Prediction of Estimated VO2max in Active University Students Using Field Tests: Rockport Walk Test Versus 20-m Shuttle Run

Background/Objectives: To develop and internally validate multiple linear regression models to predict estimated VO2max from anthropometric variables and easily obtainable physical fitness tests in active university students and to compare model performance when estimated VO2max was derived from the Rockport Walk Test versus the 20-m Shuttle Run (Course Navette). Methods: Anthropometric variables and physical fitness indicators, including body mass index (BMI), Ruffier index, and burpee repetitions, as well as sex and age, were evaluated. Estimated VO2max was obtained separately from the Rockport Walk Test and the 20-m Shuttle Run using their respective field test equations. For each test, a multiple linear regression model was fitted using the same set of predictors. Model performance was assessed using apparent metrics and internal validation with optimism correction based on repeated cross-validation. Results: The Rockport walk test model showed better predictive performance, explaining 55.2% of the variability in estimated VO2max (R2 = 0.552; adjusted R2 = 0.498) with a lower prediction error (RMSE = 3.54 mL·kg−1·min−1). In contrast, the 20-m shuttle run model showed lower explanatory capacity (R2 = 0.319; adjusted R2 = 0.256) and a substantially higher prediction error (RMSE = 11.93 mL·kg−1·min−1). Internal validation reduced performance in both models, more markedly in the 20-m shuttle run, where the corrected R2 fell to 0.163 and the corrected RMSE increased to 13.18 mL·kg−1·min−1, compared with 0.338 and 4.37 mL·kg−1·min−1 in the Rockport walk test. Conclusions: Estimated VO2max can be predicted pragmatically using low-cost models based on simple variables in a university setting; however, model performance depends on the field test used. The Rockport walk test appears more suitable for prediction using general-purpose predictors, whereas the 20-m shuttle run may require more test-specific predictors and external validation before application beyond the development sample.