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Establish VO2max prediction models based on exercise and body parameters from the step test.

Chia-An Ho1, Hung-Chih Yeh1, Hei-Tung Lau1

  • 1Graduate Institute of Sports Science, National Taiwan Sport University, Taoyuan, Taiwan.

International Journal of Medical Sciences
|June 16, 2025
PubMed
Summary

Predicting maximal oxygen uptake (VO₂max) using simple step tests offers a practical cardiorespiratory fitness (CRF) assessment. Models incorporating heart rate and body fat percentage show high accuracy for clinical use.

Keywords:
cardiorespiratory fitness assessmentmaximal oxygen uptake prediction modelstep test

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Area of Science:

  • Exercise Physiology
  • Biomedical Engineering
  • Public Health

Background:

  • Maximal oxygen uptake (VO₂max) is the gold standard for cardiorespiratory fitness (CRF) assessment.
  • Traditional VO₂max testing can be resource-intensive and inaccessible in primary care settings.
  • There is a need for practical, low-cost methods to estimate CRF.

Purpose of the Study:

  • To develop and validate predictive models for VO₂max using step test parameters.
  • To provide a simple and practical tool for CRF assessment in clinical and remote health monitoring.
  • To compare the efficacy of models based on body mass index (BMI) versus percentage body fat (PBF).

Main Methods:

  • Recruited 200 healthy Taiwanese adults for direct VO₂max measurement and step tests.
  • Collected heart rate (HR) during exercise, age, sex, PBF, BMI, and resting heart rate (RHR) data.
  • Developed six predictive models and validated them using PRESS cross-validation and Bland-Altman plots.

Main Results:

  • Percentage body fat (PBF)-based models consistently outperformed BMI-based models.
  • The PBF3 model, incorporating exercise HR responses, demonstrated the highest accuracy (R² = 0.689; SEE = 4.6971 ml·kg⁻¹·min⁻¹).
  • Validated models showed stability and reliability across diverse subgroups.

Conclusions:

  • A step test-based model can effectively estimate VO₂max, serving as a practical tool for CRF assessment.
  • The developed model is suitable for primary healthcare, remote monitoring, and cardiac rehabilitation.
  • This research facilitates CRF self-management and offers a user-friendly alternative to high-intensity exercise tests.