Detecting Sleep Apnea with Smart-tech: Scientific Breakthrough or Overhyped Technology?
DOI:
https://doi.org/10.12775/QS.2025.40.59824Keywords
sleep apnea, smart-tech, sleep monitoringAbstract
Introduction: This review paper seeks to examine the existing knowledge on the underlying mechanisms of sleep apnea, the complexities associated with its diagnosis, and the potential of cutting-edge wearable technology to enhance its detection.
Materials and Methods: A comprehensive review of the literature was conducted using the PubMed and Google Scholar databases using the following keywords: “Sleep apnea”, “Sleep disorder”, “Smart-tech”, “Smartwatch”, “Apnea treatment”, “Apnea monitoring”.
Summary: Sleep plays a pivotal role in maintaining both physical and mental well-being. Sleep disorders can severely impair cognitive functions such as concentration and memory, while also leading to chronic fatigue and emotional instability. One of the most prevalent conditions is obstructive sleep apnea, characterized by recurrent interruptions in breathing during sleep, which, if left untreated, may result in serious health complications. Diagnosing and monitoring sleep apnea remains a challenge due to the shortage of specialized sleep laboratories. The integration of smart technology wearables has the potential to usher in a new era in the diagnosis and management of sleep apnea, along with the associated health complications.
Conclusions: Advanced sleep monitoring technologies, ranging from traditional polysomnography to modern wearables and contactless systems, enable precise assessment of sleep disorders by continuously tracking key physiological parameters. An effective system must accurately differentiate between apnea types, assess severity using the apnea-hypopnea index (AHI), and analyze factors like body position to provide tailored treatment recommendations. Extending sleep monitoring beyond single-night laboratory studies to long-term home-based tracking enhances diagnostic accuracy, optimizes treatment strategies, and improves overall sleep health.
References
[1] Zhang J, Zhang Q, Wang Y, Qiu C. 2013. A real-time auto-adjustable smart pillow system for sleep apnea detection and treatment. ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN): 179-190.
[2] Barika R., Shenfield A, Razaghi H, Faust O. 2021. A smart sleep apnea detection service. In: Proceedings of CM 2021. The British Institute of NDT. [Book Section]
[3] Chen X, Xiao Y, Tang Y, Fernandez-Mendoza J, Cao G. ApneaDetector. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 2021;5(2):1-22. doi:https://doi.org/10.1145/3463514
[4] Tiron R, Lyon G, Kilroy H, et al. Screening for obstructive sleep apnea with novel hybrid acoustic smartphone app technology. Journal of Thoracic Disease. 2020;12(8):4476-4495. doi:https://doi.org/10.21037/jtd-20-804
[5] Foldvary-Schaefer NR, Waters TE. Sleep-Disordered Breathing. CONTINUUM: Lifelong Learning in Neurology. 2017;23(4):1093-1116. doi:https://doi.org/10.1212/01.con.0000522245.13784.f6
[6] Jafari B, Mohsenin V. Polysomnography. Clinics in Chest Medicine. 2010;31(2):287-297. doi:https://doi.org/10.1016/j.ccm.2010.02.005
[7] Rundo JV. Obstructive sleep apnea basics. Cleveland Clinic Journal of Medicine. 2019;86(9 suppl 1):2-9. doi:https://doi.org/10.3949/ccjm.86.s1.02
[8] Jordan AS, McSharry DG, Malhotra A. Adult obstructive sleep apnoea. The Lancet. 2014;383(9918):736-747. doi:https://doi.org/10.1016/s0140-6736(13)60734-5
[9] Massie F, Van Pee B, Bergmann J. Correlations between home sleep apnea tests and polysomnography outcomes do not fully reflect the diagnostic accuracy of these tests. Journal of Clinical Sleep Medicine. 2022;18(3):871-876. doi:https://doi.org/10.5664/jcsm.9744
[10] Chiu HY, Chen PY, Chuang LP, et al. Diagnostic accuracy of the Berlin questionnaire, STOP-BANG, STOP, and Epworth sleepiness scale in detecting obstructive sleep apnea: A bivariate meta-analysis. Sleep Medicine Reviews. 2017;36:57-70. doi:https://doi.org/10.1016/j.smrv.2016.10.004
[11] Manoni A, Loreti F, Radicioni V, et al. A New Wearable System for Home Sleep Apnea Testing, Screening, and Classification. Sensors. 2020;20(24):7014. doi:https://doi.org/10.3390/s20247014
[12] Strumpf ZB, Gu W, Tsai CW, et al. Belun Ring (Belun Sleep System BLS-100): Deep learning-facilitated wearable enables obstructive sleep apnea detection, apnea severity categorization, and sleep stage classification in patients suspected of obstructive sleep apnea. Sleep Health. 2023;9(4):430-440. doi:https://doi.org/10.1016/j.sleh.2023.05.001
[13] ISO80601-2-61:2017; MedicalElectrical Equipment—Part 2-61: Particular Requirements for Basic Safety and Essential Performance of Pulse Oximeter Equipment. ISO: Geneva, Switzerland, 2017.
[14] Walzel S, Mikus R, Rafl-Huttova V, Rozanek M, Bachman TE, Jakub Rafl. Evaluation of Leading Smartwatches for the Detection of Hypoxemia: Comparison to Reference Oximeter. Sensors. 2023;23(22):9164-9164. doi:https://doi.org/10.3390/s23229164
[15] Schyvens AM, Van Oost N, Aerts JM, et al. Accuracy of Fitbit Charge 4, Garmin Vivosmart 4 and WHOOP versus polysomnography: a systematic review of the literature (Preprint). JMIR mhealth and uhealth. Published online August 25, 2023. doi:https://doi.org/10.2196/52192
[16] Moreno-Pino F, Porras-Segovia A, López-Esteban P, Artés A, Baca-García E. Validation of Fitbit Charge 2 and Fitbit Alta HR Against Polysomnography for Assessing Sleep in Adults With Obstructive Sleep Apnea. Journal of Clinical Sleep Medicine. 2019;15(11):1645-1653. doi:https://doi.org/10.5664/jcsm.8032
[17] Ganglberger W, Bucklin AA, Tesh RA, et al. Sleep apnea and respiratory anomaly detection from a wearable band and oxygen saturation. Sleep And Breathing. 2021;26(3):1033-1044. doi:https://doi.org/10.1007/s11325-021-02465-2
[18] Braghiroli A, Kuller D, Massimo Godio, Rossato F, Sacco C, Morrone E. Validation Study of Airgo, an Innovative Device to Screen Sleep Respiratory Disorders. Frontiers in Medicine. 2022;9. doi:https://doi.org/10.3389/fmed.2022.938542
[19] Baty F, Boesch M, Widmer S, et al. Classification of Sleep Apnea Severity by Electrocardiogram Monitoring Using a Novel Wearable Device. Sensors. 2020;20(1):286. doi:https://doi.org/10.3390/s20010286
[20] Fontana P, Adão R, Camenzind M, et al. Applicability of a Textile ECG-Belt for Unattended Sleep Apnoea Monitoring in a Home Setting. Sensors. 2019;19(15):3367-3367. doi:https://doi.org/10.3390/s19153367
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Copyright (c) 2025 Aleksandra Głowacka, Paulina Grzeszczuk, Iwona Skorulska, Agnieszka Kalisz, Marta Ignatiuk-Chilkiewicz, Weronika Grywińska, Julia Kozakiewicz, Kamil Kościelecki, Klaudia Mączewska, Patrycja Długozima

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