Obstructive Sleep Apnea of Adult, Screening, Smart Watch
Conditions
Brief summary
This study aims to develop an artificial intelligence (AI) model for more accurately diagnosing obstructive sleep apnea (OSA) by collecting blood oxygen saturation and other health information during sleep using a smartwatch. OSA is common but often underdiagnosed, and the gold-standard diagnostic test, polysomnography, is costly and time-consuming. Smartwatches can provide a variety of health data, such as sleep patterns, blood oxygen saturation, and heart rate, which can help detect key symptoms and signs of OSA. By developing an AI model that uses smartwatch data to screen for OSA, this study seeks to offer a cost-effective and accessible diagnostic method, ultimately contributing to the early detection and improved treatment rates of OSA.
Interventions
DEVICEGalaxy Watch 4, Samsung Electronics Co., Ltd., South Korea
Use of the Galaxy Watch 4 during sleep for approximately two weeks prior to the polysomnography test, including the night of the test.
Sponsors
Seoul National University Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
22 Years to 85 Years
Healthy volunteers
No
Inclusion criteria
* Men and women aged 22 to 85 years who visited Seoul National University Hospital with suspected sleep apnea due to symptoms such as snoring, apnea, or excessive daytime sleepiness.
Exclusion criteria
* Patients previously diagnosed with sleep apnea who are currently undergoing treatment (e.g., positive airway pressure \[PAP\] therapy, mechanical ventilation, oral appliances, or surgery). * Patients with neuromuscular diseases or a history of chronic opioid medication use. * Patients with severe insomnia that is not controlled by medication. * Patients receiving supplemental oxygen therapy due to underlying conditions such as heart failure, chronic obstructive pulmonary disease, interstitial lung disease, hypoventilation syndrome, or stroke, or whose baseline oxygen saturation is less than 90%. * Patients with implanted cardiac pacemakers, defibrillators, or other electronic devices. * Patients inexperienced in using smartphones, apps, or smartwatches. * Pregnant women. * Patients unable or unwilling to provide written informed consent.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Predictive Accuracy of the AI Model for Moderate-to-Severe Obstructive Sleep Apnea | Up to 2 weeks prior to the polysomnography test. | Evaluation of how well the AI model, developed using clinical data and smartwatch-recorded information including nocturnal oxygen saturation, predicts moderate-to-severe obstructive sleep apnea (defined as apnea-hypopnea index ≥15/hour) diagnosed by polysomnography. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Predictive Accuracy of the Galaxy Watch Sleep Apnea Feature (SAF) | Up to 2 weeks prior to the polysomnography test. | Assessment of the accuracy of the Galaxy Watch's built-in sleep apnea feature (SAF) in predicting moderate-to-severe obstructive sleep apnea diagnosed by polysomnography. |
| Comparison of AI Model and Galaxy Watch Sleep Apnea Feature (SAF) Performance | Up to 2 weeks prior to the polysomnography test. | Comparison of the predictive performance between the AI model developed in this study and the Galaxy Watch's built-in sleep apnea feature (SAF) for detecting moderate-to-severe obstructive sleep apnea. |
| Comparison of AI Model and STOP-Bang Questionnaire Performance | Up to 2 weeks prior to the polysomnography test. | Comparison of the predictive performance between the AI model developed in this study and the STOP-Bang questionnaire for detecting moderate-to-severe obstructive sleep apnea. |
Countries
South Korea
Contacts
Primary ContactJaeyoung Cho, M.D., Ph.D.
Outcome results
None listed