Obstructive sleep apnea occurs when the upper airway repeatedly narrows or closes during sleep, interrupting airflow while respiratory effort continues. These episodes can reduce blood oxygen, fragment sleep and produce repeated physiological stress. The disorder is commonly assessed using the apnea-hypopnea index, or AHI, which represents the average number of apneas and hypopneas per hour of sleep. In the study, moderate-to-severe disease was defined as an AHI of at least 15 events per hour. Although AHI is widely used, it does not fully describe how deeply or how long oxygen levels fall during individual events. For that reason, the investigators also examined hypoxic burden, a measure intended to capture the cumulative impact of oxygen desaturations.

Hypoxic burden integrates the depth, duration and frequency of oxygen drops associated with sleep apnea. This approach can distinguish between two people with similar event counts but different physiological consequences: one may experience brief, shallow desaturations, while another undergoes longer or deeper declines in oxygenation. Previous research cited by the investigators has linked higher hypoxic burden more closely with cardiometabolic risk and disease severity than AHI alone. By testing the smartwatch against both measures, the study addressed a central challenge in wearable sleep technology: whether a consumer device can identify not only frequent breathing disturbances but also people whose disturbances place greater stress on the body.

The research enrolled 152 adults aged 22 years or older who either had a previous diagnosis of moderate-to-severe sleep apnea or had a high pre-test likelihood of the disorder, defined by a STOP-Bang questionnaire score of at least three. Participants wore the Galaxy Watch during two nights of in-laboratory polysomnography. The first laboratory night was followed by at least three nights of watch-only monitoring at home, and then a second laboratory night. In total, 147 participants completed both laboratory assessments, contributing 1,850 hours of in-lab sleep data. This design allowed the researchers to compare watch estimates with PSG measurements under controlled conditions while also examining performance during ordinary nights at home.

Polysomnography records multiple physiological signals, including brain activity, eye movements, muscle activity, heart rhythm, airflow, respiratory effort and blood oxygenation. It can therefore determine sleep stages and identify the timing and consequences of respiratory events in detail, but it requires specialized equipment, trained personnel and access to a sleep laboratory. Those requirements can make testing expensive, difficult to schedule or unavailable to people living far from specialist services. A smartwatch, by contrast, can collect signals during routine sleep with little disruption. The Galaxy Watch used in this study generated an estimated AHI, or eAHI, from wearable measurements, allowing researchers to compare a device-derived screening signal with PSG-derived disease categories.

For detecting moderate-to-severe sleep apnea, the Galaxy Watch achieved an area under the receiver operating characteristic curve of 0.94, with a 95 percent confidence interval from 0.889 to 0.980. The area under this curve summarizes how well a test separates people with and without a target condition across possible thresholds; a value of 1 represents perfect discrimination, whereas 0.5 represents performance no better than chance. At the device’s default eAHI threshold of 15, sensitivity was 94.1 percent, with a 95 percent confidence interval of 84.1 to 98 percent. Specificity was 66.7 percent, with a confidence interval from 51 to 79.4 percent. In practical terms, this threshold identified most participants meeting the PSG definition, while also producing some false-positive classifications.

The researchers also tested a threshold optimized for their study cohort. At an eAHI of 25.95, sensitivity was 82.4 percent, with a 95 percent confidence interval of 69.7 to 90.4 percent, while specificity rose to 94.9 percent, with a confidence interval from 83.1 to 98.6 percent. This shift illustrates the trade-off built into screening thresholds. A lower threshold can capture more people with disease but may refer more people who ultimately do not meet the diagnostic definition. A higher threshold can reduce false alarms but may miss some affected individuals. The best threshold depends on the intended use, the population being screened and how a positive result is followed up. The study’s results therefore demonstrate strong discrimination rather than establishing a universal threshold for clinical use.

The most striking result emerged when the investigators classified participants according to PSG-derived hypoxic burden. Within the group defined as having high-risk obstructive sleep apnea, the Galaxy Watch achieved 100 percent sensitivity and 100 percent specificity using the default eAHI threshold. In this analysis, the device separated the high- and low-risk hypoxic-burden categories without misclassification in the study cohort. Because the result came from a selected group undergoing evaluation for suspected or known disease, it should not be interpreted as proof that the watch will perform identically in the general population. Confidence intervals, population differences and variations in sleep patterns can affect diagnostic accuracy. Nevertheless, the finding supports further investigation of wearable screening based on physiologically meaningful risk measures rather than event counts alone.

