Exercise
Conditions
Keywords
Auscultation, Smartphone, Heart Sounds, Heart Auscultation, Phonocariodgraphy
Brief summary
This study aims to investigate the use of heart sound recordings, or phonocardiograms, recorded with smartphones' built-in microphones and accessory microphones to estimate heart rate (HR) and heart rate variability (HRV). HR refers to the number of heart beats per minute, while HRV refers to the variation in the intervals between heart beats. Participants will have their heart sounds (phonocardiograms) recorded before and after exercise, and electrocardiogram (ECG) and photoplethysmogram (PPG) data recorded before, during, and after exercise to induce a wide range of HR. Researchers will match the phonocardiograms, ECG, and PPG data to create a database for use in future training and testing of algorithms (including artificial intelligence (AI)).
Interventions
DIAGNOSTIC_TESTComputer algorithms
Collection of computer algorithms called ausculto® that is designed to perform heart sound analysis to estimate heart rate and heart rate variability
BEHAVIORALExercise
Participants will be requested to exercise to raise their HR over the resting range while heart-related physiological signals are collected.
Sponsors
Laboratory of Data Discovery for Health
The University of Hong Kong
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 69 Years
Healthy volunteers
Yes
Inclusion criteria
* With no tattoos on their body where a smartwatch will be placed
Exclusion criteria
* Not ready to undertake physical activity as assessed by the Physical Activity Readiness Questionnaire (PAR-Q) derived from the Canadian Society for Exercise Physiology * Implanted active medical devices in the torso, such as pacemakers and defibrillators * Failure to collect baseline heart sound time series from ECG or both smartphone and auscultation pin before exercise
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| HR estimation | Day 0 | The algorithm will estimate participants' HR from phonocardiograms at multiple timepoints. The concordance correlation coefficient, mean bias, and limits of agreement will be calculated for HR with ECG-derived HR as the gold standard. |
| HRV estimation | Day 0 | The algorithm will estimate participants' HRV from phonocardiograms at multiple timepoints. The absolute difference (error) in HRV estimation will be calculated with ECG-derived HRV as the gold standard. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Participants' comfort level for devices used in this study | Day 0 | A survey will be given to participants after heart data collection to evaluate their preferences and comfort level when using the different devices to measure HR and HRV. |
Contacts
Primary ContactJoshua Ho, Ph.D.
Outcome results
None listed