Key result
Bayesian circadian model uncovers complex relationships influencing rest-activity rhythms in epilepsy.
The proposed Bayesian circadian model offers a flexible and interpretable approach for analyzing actigraphy data, potentially aiding personalized clinical assessments.
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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Hadj-Amar et al. (2025) studied Epilepsy. Bayesian covariate-dependent anti-logistic circadian model was evaluated. A proposed Bayesian covariate-dependent anti-logistic circadian model effectively uncovered complex relationships among factors influencing rest-activity rhythms in people with epilepsy.
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