Heart rate, respiratory rate and airflow variability differences between stable and exacerbating chronic obstructive pulmonary disease patients
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Rationale
Earlier identification and treatment of chronic obstructive pulmonary disease exacerbations leads to improved clinical outcomes. Wearable technology has the ability to measure physiological signal variability which is likely to be different in states of stability and exacerbation.
Objectives
To analyse signals including heart rate, respiratory rate and airflow, from a novel small wearable device, AcuPebble RE100 and compare differences in a group of stable and exacerbating participants.
Methods
Groups of stable and exacerbating adult participants with chronic obstructive pulmonary disease were asked to wear AcuPebble RE100, which records physiological signals including heart rate, respiratory rate and airflow. Linear and non-linear variability analysis was conducted on each of these time-series to detect differences between groups.
Results
A total of 51 participants (33 stable and 18 exacerbating) were analysed. Stable participants used the device for a median (IQR) of 18 nights (10–26). The exacerbating participants had significantly higher heart rate variability measures and a significantly lower heart rate complexity measure compared to stable participants. Respiratory rate variability and complexity were significantly increased in the exacerbating participants. Detrended fluctuation analysis demonstrated two-cross over points in both populations, with the exacerbating participants demonstrating a significantly lower median α3 (0.50 (0.47–0.56) versus 0.69 (0.65–0.79), p <0.001) compared to the stable population.
Conclusion
We have shown that significant differences exist in heart rate, respiratory rate and airflow variability measures between stable and exacerbating groups of chronic obstructive pulmonary disease. This will help build exacerbation detection algorithms in the future.
Earlier identification and treatment of chronic obstructive pulmonary disease exacerbations leads to improved clinical outcomes. Wearable technology has the ability to measure physiological signal variability which is likely to be different in states of stability and exacerbation.
Objectives
To analyse signals including heart rate, respiratory rate and airflow, from a novel small wearable device, AcuPebble RE100 and compare differences in a group of stable and exacerbating participants.
Methods
Groups of stable and exacerbating adult participants with chronic obstructive pulmonary disease were asked to wear AcuPebble RE100, which records physiological signals including heart rate, respiratory rate and airflow. Linear and non-linear variability analysis was conducted on each of these time-series to detect differences between groups.
Results
A total of 51 participants (33 stable and 18 exacerbating) were analysed. Stable participants used the device for a median (IQR) of 18 nights (10–26). The exacerbating participants had significantly higher heart rate variability measures and a significantly lower heart rate complexity measure compared to stable participants. Respiratory rate variability and complexity were significantly increased in the exacerbating participants. Detrended fluctuation analysis demonstrated two-cross over points in both populations, with the exacerbating participants demonstrating a significantly lower median α3 (0.50 (0.47–0.56) versus 0.69 (0.65–0.79), p <0.001) compared to the stable population.
Conclusion
We have shown that significant differences exist in heart rate, respiratory rate and airflow variability measures between stable and exacerbating groups of chronic obstructive pulmonary disease. This will help build exacerbation detection algorithms in the future.
Date Issued
2025-11-01
Date Acceptance
2025-04-23
Citation
ERJ Open Research, 2025, 11 (6)
ISSN
2312-0541
Publisher
European Respiratory Society
Start Page
00234
End Page
2025
Journal / Book Title
ERJ Open Research
Volume
11
Issue
6
Copyright Statement
©The authors 2025 This version is distributed under the terms of the Creative Commons Attribution Non-Commercial Licence 4.0. For commercial reproduction rights and permissions contact permissions@ersnet.org
License URL
Identifier
10.1183/23120541.00234-2025)
Publication Status
Published
Article Number
ARTN 00234-2025
Date Publish Online
2025-05-16