Related Experiment Video
Updated: Sep 9, 2026
Using Near-Infrared Spectroscopy Wearable Devices to Identify Central Versus Peripheral Limitations During Exercise
Published on: December 19, 2024
A protocol for validation of novel artificial intelligence-based framework for dyspnoea investigation with
Abed A Hijleh1, Danilo C Berton2, Matthew D James1
1Respiratory Investigation Unit, Division of Respirology, Department of Medicine, Queen's University, Kingston, ON, Canada.
Background:
Exertional dyspnoea largely represents the sensory translation of an ever-growing dynamic mismatch between ventilatory demand and capacity as exercise intensifies. This fundamental tenet, however, has not been formally incorporated into data display and clinical interpretation of incremental cardiopulmonary exercise testing (CPET). The objectives of the present study were to validate a novel framework (Dynamic Assessment of Dyspnoea and Ventilation on Exercise (DyVe-X)) to quantify the severity of exertional dyspnoea while exposing its mechanical-ventilatory underpinnings across progressively higher exercise intensities in subjects showing varied pre-test likelihood of abnormality.
Methods:
Incremental CPET data from three large cohorts of tobacco-exposed subjects: smokers and ex-smokers at risk for, or at different stages of, COPD (n=1161). Based on recently published normative data, a homonymous software (DyVe-X 1.0.0 Kingston, ON, Canada) uses an algorithm based on artificial intelligence techniques to establish the burden of 1) exertional dyspnoea versus work rate and ventilation, 2) excessive breathing (low submaximal ventilatory reserve, high dyspnoea-work rate but preserved dyspnoea-ventilation), and 3) constrained breathing (reduced inspiratory reserve, high dyspnoea-work rate and dyspnoea-ventilation). We hypothesise that categories of progressive mechanical-ventilatory abnormalities, based on DyVe-X, would exhibit progressively worse physiological and sensory CPET outcomes. Using the current key criterion to indicate ventilatory limitation to exercise (peak breathing reserve ≤15%) as the comparator, we anticipate a superior performance of DyVe-X in exposing dyspnoea-generating mechanical-ventilatory abnormalities across cohorts.
Implications:
This study may validate an original framework for investigating dyspnoea using CPET, characterised by continuous assessment of symptom intensity and time course, in light of the rate of dynamic depletion of mechanical-ventilatory reserves.
