Clinical trial NCT06792188: Smartwatch-Based AI Model for OSA Prediction (SWOSA)
Official title: Smartwatch-Based Artificial Intelligence Model for Obstructive Sleep Apnea Prediction
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Obstructive Sleep Apnea of Adult
Screening
Smart Watch
Lead sponsor: Seoul National University Hospital
Sponsor class: OTHER
First posted: 2025-01-24
Enrollment: 147
Sex: ALL
Minimum age (years): 22
Maximum age (years): 85
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: 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.

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.


Eligibility criteria: 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.

Primary outcomes:
measure: Predictive Accuracy of the AI Model for Moderate-to-Severe Obstructive Sleep Apnea
timeFrame: Up to 2 weeks prior to the polysomnography test.
Secondary outcomes:
measure: Predictive Accuracy of the Galaxy Watch Sleep Apnea Feature (SAF)
timeFrame: Up to 2 weeks prior to the polysomnography test.
measure: Comparison of AI Model and Galaxy Watch Sleep Apnea Feature (SAF) Performance
timeFrame: Up to 2 weeks prior to the polysomnography test.
measure: Comparison of AI Model and STOP-Bang Questionnaire Performance
timeFrame: Up to 2 weeks prior to the polysomnography test.
Other outcomes:
Locations:
city: Seoul
country: Korea, Republic of
facility: Seoul National University Hospital
Study officials:
affiliation: Seoul National University Hospital
name: Jaeyoung Cho, M.D., Ph.D.
role: PRINCIPAL_INVESTIGATOR
References: