A systematic review of methods to predict maximal oxygen uptake from submaximal, open circuit spirometry in healthy adults

Objectives: This systematic review aimed to (i) report the accuracy of submaximal exercise-based predictive equations that incorporate oxygen uptake (measured via open circuit spirometry) to predict maximal oxygen uptake (. V˙O_{2 max}) and (ii) provide a critical reflection of the data to inform health professionals and researchers when selecting a prediction equation. Design: Systematic review. Methods: A systematic search of MEDLINE, EMBASE (via OvidSP), CINAHL, SPORTDiscus™ (via EBSCO Host) and Scopus databases was undertaken in February 2013. Studies were required to report data on healthy participants aged 18-65. y. Following tabulation of extracted data, a narrative synthesis was conducted. Results: From a total of 7597 articles screened, 19 studies were included, from which a total of 43 prediction equations were extracted. No significant difference was reported between the measured and predicted V˙O_{2 max} in 28 equations. Pearson's correlation coefficient between the predicted and measured V˙O_{2 max} ranged from r=. 0.92 to r=. 0.57. The variables most commonly used in predictive equations were heart rate (. n=. 19) and rating of perceived exertion (. n=. 24). Conclusions: Overall, submaximal exercise-based equations using open circuit spirometry to predict V˙O_{2 max} are moderately to highly accurate. The heart rate and rating of perceived exertion methods of predicting V˙O_{2 max} were of similar accuracy. Important factors to consider when selecting a predictive equation include: the level of exertion required; participant medical conditions or medications; the validation population; mode of ergometry; time and resources available for familiarisation trials; and the level of bias of the study from which equations are derived.