Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Nov 27, 2024
Date Accepted: May 15, 2025
Detection and Analysis of Circadian Biomarkers for Metabolic Syndrome Using Wearable Data: Cross-Sectional Study
ABSTRACT
Background:
Digital biomarker detection using wearable devices is utilized as a solution for managing metabolic syndrome (MetS) in daily life.
Objective:
This study aimed to detect digital biomarkers based on sleep and circadian rhythm using step counts and heart rate data obtained from wearable devices and to analyze important biomarkers for MetS risk groups.
Methods:
Circadian rhythm markers based on heart rate and step counts were analyzed, including the newly proposed continuous wavelet circadian rhythm energy. The detected markers were analyzed for their contribution to MetS using SHapley Additive exPlanations, Explainable Boosting Machine, and Tabular Neural Network models.
Results:
Analysis of sleep and circadian rhythm biomarkers using statistical analysis and artificial intelligence methods revealed that circadian rhythm markers were more important for MetS than sleep markers. Among circadian rhythm markers, heart rate-based markers were found to be more important than step counts. The continuous wavelet circadian energy proposed in this study showed the highest contribution to MetS in the artificial intelligence analysis results.
Conclusions:
The risk of MetS can be continuously identified and monitored in daily life by utilizing circadian rhythm markers.
Citation
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Copyright
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