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<p>Obstructive sleep apnea (OSA) is a common sleep disorder associated with cardiovascular and metabolic consequences. Although polysomnography (PSG) is the gold standard for diagnosis, it is costly and resource-intensive, limiting its use in population-wide screening. Smart wearable devices offer a non-invasive and accessible alternative, but their clinical validation remains limited. To assess the diagnostic accuracy of a smartwatch-based wearable device in detecting OSA, using PSG as the reference standard. A cross-sectional study was conducted involving 55 adults presenting with habitual snoring. Each participant underwent simultaneous overnight PSG and smartwatch-based monitoring. The smartwatch recorded heart rate variability (HRV), blood oxygen saturation (SpO₂), respiratory rate, and sleep duration. Diagnostic accuracy metrics were calculated for OSA. User comfort and satisfaction were also evaluated. The smartwatch demonstrated a sensitivity of 81.8%, specificity of 75.0%, positive predictive value of 78.5%, and negative predictive value of 78.9%, with an overall diagnostic accuracy of 78.0%. The Cohen’s kappa value was 0.65, indicating substantial agreement with PSG. A strong correlation was found between AHI values from PSG and smartwatch (<i>r</i> = 0.71, <i>p</i> &lt; 0.001). Significant physiological differences were noted between OSA-positive and OSA-negative participants across HRV, SpO₂, respiratory rate, and sleep duration (<i>p</i> &lt; 0.05). High levels of user satisfaction (85.5%) and usability (89.1%) were reported. Smartwatch-based wearable devices show good potential for identifying OSA with high user acceptance. They may be valuable tools for preliminary screening in community and low-resource settings.</p>

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Role of Smart Wearable Devices in OSA Detection

  • 期刊论文

  • 发表日期：

    2026年3月30日

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  • M. Karthik,
  • K. Gowthame,
  • R. B. Namasivaya Navin,
  • S. Prabakaran,
  • D. Balaji,
  • R. Muthukumar,
  • S. Rajasekaran,
  • Sarath Kumar

摘要

Obstructive sleep apnea (OSA) is a common sleep disorder associated with cardiovascular and metabolic consequences. Although polysomnography (PSG) is the gold standard for diagnosis, it is costly and resource-intensive, limiting its use in population-wide screening. Smart wearable devices offer a non-invasive and accessible alternative, but their clinical validation remains limited. To assess the diagnostic accuracy of a smartwatch-based wearable device in detecting OSA, using PSG as the reference standard. A cross-sectional study was conducted involving 55 adults presenting with habitual snoring. Each participant underwent simultaneous overnight PSG and smartwatch-based monitoring. The smartwatch recorded heart rate variability (HRV), blood oxygen saturation (SpO₂), respiratory rate, and sleep duration. Diagnostic accuracy metrics were calculated for OSA. User comfort and satisfaction were also evaluated. The smartwatch demonstrated a sensitivity of 81.8%, specificity of 75.0%, positive predictive value of 78.5%, and negative predictive value of 78.9%, with an overall diagnostic accuracy of 78.0%. The Cohen’s kappa value was 0.65, indicating substantial agreement with PSG. A strong correlation was found between AHI values from PSG and smartwatch (r = 0.71, p < 0.001). Significant physiological differences were noted between OSA-positive and OSA-negative participants across HRV, SpO₂, respiratory rate, and sleep duration (p < 0.05). High levels of user satisfaction (85.5%) and usability (89.1%) were reported. Smartwatch-based wearable devices show good potential for identifying OSA with high user acceptance. They may be valuable tools for preliminary screening in community and low-resource settings.