Bayesian Covariate-Dependent Circadian Modeling of Rest-Activity Rhythms
We propose a Bayesian covariate-dependent anti-logistic circadian model for analyzing activity data collected via wrist-worn wearable devices. The proposed approach integrates covariates into the modeling of the amplitude and phase parameters, facilitating cohort-level analysis with enhanced flexibi...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Data Science in Science |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/26941899.2025.2474943 |
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| Summary: | We propose a Bayesian covariate-dependent anti-logistic circadian model for analyzing activity data collected via wrist-worn wearable devices. The proposed approach integrates covariates into the modeling of the amplitude and phase parameters, facilitating cohort-level analysis with enhanced flexibility and interpretability. To promote model sparsity, we employ an [Formula: see text]-ball projection prior, enabling precise control over complexity while identifying significant predictors. We assess performances on simulated data and then apply the method to real-world actigraphy data from people with epilepsy. Our results demonstrate the model’s effectiveness in uncovering complex relationships among demographic, psychological, and medical factors influencing rest-activity rhythms, offering insights for personalized clinical assessments and healthcare interventions. |
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| ISSN: | 2694-1899 |