Instalab

Can the Apple Watch Help Detect Sleep Apnea?

Sleep apnea, a disorder marked by repeated interruptions in breathing during sleep, affects millions of people worldwide, yet more than 80% of cases remain undiagnosed. The consequences can be severe: fragmented sleep, excessive daytime sleepiness, cardiovascular strain, and heightened risk for conditions like hypertension and atrial fibrillation.

Traditionally, diagnosing sleep apnea requires overnight polysomnography (PSG), a gold-standard but resource-intensive test that demands specialized equipment, trained personnel, and a sleep lab. The question is, can consumer technology, specifically the Apple Watch, step in as a credible early detection tool?
Instalab Research

The Growing Role of Wearables in Sleep Medicine

Over the last decade, wearable devices have evolved from simple step counters into multi-sensor health monitors capable of tracking heart rate, oxygen saturation, body movement, and even cardiac signals through photoplethysmography (PPG) and inertial measurement units (IMUs). The Apple Watch, with its large user base and powerful sensor suite, is uniquely positioned for large-scale sleep health monitoring.

Multiple studies have explored the feasibility of using consumer-grade wearables for sleep tracking, with the Apple Watch showing high accuracy in detecting sleep and wake states compared to validated actigraphy devices. The question is whether this performance extends to identifying breathing-related disorders like sleep apnea.

Apple Watch and Direct Sleep Apnea Detection

A recent pilot study used only the Apple Watch’s IMU sensors to detect sleep apnea. By extracting seismocardiographic and respiratory signals from wrist movements and subtle body vibrations, researchers analyzed over 52,000 30-second epochs from adults undergoing PSG. Nearly a quarter represented apnea or hypopnea events.

Machine learning algorithms, particularly a Random Forest classifier, achieved strong predictive performance:

  • Per-epoch detection reached an AUC of over 0.83 in the test group.
  • Per-subject correlation with the apnea-hypopnea index (AHI) from PSG was exceptionally high (r = 0.93).
  • For identifying moderate to severe apnea (AHI ≥ 15), sensitivity was 100% and specificity 90%.

These findings suggest the Apple Watch could serve as a high-accessibility screening tool for sleep apnea without additional hardware, potentially transforming early diagnosis rates.

Using Heart Rate and Oxygen Signals

In addition to motion-based detection, other studies have evaluated the Apple Watch’s ability to monitor cardiorespiratory signals relevant to apnea diagnosis.

Patients with obstructive sleep apnea often have cardiovascular comorbidities such as atrial fibrillation. Research has shown the Apple Watch to have strong agreement with clinical telemetry in heart rate measurement for these patients, suggesting it can provide reliable heart rate data that may be incorporated into apnea screening models.

The Apple Watch also uses PPG-based sensors to estimate oxygen saturation, another key metric in apnea detection. Studies comparing it to PSG found moderate diagnostic accuracy for detecting mild OSA, although accuracy declined with increasing apnea severity. This indicates oxygen data should be combined with other measurements for reliable detection.

Comparisons With Other Smartwatch-Based Systems

The Apple Watch is not the only smartwatch under investigation for apnea detection, but it compares favorably with others. Research on smartwatch-based OSA detection against PSG in large participant groups has found high sensitivity and specificity, particularly in severe apnea cases.

Other PPG-based wearable devices have demonstrated sensitivity above 80% and specificity close to 90% for moderate-to-severe apnea detection when analyzing heart rate variability patterns. These findings suggest that as detection algorithms improve, consumer smartwatches will become increasingly viable tools for apnea screening.

Limitations and Considerations

  • Severity sensitivity – Accuracy can decline when distinguishing between mild, moderate, and severe apnea.
  • Controlled conditions – Many studies have been performed in clinical or semi-controlled environments, so long-term real-world performance still needs more data.
  • Algorithm accessibility – The most effective detection methods use machine learning models not yet integrated into Apple’s native software, meaning third-party apps or future software updates would be required for widespread screening.

The Future of At-Home Screening

With more than 30 million Apple Watches in circulation, even modest screening accuracy could dramatically expand early detection, especially in communities with limited access to sleep clinics. In the future, nightly watch data could quietly flag potential apnea events, prompting users to seek a formal sleep study. This could shorten diagnosis timelines from years to weeks.

Research is progressing toward fully automated algorithms that combine motion, heart rate, and oxygen data for robust apnea screening. As these algorithms integrate into consumer devices, the gap between medical-grade and consumer-grade monitoring will continue to narrow.

Take the Next Step in Detecting Sleep Apnea

If you suspect you may have sleep apnea, you can order a validated at-home sleep test kit like the WatchPAT One. This FDA-cleared device features a wrist sensor, pulse oximeter, and chest sensor, using three points of contact: the wrist, finger, and neck. It measures central and obstructive sleep apnea events, true sleep time, snoring, breathing rates, oxygen levels, heart rate, body position, and sleep stages.

References
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  • Hayano, J., Yamamoto, H., Nonaka, I., Komazawa, M., Itao, K., Udea, N., Tanaka, H., & Yuda, E., 2020. Quantitative detection of sleep apnea with wearable watch device. PLoS ONE, 15. https://doi.org/10.1371/journal.pone.0237279.
  • Hayano, J., Adachi, M., Murakami, Y., Sasaki, F., & Yuda, E., 2025. Detection of sleep apnea using only inertial measurement unit signals from apple watch: a pilot-study with machine learning approach. Sleep & Breathing = Schlaf & Atmung, 29. https://doi.org/10.1007/s11325-025-03255-w.
  • Huynh, P., Shan, R., Osuji, N., Ding, J., Isakadze, N., Marvel, F., Sharma, G., & Martin, S., 2021. Heart Rate Measurements in Patients with Obstructive Sleep Apnea and Atrial Fibrillation: Prospective Pilot Study Assessing Apple Watch’s Agreement With Telemetry Data. JMIR Cardio, 5. https://doi.org/10.2196/18050.
  • Kim, M., Park, S., & Choi, M., 2022. Diagnostic Performance of Photoplethysmography-Based Smartwatch for Obstructive Sleep Apnea. Journal of Rhinology, 29, pp. 155 - 162. https://doi.org/10.18787/jr.2022.00424.
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