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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Veröffentlicht in:Journal of human kinetics 2023-01, Vol.87, p.167-178
Hauptverfasser: Haischer, Michael H, Carzoli, Joseph P, Cooke, Daniel M, Pelland, Joshua C, Remmert, Jacob F, Zourdos, Michael C
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container_start_page 167
container_title Journal of human kinetics
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creator Haischer, Michael H
Carzoli, Joseph P
Cooke, Daniel M
Pelland, Joshua C
Remmert, Jacob F
Zourdos, Michael C
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 = 0.004, p = 0.637) nor velocity loss (R = 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 ( ) was identified as the best and most parsimonious model (R = 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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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 = 0.004, p = 0.637) nor velocity loss (R = 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 ( ) was identified as the best and most parsimonious model (R = 0.259, F = 9.247, p &lt; 0.001) due to the lowest AIC value (311.086). A SEE of 2.21 repetitions was identified with this model. 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title Predicting Total Back Squat Repetitions from Repetition Velocity and Velocity Loss
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