DOI
https://doi.org/10.25772/3ddf-c979
Author ORCID Identifier
https://orcid.org/0009-0003-6660-0391
Defense Date
2026
Document Type
Dissertation
Degree Name
Doctor of Philosophy
Department
Biostatistics
First Advisor
Shanshan Chen
Second Advisor
Nitai Mukhopadhyay
Third Advisor
Dipankar Bandyopadhyay
Fourth Advisor
Brian Berman
Fifth Advisor
Maha Alattar
Abstract
Actigraphy and polysomnography (PSG) represent complementary but largely disconnected modalities for measuring sleep. Actigraphy enables intensive longitudinal data collection at scale but lacks physiological detail, while PSG provides gold-standard sleep architecture characterization at the cost of logistical feasibility in large studies. This dissertation develops a unified statistical pipeline for extracting and analyzing night activity segments from actigraphy data and establishing their quantitative connections to PSG-derived sleep metrics. First, we model actigraphy under the Zero-Augmented Gamma distribution to account for its zero-inflated, right-skewed structure. Second, we introduce Pi-Change, a prior-informed multiple change point detection algorithm that incorporates circadian rhythm information via a time-varying penalty. Third, we develop a hypothesis test framework for detecting segmentation errors in actigraphy data. Fourth, we propose a novel marked point process model for night activity segments that accommodates inter-device variation in actigraphy. Finally, we apply a pipeline composed of the previous methods to the Multi-Ethnic Study of Atherosclerosis Sleep dataset, using multi-state models and Bayesian mixed-effects models to characterize associations between actigraphy-derived parameters and PSG sleep stage metrics, demographic covariates, and subjective sleep quality. Results indicate that the point process parameters representing nocturnal activity onset and duration are associated with sleep onset latency, time spent in the wake after sleep onset stage of sleep, total sleep time, and sleep efficiency. Further, these parameters are associated with age, sex, race/ethnicity, and job schedule. Together, these contributions provide a principled, scalable framework for bridging wearable and gold-standard sleep measurement, with implications for population-scale sleep research and clinical monitoring.
Rights
© The Author
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
8-7-2026