Prediction of Percentage of Completed Repetitions to Failure Using Velocity Loss: Does the Relationship Remain Stable throughout a Training Session?


Şentürk D.

Applied Sciences (Switzerland), vol.14, no.11, 2024 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 14 Issue: 11
  • Publication Date: 2024
  • Doi Number: 10.3390/app14114531
  • Journal Name: Applied Sciences (Switzerland)
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Agricultural & Environmental Science Database, Applied Science & Technology Source, Communication Abstracts, INSPEC, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
  • Keywords: fatigue, level of exertion, neuromuscular fatigue, resistance training, velocity-based training
  • Istanbul Gelisim University Affiliated: Yes

Abstract

Featured Application: The fastest velocity specific to each set should be recommended to obtain a more accurate estimation of the actual percentage of completed repetitions (%Rep) because the use of the fastest velocity of the first set could overestimate the %Rep when the successive sets are initiated in a fatigued condition. 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.