Nonexercise Equations to Estimate Fitness in White European and South Asian Men

Purpose: Cardiorespiratory fitness is a strong, independent predictor of health, whether it is measured in an exercise test or estimated in an equation. The purpose of this study was to develop and validate equations to estimate fitness in middle-age white European and South Asian men. Methods: Multiple linear regression models (n = 168, including 83 white European and 85 South Asian men) were created using variables that are thought to be important in predicting fitness ((V) over dotO(2max), mL.kg(-1).min(-1)): age (yr), body mass index (kg.m(-2)), resting HR (bpm); smoking status (0, never smoked; 1, ex or current smoker), physical activity expressed as quintiles (0, quintile 1; 1, quintile 2; 2, quintile 3; 3, quintile 4; 4, quintile 5), categories of moderate-to-vigorous intensity physical activity (MVPA) (0, <75 min.wk(-1); 1, 75-150 min.wk(-1); 2, >150-225 min.wk(-1); 3, >225-300 min.wk(-1); 4, >300 min.wk(-1)), or minutes of MVPA (min.wk(-1)); and, ethnicity (0, South Asian; 1, white). The leave-one-out cross-validation procedure was used to assess the generalizability, and the bootstrap and jackknife resampling techniques were used to estimate the variance and bias of the models. Results: Around 70% of the variance in fitness was explained in models with an ethnicity variable, such as: (V) over dotO(2max) = 77.409 - (age x 0.374) - (body mass index x 0.906) - (ex or current smoker x 1.976) + (physical activity quintile coefficient) - (resting HR x 0.066) + (white ethnicity x 8.032), where physical activity quintile 1 is 0, 2 is 1.127, 3 is 1.869, 4 is 3.793, and 5 is 3.029. Only around 50% of the variance was explained in models without an ethnicity variable. All models with an ethnicity variable were generalizable and had low variance and bias. Conclusion: These data demonstrate the importance of incorporating ethnicity in nonexercise equations to estimate cardiorespiratory fitness in multiethnic populations.