The researchers describe the study as evidence that consumer-grade wearables could help triage people for definitive sleep testing, particularly where access to laboratory PSG is limited. A watch-based signal could encourage earlier referral, support large-scale population screening or help clinicians prioritize patients who appear to have more consequential oxygen disturbances. It could also enable repeated measurements across several nights, potentially capturing variability that a single night may miss. At the same time, the device cannot independently establish the full clinical diagnosis, determine every type of sleep disorder or prescribe treatment. The study’s data were not publicly released because of privacy, commercial and ethical restrictions, and the underlying code was not made available for proprietary reasons. Samsung Electronics provided financial support and study materials. The trial was registered at ClinicalTrials.gov as NCT06603441, and the results point toward a future in which familiar wearable technology helps connect people with specialist care without removing the need for clinical evaluation.

The study’s prospective structure is important because wearable algorithms can appear accurate when tested retrospectively on data used during development. Here, the watch was assessed while participants underwent repeated laboratory evaluations, creating comparisons with PSG measurements collected during the same period rather than relying solely on historical records. The intervening watch-only nights also placed the device in a less controlled setting. That combination provides a more informative test of whether performance can persist beyond a single supervised examination, although it still does not reproduce the diversity of a population-wide screening program.

Interpretation of the findings should account for the composition of the enrolled group. Participants were adults already known to have moderate-to-severe OSA or considered likely to have it on the basis of STOP-Bang screening. Such an enriched sample is useful for evaluating whether a device can distinguish clinically important categories, but it may not reflect people with mild disease, no symptoms, different patterns of comorbidity or a low baseline probability of OSA. Diagnostic measures such as predictive value also change with prevalence, so results observed in this cohort cannot be transferred directly to every primary-care or consumer setting.

The threshold results have implications for how a wearable might be incorporated into care. A screening system designed to minimize missed moderate-to-severe cases could favor a more sensitive threshold and accept additional referrals for confirmatory assessment. A service facing limited diagnostic capacity might instead use a more specific threshold to prioritize patients most likely to meet laboratory criteria. Neither strategy makes the watch result definitive: a positive signal would require clinical review and diagnostic testing, while a negative result might not safely exclude disease in someone with substantial symptoms or other clinical concerns. Thresholds would also need evaluation in the intended healthcare workflow.

Hypoxic burden adds a potentially useful dimension because respiratory-event frequency alone can obscure differences in the oxygen consequences of apnea. Two individuals with comparable AHI values may not experience equivalent exposure to desaturation, and cumulative oxygen stress is relevant to understanding risk. The study therefore tests a practical screening concept: a wearable need not reproduce every PSG signal to help identify a subgroup warranting attention, provided its output is associated with a clinically meaningful physiological classification. That concept remains dependent on how hypoxic-burden categories are defined and validated across different populations and devices.

Several questions remain before routine deployment can be assumed. Independent studies would need to examine performance in broader age ranges, less-selected groups and settings outside the participating laboratory, while also assessing night-to-night stability and the consequences of algorithm errors. Evaluation against formal diagnostic pathways would clarify how often watch alerts lead to confirmed disease and appropriate treatment rather than unnecessary testing. Privacy and proprietary-code restrictions may also make independent replication more difficult. Even with those uncertainties, the results support a measured role for smartwatches as an entry point to care: they may help reveal risk at scale, while PSG or another clinically accepted diagnostic assessment remains responsible for confirmation and management decisions.

Subject of Research: Smartwatch detection of moderate-to-severe obstructive sleep apnea and high hypoxic burden

Article Title: Smartwatch-based detection of moderate-to-severe and high-risk obstructive sleep apnea

Article References: Alavi, A., Costa, E., Matsumoto, M. M. S., Odenwald, N., Elkarra, N., Ma, Y., Taweesedt, P. T., Kawai, M., Kushida, C., & Capasso, R. (2026). Smartwatch-based detection of moderate-to-severe and high-risk obstructive sleep apnea. Journal of Clinical Sleep Medicine, 22(1), Article 150. https://doi.org/10.1007/s44470-026-00159-8

Image Credits: AI Generated

DOI: 10.1007/s44470-026-00159-8

Keywords: obstructive sleep apnea, smartwatches, wearable technology, polysomnography, hypoxic burden, sleep medicine, digital health, sleep screening, Smartwatch-based, detection, moderate-to-severe, high-risk

Obstructive sleep apnea is a highly common yet underappreciated affliction that impacts more than 936 million adults worldwide, particularly those aged 30 to 69. This condition presents an array of cardiovascular risks that cannot be overlooked. Individuals suffering from OSA experience repeated interruptions in airflow during sleep due to upper airway blockage or collapse. These episodes result in shallow breathing or complete cessation of breath, often accompanied by loud snoring and gasping. Alarmingly, despite the wide reach of this disorder, it frequently goes without being diagnosed, placing individuals at risk for significant health complications.

