# An evidence-based multi-factorial model to predict the oxygen cost of ventilation during ramp-incremental cycle ergometry exercise

**Authors:** Bridgette G. J. O’Malley, Robert A. Robergs, Karel Hrach, Chantal A. Vella, Derek W. Marks

PMC · DOI: 10.3389/fphys.2026.1702120 · Frontiers in Physiology · 2026-02-19

## TL;DR

This study creates a model to estimate the oxygen cost of breathing during intense cycling exercise and shows it significantly affects oxygen uptake measurements.

## Contribution

A novel non-linear model predicts oxygen cost of ventilation and its impact on maximal oxygen uptake estimates during ramp-incremental cycling.

## Key findings

- The model explains 81% of variance in oxygen cost of ventilation (V̇O2VENT).
- V̇O2VENT contributes 17.43% of total oxygen uptake at maximal exercise.
- Correcting for V̇O2VENT significantly lowers estimated oxygen uptake values near exhaustion.

## Abstract

During maximal ramp-incremental exercise (RIE), the oxygen uptake–power output relationship (
V˙
O2gain) may deviate from linearity near exhaustion. An increased oxygen cost of ventilation (
V˙
O2VENT) is a plausible but under-quantified contributor. This study tested a non-linear multi-factorial model using measured 
V˙
O2VENT and six predictors: resting expired ventilation (
V˙

E), weight, height, age, 
V˙
O2 peak, and maximal heart rate (HRMax) to 1) estimate 
V˙
O2VENT and its contribution to maximal oxygen uptake (
V˙
O2max) in an independent dataset and 2) determine whether correcting 
V˙
O2 by 
V˙
O2VENT (
V˙
O2VCORR) alters 
V˙
O2max and 
V˙
O2gain estimates.

Published data from 42 participants (11 women, 31 men; 29 ± 6.5 years; 
V˙
O2max = 4.02 ± 1.06 L min−1) were used to derive the model. Leave-one-out cross-validation (LOOCV) was used to assess validity, with predictive accuracy and coefficient stability evaluated via bootstrap resampling. The model was applied to an independent RIE dataset to generate 
V˙
O2VCORR, which was compared with uncorrected 
V˙
O2 across six %Wpeak intensities using repeated-measures ANOVA and final 30 s slope analysis.

The model explained 81% of 
V˙
O2VENT variance (adjusted R
2 = 0.78). 
V˙
O2VENT represented 17.43% ± 3.58% of 
V˙
O2 at 
V˙
O2max. Across 35%–100% Wpeak, 
V˙
O2VCORR values (L·min−1) increased with intensity (1.77 ± 0.43, 2.68 ± 0.57, 3.43 ± 0.72, 3.72 ± 0.79, 3.84 ± 0.86, and 3.92 ± 0.82) but remained significantly lower than uncorrected 
V˙
O2 (p < 0.001), with the final-30 s 
V˙
O2 slope attenuated following correction (p = 0.002).

The internally validated model revealed 
V˙
O2VENT may contribute to a significant fraction of 
V˙
O2 near exhaustion.

## Full-text entities

- **Diseases:** hypocapnia (MESH:D016857), hyperventilation (MESH:D006985), fatigue (MESH:D005221), respiratory muscle fatigue (MESH:D012133)
- **Chemicals:** ATP (MESH:D000255), CO2 (MESH:D002245), hydrogen (MESH:D006859), O2-PO (-), catecholamines (MESH:D002395), PO (MESH:D011059), E (MESH:D004540), O2 (MESH:D010100), lactate (MESH:D019344)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

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## Figures

6 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12960085/full.md

## References

38 references — full list in the complete paper: https://tomesphere.com/paper/PMC12960085/full.md

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Source: https://tomesphere.com/paper/PMC12960085