Non-exercise prediction model to estimate cardiorespiratory fitness in brazilian adults
DOI:
https://doi.org/10.21527/2176-7114.2026.51.15412Keywords:
Regression analysis, cardiorespiratory fitness, prediction algorithmsAbstract
The most accurate tool to assess cardiorespiratory fitness (CRF) is cardiopulmonary exercise testing (CPET). However, for its use it is required expensive equipment, trained technicians and considerable time, restricting its use in large population studies. Given these limitations, this study aims to develop and cross-validate a prediction model for estimating CRF in adults using simple resting outcomes. The study used data from 8,526 subjects, aged between 18 and 65 years, consisting of 5,291 men and 3,235 women divided into development group (G1) and cross validation group (G2). To develop models to estimate the CRF, the variables analysed were gender, conditioning, age, pre-exercise heart rate, body mass index, hypertension, diabetes, dyslipidaemia and smoking. Using multiple linear regression, a model was developed to estimate oxygen uptake peak (VO2peak) based on age, gender, body mass index (BMI), Heart rate Pre-effort (HRpre-esf) conditioning, smoking, dyslipidaemia, hypertension, and diabetes. This predictive model showed a coefficient correlation (r) = 0.74, and explained 55.5% of the variation of VO2peak, with standard error of estimate (SEE) indicating 6.93 ml.kg-1.min-1 variation. Cross-validation using other subjects confirmed these statistics with correlation of (r) = 0.75, adjusted R2 of 0.56, and SEE of 6.11 ml.kg. -1min-1. The model developed to predict CRF is feasible and practical for prediction of VO2peak in epidemiological studies or when a CPET cannot be performed. These models, which do not involve physical exercise, can serve as a tool to guide exercise prescription strategies.
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