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. 2016 Oct;116(10):2035-43.
doi: 10.1007/s00421-016-3457-0. Epub 2016 Aug 30.

1RM Prediction: A Novel Methodology Based on the Force-Velocity and Load-Velocity Relationships

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1RM Prediction: A Novel Methodology Based on the Force-Velocity and Load-Velocity Relationships

Pietro Picerno et al. Eur J Appl Physiol. .

Abstract

Purpose: This study aimed to evaluate the accuracy of a novel approach for predicting the one-repetition maximum (1RM). The prediction is based on the force-velocity and load-velocity relationships determined from measured force and velocity data collected during resistance-training exercises with incremental submaximal loads. 1RM was determined as the load corresponding to the intersection of these two curves, where the gravitational force exceeds the force that the subject can exert.

Methods: The proposed force-velocity-based method (FVM) was tested on 37 participants (23.9 ± 3.1 year; BMI 23.44 ± 2.45) with no specific resistance-training experience, and the predicted 1RM was compared to that achieved using a direct method (DM) in chest-press (CP) and leg-press (LP) exercises.

Results: The mean 1RM in CP was 99.5 kg (±27.0) for DM and 100.8 kg (±27.2) for FVM (SEE = 1.2 kg), whereas the mean 1RM in LP was 249.3 kg (±60.2) for DM and 251.1 kg (±60.3) for FVM (SEE = 2.1 kg). A high correlation was found between the two methods for both CP and LP exercises (0.999, p < 0.001). Good agreement between the two methods emerged from the Bland and Altman plot analysis.

Conclusion: These findings suggest the use of the proposed methodology as a valid alternative to other indirect approaches for 1RM prediction. The mathematical construct is simply based on the definition of the 1RM, and it is fed with subject's muscle strength capacities measured during a specific exercise. Its reliability is, thus, expected to be not affected by those factors that typically jeopardize regression-based approaches.

Keywords: Force–velocity relationship; Muscle strength assessment; One-repetition maximum; Resistance training.

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