New Generalized Equation for Predicting Maximal Oxygen Uptake (From the Fitness Registry and the Importance of Exercise National Database)
journal contributionposted on 2018-10-18, 00:00 authored by Peter Kokkinos
Impaired cardiorespiratory fitness (CRF) is closely linked to chronic illness and associated with adverse events. The American College of Sports Medicine (ACSM) regression equations (ACSM equations) developed to estimate oxygen uptake have known limitations leading to well-documented overestimation of CRF, especially at higher work rates. Thus, there is a need to explore alternative equations to more accurately predict CRF. We assessed maximal oxygen uptake (VO2 max) obtained directly by open-circuit spirometry in 7,983 apparently healthy individuals who participated in the Fitness Registry and the Importance of Exercise National Database (FRIEND). We randomly sampled 70% of the participants from each of the following age categories: <40, 40- 50, 50-70 and >=70) and used the remaining 30% for validation. Multivariable linear regression analysis was applied to identify the most relevant variables and construct the best prediction model for VO2 max. Treadmill speed and treadmill speed * grade were considered in the final model as predictors of measured VO2 max and the following equation was generated: VO2 max in ml O2●kg-1●min-1= Speed (m/min) * (0.17 + Fractional Grade * 0.79) +3.5. The FRIEND equation predicted VO2 max with an overall error >4 times lower than the error associated with the traditional ACSM equations (5.1±18.3% vs 21.4±24.9%, respectively). Overestimation associated with the ACSM equation was accentuated when different protocols were considered separately. In conclusion, The FRIEND equation predicts VO2 max more precisely than the traditional ACSM equations with an overall error >4 times lower than that associated with the ACSM equations.
CitationKokkinos, P., Kaminsky, L. A., Arena, R., Zhang, J. J. and Myers, J. New Generalized Equation for Predicting Maximal Oxygen Uptake (from the Fitness Registry and the Importance of Exercise National Database). American Journal of Cardiology. 2017. 120(4): 688-692. 10.1016/j.amjcard.2017.05.037.