, 41(6), 28-40. https://doi.org/10.1519/SSC.0000000000000496
Journal Article Type | Article |
---|---|
Publication Date | 2019-12 |
Deposit Date | Aug 1, 2023 |
Journal | Strength and Conditioning Journal |
Print ISSN | 1524-1602 |
Publisher | Lippincott, Williams & Wilkins |
Peer Reviewed | Peer Reviewed |
Volume | 41 |
Issue | 6 |
Pages | 28-40 |
DOI | https://doi.org/10.1519/SSC.0000000000000496 |
You might also like
Downloadable Citations
About USIR
Administrator e-mail: library-research@salford.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search