Advancing Sleep Apnea Diagnosis: Smartwatches Versus Traditional Methods
Exploring the capabilities of smartwatches in medical diagnostics, recent research compares the accuracy of a popular smartwatch with traditional polysomnography (PSG) for measuring oxygen levels overnight and assessing sleep apnea severity. The study focuses on how well the smartwatch's sensors detect oxygen saturation and breathing irregularities, which are crucial for diagnosing obstructive sleep apnea (OSA), a condition where breathing stops intermittently during sleep, posing serious health risks.
Researchers found that the smartwatch, while less precise than PSG, offers a promising alternative for continuous monitoring of oxygen saturation with minimal error. The wearable device demonstrated good potential in predicting the severity of sleep apnea compared to the gold-standard PSG, particularly when combined with sleep questionnaire data. This suggests that smartwatches could play a significant role in early screening and ongoing management of sleep disorders.
The findings indicate that wearable technology could enhance access to sleep apnea testing and monitoring, making it more convenient and cost-effective. This shift could lead to broader, more proactive management of sleep health, emphasizing the integration of technology in preventive healthcare.
Article Information
Performance of a commercial smart watch compared to polysomnography reference for overnight continuous oximetry measurement and sleep apnea evaluation
Published in J Clin Sleep Med. Sara H Browne et al.
Abstract
Study objectives: To evaluate the accuracy and precision of continuous overnight oxygen saturation (SpO2) measurement by a commercial wrist device (WD) incorporating high-grade sensors, and investigate WD estimation of sleep-disordered breathing by quantifying overnight oxygen desaturation index (ODI) compared to polysomnography (PSG) ODI and apnea-hypopnea index (AHI) with and without sleep questionnaire data, to assess WD ability to detect obstructive sleep apnea (OSA) and determine its severity.
Methods: Participants completed sleep questionnaires, had a WD (Samsung Galaxy Watch 4) placed on their wrist, and underwent attending, in-lab overnight PSG (Nihon Kohden) with pulse oximetry probe secured either to a finger or ear lobe. PSG data was scored by a single experienced registered PSG technologist. Statistical analysis included demographic characteristics, continuous SpO2 measurement WD vs PSG root mean square error (RMSE) with Bland Altman plot and linear regression associations. Predictive models for PSG ODI and AHI severity were built using logistic regression with probability cutoffs determined via receiver operating curve (ROC) characteristics.
Results: The 51 participants analyzed had median age of 49 (range 22-78) years, 66.7% were male, with median body mass index (BMI) 28.1 (range 20.1, 47.3) kg/m2 with race/ethnicity distribution of 49.0% Caucasian, 25.5% Hispanic, 9.8% African-American, 9.8% Asian, and 5.9% Middle Eastern. WD vs PSG continuous SpO2 measurement in percentage points demonstrated bias of 0.91 (CI95 0.38, 1.45), standard deviation 2.37 (CI95 2.36, 2.38), and RMSE 2.54 (CI95 2.34, 2.73). WD area under the curve (AUC) ROC characteristics for predicting PSG were 0.882 ODI>15/h, 0.894 AHI>30/h, 0.800 AHI>15/h, and 0.803 AHI>5/h. WD plus select sleep questionnaire AUCs for predicting PSG were 0.943 AHI>30/h, 0.868 AHI>15/h, and 0.863 AHI>5/h.
Conclusions: The WD conducted reliable overnight continuous SpO2 monitoring with RMSE <3% vs PSG. Predictive models of PSG AHI based on WD measurements alone, or plus sleep questionnaires, demonstrated excellent to outstanding discrimination for OSA identification and severity. Longitudinal WD use should be evaluated promptly based on WD potential to improve accessibility and accuracy of OSA testing, as well as support treatment follow-up.
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