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The impact of test loads on the accuracy of 1RM prediction using the load-velocity relationship
BACKGROUND: Numerous methods have been proposed that use submaximal loads to predict one repetition maximum (1RM). One common method applies standard linear regression equations to load and average vertical lifting velocity (V(mean)) data developed during squat jumps or three bench press throw (BP-T...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5975420/ https://www.ncbi.nlm.nih.gov/pubmed/29854409 http://dx.doi.org/10.1186/s13102-018-0099-z |
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author | Sayers, Mark G. L. Schlaeppi, Michel Hitz, Marina Lorenzetti, Silvio |
author_facet | Sayers, Mark G. L. Schlaeppi, Michel Hitz, Marina Lorenzetti, Silvio |
author_sort | Sayers, Mark G. L. |
collection | PubMed |
description | BACKGROUND: Numerous methods have been proposed that use submaximal loads to predict one repetition maximum (1RM). One common method applies standard linear regression equations to load and average vertical lifting velocity (V(mean)) data developed during squat jumps or three bench press throw (BP-T). The main aim of this project was to determine which combination of three submaximal loads during BP-T result in the most accurate prediction of 1RM Smith Machine bench press strength in healthy individuals. METHODS: In this study combinations of three BP-T loads were used to predict 1RM Smith Machine bench press strength. Additionally, we examined whether regression models developed using peak vertical bar velocity (V(peak)), rather than V(mean), provide the most accurate prediction of Smith Machine bench press 1RM. 1RM Smith Machine bench press strength was measured directly in 12 healthy regular weight trainers (body mass = 80.8 ± 5.7 kg). Two to three days later a linear position transducer attached to the collars on a Smith Machine was used to record V(mean) and V(peak) during BP-T between 30 and 70% of 1RM (10% increments). RESULTS: Repeated measures analysis of variance testing showed that the mean values for slope and ordinate intercept for the regression models at each of the load ranges differed significantly depending on whether V(mean) or V(peak) were used in the prediction models (P < 0.001). Conversely, the abscissa intercept did not differ significantly between either measure of vertical bar velocity at each load range. The key finding in this study was that 1RM Smith Machine bench press strength can be determined with high relative accuracy by examining V(mean) and V(peak) during BP-T over three loads, with the most precise models using V(peak) during loads representing 30, 40 and 50% of 1RM (R(2) = 0.96, SSE = 4.2 kg). CONCLUSIONS: These preliminary findings indicate that exercise programmers working with normal healthy populations can accurately predict Smith Machine 1RM bench press strength using relatively light load Smith Machine BP-T testing, avoiding the need to expose their clients to potentially injurious loads. |
format | Online Article Text |
id | pubmed-5975420 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-59754202018-05-31 The impact of test loads on the accuracy of 1RM prediction using the load-velocity relationship Sayers, Mark G. L. Schlaeppi, Michel Hitz, Marina Lorenzetti, Silvio BMC Sports Sci Med Rehabil Technical Advance BACKGROUND: Numerous methods have been proposed that use submaximal loads to predict one repetition maximum (1RM). One common method applies standard linear regression equations to load and average vertical lifting velocity (V(mean)) data developed during squat jumps or three bench press throw (BP-T). The main aim of this project was to determine which combination of three submaximal loads during BP-T result in the most accurate prediction of 1RM Smith Machine bench press strength in healthy individuals. METHODS: In this study combinations of three BP-T loads were used to predict 1RM Smith Machine bench press strength. Additionally, we examined whether regression models developed using peak vertical bar velocity (V(peak)), rather than V(mean), provide the most accurate prediction of Smith Machine bench press 1RM. 1RM Smith Machine bench press strength was measured directly in 12 healthy regular weight trainers (body mass = 80.8 ± 5.7 kg). Two to three days later a linear position transducer attached to the collars on a Smith Machine was used to record V(mean) and V(peak) during BP-T between 30 and 70% of 1RM (10% increments). RESULTS: Repeated measures analysis of variance testing showed that the mean values for slope and ordinate intercept for the regression models at each of the load ranges differed significantly depending on whether V(mean) or V(peak) were used in the prediction models (P < 0.001). Conversely, the abscissa intercept did not differ significantly between either measure of vertical bar velocity at each load range. The key finding in this study was that 1RM Smith Machine bench press strength can be determined with high relative accuracy by examining V(mean) and V(peak) during BP-T over three loads, with the most precise models using V(peak) during loads representing 30, 40 and 50% of 1RM (R(2) = 0.96, SSE = 4.2 kg). CONCLUSIONS: These preliminary findings indicate that exercise programmers working with normal healthy populations can accurately predict Smith Machine 1RM bench press strength using relatively light load Smith Machine BP-T testing, avoiding the need to expose their clients to potentially injurious loads. BioMed Central 2018-05-29 /pmc/articles/PMC5975420/ /pubmed/29854409 http://dx.doi.org/10.1186/s13102-018-0099-z Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Technical Advance Sayers, Mark G. L. Schlaeppi, Michel Hitz, Marina Lorenzetti, Silvio The impact of test loads on the accuracy of 1RM prediction using the load-velocity relationship |
title | The impact of test loads on the accuracy of 1RM prediction using the load-velocity relationship |
title_full | The impact of test loads on the accuracy of 1RM prediction using the load-velocity relationship |
title_fullStr | The impact of test loads on the accuracy of 1RM prediction using the load-velocity relationship |
title_full_unstemmed | The impact of test loads on the accuracy of 1RM prediction using the load-velocity relationship |
title_short | The impact of test loads on the accuracy of 1RM prediction using the load-velocity relationship |
title_sort | impact of test loads on the accuracy of 1rm prediction using the load-velocity relationship |
topic | Technical Advance |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5975420/ https://www.ncbi.nlm.nih.gov/pubmed/29854409 http://dx.doi.org/10.1186/s13102-018-0099-z |
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