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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Smart Watch Group | 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. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Galaxy Watch 4, Samsung Electronics Co., Ltd., South Korea | Device | Use of the Galaxy Watch 4 during sleep for approximately two weeks prior to the polysomnography test, including the night of the test. |
| Measure | Description | Time Frame |
|---|---|---|
| Predictive Accuracy of the AI Model for Moderate-to-Severe Obstructive Sleep Apnea | 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. | Up to 2 weeks prior to the polysomnography test. |
| Measure | Description | Time Frame |
|---|---|---|
| Predictive Accuracy of the Galaxy Watch Sleep Apnea Feature (SAF) | 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. | Up to 2 weeks prior to the polysomnography test. |
| Comparison of AI Model and Galaxy Watch Sleep Apnea Feature (SAF) Performance |
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Inclusion Criteria:
Exclusion Criteria:
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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.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Jaeyoung Cho, M.D., Ph.D. | Contact | +82-2-2072-2503 | apricot6@snu.ac.kr |
| Name | Affiliation | Role |
|---|---|---|
| Jaeyoung Cho, M.D., Ph.D. | Seoul National University Hospital | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Seoul National University Hospital | Recruiting | Seoul | 03080 | South Korea |
De-identified individual participant data that support the findings of this study
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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. |
| Up to 2 weeks prior to the polysomnography test. |
| Comparison of AI Model and STOP-Bang Questionnaire Performance | 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. | Up to 2 weeks prior to the polysomnography test. |