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

Area of Science:

• Sports Science
• Biomechanics
• Strength and Conditioning

Background:

• One-repetition maximum (1RM) is crucial for strength assessment.
• Load-velocity profiling (LVP) offers a non-fatiguing method for 1RM estimation.
• Optimizing predictive models for LVP is essential for practical application.

Purpose of the Study:

• To compare the predictive accuracy of different regression models (linear vs. quadratic) and profiling methods (combined vs. back squat only) for 1RM estimation using LVP data.
• To determine the most valid method for predicting daily 1RM in strength-trained individuals.

Main Methods:

• Fourteen strength-trained men completed initial 1RMs and two LVPs using free-weight back squats.
• LVPs were constructed using either a combined method (jump squat + back squat) or back squat only.
• Quadratic and linear regression models were applied to LVP data to predict 1RM.

Main Results:

• All models demonstrated low systematic bias (<10 kg) and high correlation (r > 0.97).
• Quadratic models showed significantly higher predictive validity than linear models for both profiling methods.
• The combined method with quadratic modeling yielded the most accurate 1RM predictions, with no significant differences from measured 1RM.

Conclusions:

• Quadratic regression modeling, particularly with the combined jump squat and back squat method, is superior for 1RM estimation from LVP data.
• This approach provides the greatest predictive validity and is recommended for practitioners aiming to autoregulate training loads based on daily 1RM predictions.