Validity of load-velocity relationship to predict 1 repetition maximum during deadlifts performed with and without lifting straps: the accuracy of six prediction models

Ivan Jukic*, Amador García-Ramos, Jan Malecek, Dan Omcirk, James J. Tufano

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

35 Citations (Scopus)

Abstract

This study aimed to compare the accuracy of six 1 repetition maximum (1RM) prediction models during deadlifts performed with (DLw) and without (DLn) lifting straps. In a counterbalanced order, 18 resistance-trained men performed 2 sessions that consisted of an incremental loading test (20-40-60-80-90% of 1RM) followed by 1RM attempts during the DLn (1RM = 162.0 ± 26.9 kg) and DLw (1RM = 179.0 ± 29.9 kg). Predicted 1RMs were calculated by entering both group and individualized mean concentric velocity of the 1RM (V1RM) into an individualized linear and polynomial regression equations, which were derived from the load-velocity relationship of 5 ([20-40-60-80-90% of 1RM], i.e., multiple-point method) or 2 ([40 and 90% of 1RM] i.e., 2-point method) incremental warm-up sets. The predicted 1RMs were deemed highly valid if the following criteria were met: trivial to small effect size, practically perfect r, and low absolute errors (<5 kg). The main findings revealed that although prediction models were more accurate during the DLn than DLw, none of the models provided an accurate estimation of the 1RM during both DLn (r = 0.92-0.98; absolute errors: 6.6-8.1 kg) and DLw (r = 0.80-0.93; absolute errors: 12.4-16.3 kg) according to our criteria. Therefore, these results suggest that the 1RM for both DLn and DLw should not be estimated through the recording of movement velocity if sport professionals are not willing to accept more than 5 kg of absolute errors.

Original languageEnglish
Pages (from-to)902-910
Number of pages9
JournalJournal of Strength and Conditioning Research
Volume36
Issue number4
Early online date13 Apr 2020
DOIs
Publication statusPublished - 30 Apr 2022
Externally publishedYes

Keywords

  • Strength testing
  • Monitoring
  • Resistance training

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