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

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 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) + (70–100% 1RM)) or only 30–100% 10% increments. Quadratic and linear regression modeling applied data estimate 80% 1RM (kg) using mean velocity identified LVP as reference point, with load (kg), then extrapolated predict 1RM. prediction based on analyzed analysis of variance, effect size (g/ηp2), Pearson correlation coefficients (r), paired t-tests, standard error (SEE), limits agreement (LOA). p < 0.05. All reported systematic bias 10 kg, r > 0.97, SEE 5 however, all significantly measured (p = 0.015 <0.001). Significant differences observed between quadratic for 0.001; ηp2 0.90) 0.004, 0.35) methods. exercises when applying 0.001, 0.67–0.80), but not 0.632–0.929, 0.001–0.18). employing rendered greatest validity. Practitioners should therefore utilize this looking daily means autoregulation.