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Predicting Total Back Squat Repetitions from Repetition Velocity and Velocity Loss

The purpose of this investigation was to determine if average concentric velocity (ACV) of a single repetition at 70% of one-repetition maximum (1RM), ACV of the first repetition of a set to failure at 70% of 1RM, or the velocity loss during the set could predict the number of repetitions performed...

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Autores principales: Haischer, Michael H., Carzoli, Joseph P., Cooke, Daniel M., Pelland, Joshua C., Remmert, Jacob F., Zourdos, Michael. C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Termedia Publishing House 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10203840/
https://www.ncbi.nlm.nih.gov/pubmed/37229411
http://dx.doi.org/10.5114/jhk/162021
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author Haischer, Michael H.
Carzoli, Joseph P.
Cooke, Daniel M.
Pelland, Joshua C.
Remmert, Jacob F.
Zourdos, Michael. C.
author_facet Haischer, Michael H.
Carzoli, Joseph P.
Cooke, Daniel M.
Pelland, Joshua C.
Remmert, Jacob F.
Zourdos, Michael. C.
author_sort Haischer, Michael H.
collection PubMed
description The purpose of this investigation was to determine if average concentric velocity (ACV) of a single repetition at 70% of one-repetition maximum (1RM), ACV of the first repetition of a set to failure at 70% of 1RM, or the velocity loss during the set could predict the number of repetitions performed in the back squat. Fifty-six resistance-trained individuals participated in the study (male = 41, age = 23 ± 3 yrs, 1RM = 162.0 ± 40.0 kg; female = 15, age = 21 ± 2 yrs, 1RM = 81.5 ± 12.5 kg). After 1RM testing, participants performed single repetition sets with 70% of 1RM and a set to failure with 70% of 1RM. ACV was recorded on all repetitions. Regression model comparisons were performed, and Akaike Information Criteria (AIC) and Standard Error of the Estimate (SEE) were calculated to determine the best model. Neither single repetition ACV at 70% of 1RM (R(2) = 0.004, p = 0.637) nor velocity loss (R(2) = 0.011, p = 0.445) were predictive of total repetitions performed in the set to failure. The simple quadratic model using the first repetition of the set to failure ([Formula: see text]) was identified as the best and most parsimonious model (R(2) = 0.259, F = 9.247, p < 0.001) due to the lowest AIC value (311.086). A SEE of 2.21 repetitions was identified with this model. This average error of ~2 repetitions warrants only cautious utilization of this method to predict total repetitions an individual can perform in a set, with additional autoregulatory or individualization strategies being necessary to finalize the training prescription.
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spelling pubmed-102038402023-05-24 Predicting Total Back Squat Repetitions from Repetition Velocity and Velocity Loss Haischer, Michael H. Carzoli, Joseph P. Cooke, Daniel M. Pelland, Joshua C. Remmert, Jacob F. Zourdos, Michael. C. J Hum Kinet Research Paper The purpose of this investigation was to determine if average concentric velocity (ACV) of a single repetition at 70% of one-repetition maximum (1RM), ACV of the first repetition of a set to failure at 70% of 1RM, or the velocity loss during the set could predict the number of repetitions performed in the back squat. Fifty-six resistance-trained individuals participated in the study (male = 41, age = 23 ± 3 yrs, 1RM = 162.0 ± 40.0 kg; female = 15, age = 21 ± 2 yrs, 1RM = 81.5 ± 12.5 kg). After 1RM testing, participants performed single repetition sets with 70% of 1RM and a set to failure with 70% of 1RM. ACV was recorded on all repetitions. Regression model comparisons were performed, and Akaike Information Criteria (AIC) and Standard Error of the Estimate (SEE) were calculated to determine the best model. Neither single repetition ACV at 70% of 1RM (R(2) = 0.004, p = 0.637) nor velocity loss (R(2) = 0.011, p = 0.445) were predictive of total repetitions performed in the set to failure. The simple quadratic model using the first repetition of the set to failure ([Formula: see text]) was identified as the best and most parsimonious model (R(2) = 0.259, F = 9.247, p < 0.001) due to the lowest AIC value (311.086). A SEE of 2.21 repetitions was identified with this model. This average error of ~2 repetitions warrants only cautious utilization of this method to predict total repetitions an individual can perform in a set, with additional autoregulatory or individualization strategies being necessary to finalize the training prescription. Termedia Publishing House 2023-04-20 /pmc/articles/PMC10203840/ /pubmed/37229411 http://dx.doi.org/10.5114/jhk/162021 Text en Copyright: © Academy of Physical Education in Katowice https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/). This license lets others distribute, remix, adapt, and build upon your work, even commercially, as long as they credit you for the original creation.
spellingShingle Research Paper
Haischer, Michael H.
Carzoli, Joseph P.
Cooke, Daniel M.
Pelland, Joshua C.
Remmert, Jacob F.
Zourdos, Michael. C.
Predicting Total Back Squat Repetitions from Repetition Velocity and Velocity Loss
title Predicting Total Back Squat Repetitions from Repetition Velocity and Velocity Loss
title_full Predicting Total Back Squat Repetitions from Repetition Velocity and Velocity Loss
title_fullStr Predicting Total Back Squat Repetitions from Repetition Velocity and Velocity Loss
title_full_unstemmed Predicting Total Back Squat Repetitions from Repetition Velocity and Velocity Loss
title_short Predicting Total Back Squat Repetitions from Repetition Velocity and Velocity Loss
title_sort predicting total back squat repetitions from repetition velocity and velocity loss
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10203840/
https://www.ncbi.nlm.nih.gov/pubmed/37229411
http://dx.doi.org/10.5114/jhk/162021
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