Related Experiment Video
Updated: Jan 16, 2026
Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health
Published on: February 21, 2025
AI‑Enhanced Smartwatch AHI Estimation and AI‑Scored Polysomnography for Obstructive Sleep Apnea: Real‑World
Donghyeok Kim1, Jeong Yup Han2, Hyunjun Jung2
1Department of Otorhinolaryngology-Head and Neck Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Objective:
This study validated the accuracy of an artificial‑intelligence (AI) smartwatch algorithm that directly estimates the apnea-hypopnea index (AHI) by comparing its performance with AI-scored Level 1 polysomnography (PSG) in Korean adults. The model was trained in South‑American cohorts, allowing inter‑ethnic validation.
Methods:
A total of 90 adults underwent simultaneous Level 1 PSG and smartwatch recording. Fifty‑three datasets with ≥ 3 hours of valid watch data were analyzed. AHI values were obtained as follows: expert‑scored PSG (pAHI), AI‑scored PSG (aiAHI), and smartwatch output (eAHI). Agreement was assessed with Spearman correlation, intraclass correlation coefficients, and receiver‑operating‑characteristic curves.
Results:
eAHI correlated strongly with aiAHI (ρ = 0.88, ICC = 0.87) and pAHI (ρ = 0.85, ICC = 0.82). For detecting moderate‑to‑severe OSA (aiAHI ≥ 15 events/h), the smartwatch algorithm yielded 92.3% sensitivity, 92.6% specificity, and 92.5% overall accuracy. Bland-Altman analysis revealed systematic underestimation of actual AHI by the smartwatch, particularly in mild OSA.
Conclusion:
This study demonstrates that the evaluated smartwatch-based AHI estimation algorithm shows high concordance with PSG-derived values, particularly for the detection and classification of moderate to severe OSA. However, it should be noted that this smartwatch algorithm tends to underestimate the AHI of OSA due to limitations in scoring unit and recording duration calculation. These findings support the clinical utility of wearable technology as a practical and scalable tool for early identification and longitudinal monitoring of OSA in real-world environments, while highlighting the need for further optimization to accurately detect mild cases.
