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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Bibliographic Details
Published inData science in science Vol. 4; no. 1
Main Authors Hadj-Amar, Beniamino, Krishnan, Vaishnav, Vannucci, Marina
Format Journal Article
LanguageEnglish
Published Taylor & Francis Group 31.12.2025
Subjects
Anti-logistic Circadian model
l1-ball projection prior
multi-subject modeling
rest-activity rhythms
wereable devices
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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.
ISSN:2694-1899
2694-1899
DOI:10.1080/26941899.2025.2474943