Matrol, Afrina Adlyna Mohamad and Koh, Melanie and Tan, Wei Ping Eddy and Ong, Hang Cheng and Ramanaidu, Letchumy Praba and Saw, Shier Nee (2025) Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health. Jove-Journal of Visualized Experiments (216). e67674. ISSN 1940-087X, DOI https://doi.org/10.3791/67674.
Full text not available from this repository.Abstract
This study aims to validate the accuracy of low-cost fitness smartwatches by comparing their data with gold-standard measurements for cardiovascular and physical activity parameters. The study enrolled 50 subjects, 26 undergoing validation testing for heart rate, blood oxygen saturation (SpO2), and sleep data against polysomnography (PSG). Additionally, 24 subjects participated in the 3-Minute Walk Test (3MWT) and Stairs Climbing (SC), with step counts validated against manual video calculations. Results showed no significant difference between the device's measurements and gold standard values for shallow sleep, deep sleep, REM time, mean heart rate, minimum heart rate, and SpO2. However, the device significantly underestimated manually counted steps (p = 0.009 (3MWT); p = 0.012 (SC)), total sleep duration (p = 0.004), and wake time (p = 8.94 x 10-8) while overestimating maximum heart rate (p = 0.011). These findings highlight the importance of accurate validation and interpretation of wearable device data in clinical contexts. Given these limitations, excluding the device's readings in future analyses is recommended to maintain data reliability and research integrity. This study underscores the need for ongoing validation and improvement of wearable technology to ensure its reliability and effectiveness in healthcare.
| Item Type: | Article |
|---|---|
| Funders: | UM Research Center (IIRG001C-2021IISS) |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > R Medicine (General) |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Artificial Intelligence Faculty of Medicine > Medicine Department |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 22 Apr 2025 08:10 |
| Last Modified: | 22 Apr 2025 08:10 |
| URI: | http://eprints.um.edu.my/id/eprint/47942 |
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