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Author Spotlight: Evaluating the Therapeutic Efficacy of Moving Cupping Along Meridians for Acute Exacerbation of COPD
Published on: September 27, 2024
Heart rate, respiratory rate and airflow variability differences between stable and exacerbating COPD patients
Amar J Shah1,2, Anita Saigal1,2, Renard Pramono3,4
1UCL Respiratory, University College London, London, UK.
Rationale:
Earlier identification and treatment of COPD 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. The objectives of the present study were 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 COPD were asked to wear AcuPebble RE100, which records physiological signals including heart rate, respiratory rate and airflow. Linear and nonlinear 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 (interquartile range) of 18 nights (10-26). The exacerbating participants had significantly higher heart rate variability measures and a significantly lower heart rate complexity measure compared with stable participants. Respiratory rate variability and complexity were significantly increased in the exacerbating participants. Detrended fluctuation analysis demonstrated two crossover 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 with 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 COPD. This will help build exacerbation detection algorithms in the future.
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