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Classification of cardiorespiratory fitness without exercise testing
C E Matthews1, D P Heil, P S Freedson
1Division of Preventive and Behavioral Medicine University of Massachusetts, Medical School, Worcester 01655, USA. chuck.matthews@banyan.ummed.edu
Purpose:
We examined the ability of a nonexercise based VO2max, prediction model to classify cardiorespiratory fitness (CRF) in a population of men and women aged 19-79 yr of age (N = 799).
Methods:
A VO2max (mL.kg(-1).min(-1)) prediction model was developed in the study group using multiple linear regression from the independent variables age, age2, gender, physical activity status, height, and body mass. The classification accuracy of this model was examined by cross-tabulating age and gender specific quintiles of measured and predicted CRF.
Results:
Overall classification accuracy of the model was modest (36%); however, 83% of all subjects were either classified correctly or within one quintile of measured CRF. Extreme misclassification (e.g., misclassifying a low fit individual as high fit) was only rarely observed (0.13%).
Conclusions:
The present results support the concept that CRF prediction models can be used to reasonably characterize the fitness level of a cohort using data that can be obtained from a questionnaire. Accordingly, predicted CRF values may be useful as an exposure variable in large epidemiologic studies in which exercise testing is not feasible.