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NCT Number: NCT06792188

Smartwatch-Based AI Model for OSA Prediction (SWOSA)

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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Key information

Conditions

Obstructive Sleep Apnea of Adult Screening Smart Watch

Age range

22 year–85 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Seoul National University Hospital

Seoul, 03080, South Korea

Location status: Recruiting

Location contact

Jaeyoung Cho, MD

CONTACT

[email protected]

82-2-2072-2503

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

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.

Primary outcomes

  1. Predictive Accuracy of the AI Model for Moderate-to-Severe Obstructive Sleep Apnea

    Time frame: 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 outcomes

  1. Predictive Accuracy of the Galaxy Watch Sleep Apnea Feature (SAF)

    Time frame: 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.

  2. Comparison of AI Model and Galaxy Watch Sleep Apnea Feature (SAF) Performance

    Time frame: 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.

  3. Comparison of AI Model and STOP-Bang Questionnaire Performance

    Time frame: 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.

Study contacts

Contact information is provided by the study sponsor or research team.

Jaeyoung Cho, M.D., Ph.D.

CONTACT

[email protected]

+82-2-2072-2503

Sponsors and collaborators

Lead sponsor

Seoul National University Hospital

Other

Registry information

Official study title

Smartwatch-Based Artificial Intelligence Model for Obstructive Sleep Apnea Prediction

Important dates

Study start
2025
Primary completion
2026
Study completion
2026
First posted
Jan 24, 2025
Registry last updated
May 15, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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