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Recruiting NCT06792188

Smartwatch-Based AI Model for OSA Prediction (SWOSA)

Observational Obstructive Sleep Apnea of Adult Screening Smart Watch

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: Galaxy Watch 4, Samsung Electronics Co., Ltd., South Korea.
Who it may be relevant to
Registry conditions: Obstructive Sleep Apnea of Adult, Screening, Smart Watch. Basic parameters: 22 years — 85 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
South Korea
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Official title

Smartwatch-Based Artificial Intelligence Model for Obstructive Sleep Apnea Prediction

Overview

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

  • Device Galaxy 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.

Primary outcome measures

  • Predictive Accuracy of the AI Model for Moderate-to-Severe Obstructive Sleep Apnea [Time frame: Up to 2 weeks prior to the polysomnography test.]
Secondary outcome measures (3)
  • Predictive Accuracy of the Galaxy Watch Sleep Apnea Feature (SAF) [Time frame: Up to 2 weeks prior to the polysomnography test.]
  • 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 AI Model and STOP-Bang Questionnaire Performance [Time frame: Up to 2 weeks prior to the polysomnography test.]

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.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model
Cohort

Study locations

South Korea · 1 center
  • Seoul National University Hospital — Seoul

Identifiers

NCT: NCT06792188 · 24111291590

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