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Feasibility of Using a Single Heart Rate-Based Measure for Real-time Feedback in a Voluntary Deep Breathing App for
Christian L Petersen1, Matthias Görges1,2, Evgenia Todorova3,4
1Department of Anesthesiology, Pharmacology & Therapeutics, The University of British Columbia, Vancouver, BC, Canada.
Insights
This study developed a smartphone app using biofeedback to help children with deep diaphragmatic breathing. The app effectively measures breathing compliance, showing improved results during paced breathing exercises.
Area of Science:
- Pediatric psychology
- Biomedical engineering
- Digital health
Background:
- Deep diaphragmatic breathing (belly breathing) is a key intervention for managing anxiety, stress, and pain in children.
- Integrating physiological monitoring with mobile technology offers a novel approach to enhance patient engagement and adherence to behavioral interventions.
- This technology has the potential to improve patient experience and compliance, aiding in anxiety reduction, pain modulation, and self-regulation during medical procedures.
Purpose of the Study:
- To introduce a straightforward biofeedback methodology for quantifying breathing compliance within a mobile smartphone application.
- To develop and validate a tool for objective assessment of deep breathing exercises in pediatric populations.
Main Methods:
- A smartphone application was created, integrating pulse oximetry with an animated paced deep breathing protocol.
- Photoplethysmogram (PPG) data were acquired from children during both spontaneous and paced deep breathing.
- Key metrics, synchronized respiratory sinus arrhythmia (RSAsync) and the heart rate inspiratory:expiratory ratio (HR-I:Esync), were extracted from PPG signals.
Main Results:
- Eighty children (ages 5-17) demonstrated a significant positive RSAsync effect during paced deep breathing.
- The median HR-I:Esync ratio was significantly higher during paced deep breathing (1.26) compared to spontaneous breathing (0.98).
- The HR-I:Esync values were found to be independent of the participants' age.
Conclusions:
- A heart rate inspiratory:expiratory synchronized (HR-I:Esync) level of 1.1 was established as an age-independent threshold.
- This threshold can be utilized for programming breathing patterns to optimize compliance in biofeedback applications.
- The study validates a mobile biofeedback approach for improving children's adherence to deep breathing exercises.
Background:
Deep diaphragmatic breathing, also called belly breathing, is a popular behavioral intervention that helps children cope with anxiety, stress, and their experience of pain. Combining physiological monitoring with accessible mobile technology can motivate children to comply with this intervention through biofeedback and gaming. These innovative technologies have the potential to improve patient experience and compliance with strategies that reduce anxiety, change the experience of pain, and enhance self-regulation during distressing medical procedures.
Objective:
The aim of this paper was to describe a simple biofeedback method for quantifying breathing compliance in a mobile smartphone app.
Methods:
A smartphone app was developed that combined pulse oximetry with an animated protocol for paced deep breathing. We collected photoplethysmogram data during spontaneous and subsequently paced deep breathing in children. Two measures, synchronized respiratory sinus arrhythmia (RSAsync) and the corresponding relative synchronized inspiration/expiration heart rate ratio (HR-I:Esync), were extracted from the photoplethysmogram.
Results:
Data collected from 80 children aged 5-17 years showed a positive RSAsync effect in all participants during paced deep breathing, with a median (IQR; range) HR-I:Esync ratio of 1.26 (1.16-1.35; 1.01-1.60) during paced deep breathing compared to 0.98 (0.96-1.02; 0.82-1.18) during spontaneous breathing (median difference 0.25, 95% CI 0.23-0.30; P<.001). The measured HR-I:Esync values appeared to be independent of age.
Conclusions:
An HR-I:Esync level of 1.1 was identified as an age-independent threshold for programming the breathing pattern for optimal compliance in biofeedback.
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