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Article

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

1
Academy of Sport and Physical Activity, Sheffield Hallam University, Sheffield S10 2BP, UK
2
School of Health Sciences, Robert Gordon University, Aberdeen AB10 7QE, UK
3
School of Sport and Exercise Sciences, University of Lincoln, Lincoln LN6 7TS, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Dale Wilson Chapman
Sports 2021, 9(7), 88; https://doi.org/10.3390/sports9070088
Received: 23 May 2021 / Revised: 16 June 2021 / Accepted: 17 June 2021 / Published: 22 June 2021
(This article belongs to the Special Issue New Perspectives in Resistance Training)
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 underwent initial 1RMs in the free-weight back squat, followed by two LVPs, over three sessions. Profiles 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 modeling 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. The 1RM prediction was based on LVP two data and analyzed via analysis of variance, effect size (g/ηp2), 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; ηp2 = 0.90) and back squat (p = 0.004, ηp2 = 0.35) methods. Significant differences were observed between exercises when applying linear modeling (p < 0.001, ηp2 = 0.67–0.80), but not quadratic (p = 0.632–0.929, ηp2 = 0.001–0.18). Quadratic modeling 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. View Full-Text
Keywords: load-velocity profiling; 1RM prediction; 1RM estimation; maximal strength; linear regression load-velocity profiling; 1RM prediction; 1RM estimation; maximal strength; linear regression
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MDPI and ACS Style

Thompson, S.W.; Rogerson, D.; Ruddock, A.; Greig, L.; Dorrell, H.F.; Barnes, A. A Novel Approach to 1RM Prediction Using the Load-Velocity Profile: A Comparison of Models. Sports 2021, 9, 88. https://doi.org/10.3390/sports9070088

AMA Style

Thompson SW, Rogerson D, Ruddock A, Greig L, Dorrell HF, Barnes A. A Novel Approach to 1RM Prediction Using the Load-Velocity Profile: A Comparison of Models. Sports. 2021; 9(7):88. https://doi.org/10.3390/sports9070088

Chicago/Turabian Style

Thompson, Steve W., David Rogerson, Alan Ruddock, Leon Greig, Harry F. Dorrell, and Andrew Barnes. 2021. "A Novel Approach to 1RM Prediction Using the Load-Velocity Profile: A Comparison of Models" Sports 9, no. 7: 88. https://doi.org/10.3390/sports9070088

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