Bayesian Covariate-Dependent Circadian Modeling of Rest-Activity Rhythms
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913679481503744 |
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| author | Hadj-Amar, Beniamino Krishnan, Vaishnav Vannucci, Marina |
| author_facet | Hadj-Amar, Beniamino Krishnan, Vaishnav Vannucci, Marina |
| contents | 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 l_1-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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_03273 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bayesian Covariate-Dependent Circadian Modeling of Rest-Activity Rhythms Hadj-Amar, Beniamino Krishnan, Vaishnav Vannucci, Marina Methodology Applications 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 l_1-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. |
| title | Bayesian Covariate-Dependent Circadian Modeling of Rest-Activity Rhythms |
| topic | Methodology Applications |
| url | https://arxiv.org/abs/2502.03273 |