Prediction of VO2 peak using omni ratings of perceived exertion from a submaximal cycle exercise test

Ryan J. Mays, Fredric L. Goss, Elizabeth F. Nagle, Michael Gallagher, Mark A. Schafer, Kevin H. Kim, Robert J. Robertson

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The primary aim of this study was to develop statistical models to predict peak oxygen consumption (VO2 peak) using OMNI Ratings of Perceived Exertion measured during submaximal cycle ergometry. Male (M = 20.9 yr., SE = 0.4) and female (M = 21.6 yr., SE = 0.5) participants (N = 81) completed a load-incremented maximal cycle ergometer exercise test. Simultaneous multiple linear regression was used to develop separate VO2 peak statistical models using submaximal ratings of perceived exertion for the overall body, legs, and chest/breathing as predictor variables. VO2 peak (L·min-1) predicted for men and women from ratings of perceived exertion for the overall body (3.02 ± 0.06; 2.03 ± 0.04), legs (3.02 ± 0.06; 2.04 ± 0.04), and chest/breathing (3.02 ± 0.05; 2.03 ± 0.03) were similar to measured VO2 peak (3.02 ± 0.10; 2.03 ± 0.06, ps >.05). Statistical models based on submaximal OMNI Ratings of Perceived Exertion provide an easily administered and accurate method to predict VO2 peak.

Original languageEnglish (US)
Pages (from-to)863-881
Number of pages19
JournalPerceptual and motor skills
Volume118
Issue number3
DOIs
StatePublished - Jun 2014

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