A Novel Approach to 1RM Prediction Using the Load-Velocity Profile: A Comparison of Models

Thompson, Steve, Rogerson, David, Ruddock, Alan , Greig, Leon, Dorrell, Harry and Barnes, Andrew (2021) A Novel Approach to 1RM Prediction Using the Load-Velocity Profile: A Comparison of Models. Sports, 9 (7). p. 88. ISSN 2075-4663

Full content URL: https://doi.org/10.3390/sports9070088

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A Novel Approach to 1RM Prediction Using the Load-Velocity Profile: A Comparison of Models
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Abstract

The study aim was to compare different predictive models in one repetition maximum(1RM) estimation from load-velocity profile (LVP) data. Fourteen strength-trained men under-went initial 1RMs in the free-weight back squat, followed by two LVPs, over three sessions. Pro-files were constructed via a combined method (jump squat (0 load, 30–60% 1RM) + back squat(70–100% 1RM)) or back squat only (0 load, 30–100% 1RM) in 10% increments. Quadratic and linear regression modelling was applied to the data to estimate 80% 1RM (kg) using 80% 1RM mean velocity identified in LVP one as the reference point, with load (kg), then extrapolated to predict 1RM. The1RM prediction was based on LVP two data and analyzed via analysis of variance, effect size (g/η2p),Pearson correlation coefficients (r), paired t-tests, standard error of the estimate (SEE), and limits of agreement (LOA).p< 0.05. All models reported systematic bias < 10 kg, r> 0.97, and SEE < 5 kg, however, all linear models were significantly different from measured 1RM (p= 0.015 <0.001). Significant differences were observed between quadratic and linear models for combined (p< 0.001;η2p= 0.90) and back squat (p= 0.004,η2p= 0.35) methods. Significant differences were observed between exercises when applying linear modelling (p< 0.001,η2p= 0.67–0.80), but not quadratic(p= 0.632–0.929,η2p= 0.001–0.18). Quadratic modelling employing the combined method rendered the greatest predictive validity. Practitioners should therefore utilize this method when looking to predict daily 1RMs as a means of load autoregulation.

Keywords:load-velocity profiling, 1RM prediction, 1RM estimation, maximal strength, linear regression
Subjects:C Biological Sciences > C600 Sports Science
Divisions:College of Social Science > School of Sport and Exercise Science
ID Code:45389
Deposited On:03 Aug 2021 15:45

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