A non-exercise prediction model for estimation of cardiorespiratory fitness in adults

Abstract

The most accurate tool for assessment of cardiorespiratory fitness is cardiopulmonary exercise testing (CPET). However, CPET requires expensive equipment, trained technicians and time, which limits their use in population studies. In view of this issue, the present study aims to develop regression equations for predicting the cardiorespiratory fitness of adults using simple measurement variables. The study used data from 8,293 subjects, 5,291 male and 3,235 female (age range, 18 to 65 years). The sample was recruited in Florianopolis, Santa Catarina. To develop equations for prediction of peak oxygen uptake (VO2peak), the data associated were: fitness, age, body mass, height, resting heart rate, hypertension, diabetes, dyslipidemia and smoking. After statistical analyses, two equations for men and two for women were developed. The complete equations showed an adjusted R2 = 0.531 and a standard error of estimate (SEE) = 7.15 ml-1?kg-1?min for men and R2 = 0.436 and SEE = 5.68 ml-1?kg-1?min for women. We conclude that the model developed for prediction of cardiorespiratory fitness is feasible and practical for prediction of VO2peak in epidemiological studies or when CPET cannot be performed.