Dr. Virend Somers, M.D., Ph.D., who serves as the Alice Sheets Marriott Professor of Cardiovascular Medicine and the senior author of the recent study published in JACC: Advances, elucidates the seriosity of the matter. Dr. Somers emphasizes that OSA not only presents an immediate health threat but also creates substantial cardiovascular challenges, to the extent that AI algorithms can effectively extract the distinctive markers of OSA from ECG data. This ECG data fundamentally represents the electrical activity of the heart’s muscle cells.

The prowess of the AI model was unveiled through an extensive analysis of over 11,000 ECG tests conducted at Mayo Clinic, all paired with comprehensive sleep evaluations. Of this cohort, more than 7,000 patients had already been diagnosed with OSA, while around 4,000 served as control subjects without the disorder. The research aimed to leverage cutting-edge AI algorithms to uncover correlations between the ECG readings and OSA, providing powerful insights to medical professionals.

One of the most astonishing revelations from the study was the enhanced detection of OSA in the female participants compared to their male counterparts, despite the lower severity of the condition in women. Dr. Somers remarked on this unexpected finding, stressing its significance in light of emerging literature that indicates women who suffer from OSA may experience more pronounced cardiovascular repercussions, even when their sleep apnea presents as less severe by standard diagnostic standards.

This discrepancy raises critical questions regarding the current understanding of OSA and its effects on different demographics. The findings suggest that although women may present with milder OSA symptoms, the potential damage to their heart muscle cells could be more substantial than previously assumed. Dr. Somers points out the need for a reassessment of how OSA is diagnosed and treated, particularly among women, to mitigate the pressing cardiovascular risks associated with the disorder.

Moreover, the research team underscored that the implications of their AI-driven approach extend beyond mere diagnosis. Analysis using this developed model has the capability to evaluate how various OSA treatments influence a patient’s cardiovascular risk, which is a crucial factor that can drastically change patient outcomes and overall quality of life. By understanding the relationship between OSA treatments and cardiovascular health, clinicians can make more informed decisions tailored to each patient’s needs.

With significant strides being made in the realm of AI-assisted medical diagnostics, the future for those affected by sleep apnea appears promising. The development of the AI algorithm at Mayo Clinic represents a landmark achievement that could alter the landscape of sleep medicine. As OSA remains commonly misrecognized or underestimated, this technology can provide a critical tool in identifying those at risk more reliably and efficiently.

The implications of these findings stretch far and wide, potentially leading to national campaigns aimed at raising awareness about OSA and its widespread impacts. There is a clear and urgent need to address the underdiagnosis of women and to ensure that effective screening measures are put in place to capture the true prevalence of this disorder in all affected populations.

This technology’s emergence at Mayo Clinic is just the start of a transformative journey in biomedical research and cardiology, setting the stage for further studies that could bolster understanding of OSA while promoting a more robust, health-centric approach to treatment regimens inspired by comprehensive and precise data analytics.

In conclusion, the innovative AI algorithm developed at the Mayo Clinic is equipped to illuminate the less understood dynamics of obstructive sleep apnea, offering both a diagnostic breakthrough and a beacon of hope for millions worldwide. As this technology evolves, the potential to personalize and improve treatment strategies for OSA will revolutionize patient care, ensuring that underlying cardiovascular risks are identified and managed effectively.

Subject of Research: AI algorithm for detection of obstructive sleep apnea using ECG
Article Title: Deep Neural Network Algorithm Using the Electrocardiogram for Detection of Obstructive Sleep Apnea
News Publication Date: 16-Sep-2025
Web References: (Links to sources not provided in this context)
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Keywords

Obstructive sleep apnea, artificial intelligence, electrocardiogram, cardiovascular health, women’s health, Mayo Clinic, medical research, AI algorithms, diagnostics.