Language
English
Publication Date
1-1-2025
Journal
Data Science in Science
DOI
10.1080/26941899.2025.2474943
PMID
42394892
PMCID
PMC13327678
PubMedCentral® Posted Date
7-3-2026
PubMedCentral® Full Text Version
Author MSS
Abstract
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 l1-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.
Keywords
Anti-logistic Circadian model, rest-activity rhythms, l1-ball projection prior, multi-subject modeling, wereable devices
Published Open-Access
yes
Recommended Citation
Beniamino Hadj-Amar, Vaishnav Krishnan, and Marina Vannucci, "Bayesian Covariate-Dependent Circadian Modeling of Rest-Activity Rhythms" (2025). Faculty, Staff and Students Publications. 7200.
https://digitalcommons.library.tmc.edu/baylor_docs/7200