Analyzing one-repetition-max predictions: load-velocity relationship vs. repetition to failure equation in ten lower extremity exercises

Abstract

Abstract Purpose This study aims to evaluate the accuracy of predicting one-repetition-maximum (1RM) using the load-velocity relationship and different repetition-to-failure estimation equations for ten lower-extremity exercises. Methods A total of 22 healthy participants were recruited. The tested exercises included ankle, knee, and hip joint flexion and extension, as well as hip abduction, hip adduction, and leg press. Velocity during the concentric phase was measured using a linear transducer, and individual linear regression models were established using incremental submaximal loads (40–80% 1RM) and velocity to estimate the 1RM. Repetition-to-failure estimations of 1 RM were assessed with eleven different regression equations, among them the Lombardi equation. Intraclass correlation coefficient (ICC), Bland and Altman plots, and normalized mean absolute error (NMAE) were used to compare the estimations with a measured 1RM. Results Predictions based on the load-velocity relationship exhibited NMAE values ranging from 8.6% to 35.2%, ICC values from 0.35 to 0.87, and substantial limits of agreement across all exercises, in contrast to the measured 1RM values. Among the fatigue estimation equations, the Lombardi equation demonstrated the lowest NMAE across all exercises (5.8%), with an excellent ICC of 0.99 and narrow limits of agreement. Conclusion The load-velocity relationship proved inadequate for predicting 1RM in lower-extremity single-joint exercises. However, the Lombardi estimation equations showcased favorable predictive performance with a consistently low average NMAE across all exercises studied.