Prediction of percentage of completed repetitions to failure using velocity loss: does the relationship remain stable throughout a training session?

Alejandro Pérez-Castilla, Deniz Şentürk, Zeki Akyildiz, Ivan Jukic, Amador García-Ramos*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    3 Citations (Scopus)
    42 Downloads (Pure)

    Abstract

    This study explored the goodness-of-fit and the effect of fatigue on the precision of both generalized and individualized relationships between the velocity loss (%VL) magnitude and the percentage of completed repetitions with respect to the maximal that can be performed to failure (%Rep) in the Smith machine parallel back-squat exercise. Twenty-nine resistance-trained males completed four sets to failure, with a rest period of 2 min, against 75% of the one-repetition maximum. Generalized and individualized %Rep-%VL equations determined in the first set were used to estimate %Rep when a 20%VL was achieved during the three successive sets. Individualized %Rep-%VL relationships (R2 = 0.84–0.99) showed a greater goodness-of-fit than the generalized %Rep-%VL relationship (R2 = 0.82). However, the accuracy in the %Rep estimation was always low (absolute errors > 10%) and comparable for both regression models (p = 0.795). %Rep was progressively overestimated when increasing the number of sets using the MVfastest of the first set (from 15% to 45%), but no meaningful overestimations were observed using the MVfastest of each set (~2%). In conclusion, neither the generalized nor the individual %Rep-%VL equations provide accurate estimations of %Rep during the parallel back-squat exercise executed under fatigue.
    Original languageEnglish
    Article number4531
    Number of pages10
    JournalApplied Sciences (Switzerland)
    Volume14
    Issue number11
    Early online date25 May 2024
    DOIs
    Publication statusPublished - 25 May 2024

    Keywords

    • Fatigue
    • Level of exertion
    • Neuromuscular fatigue
    • Resistance training
    • Velocity-based training

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