A Doubly-HDPHMM Framework to Study Heterogeneous Population and Individual Rest-Activity Behaviors

Music City Center 

The growing availability of wearable device-monitored actigraphy data (human activity movements) has driven the development of advanced statistical models to quantify human rest-activity behaviors. Key features from 24-hour actigraphy data, used as digital biomarkers, are linked to metabolic and neurodegenerative diseases. Hidden Markov models (HMM) have recently been applied to actigraphy data as an effective framework for modeling individual rest-activity patterns. We propose a Doubly Hierarchical Dirichlet Process HMM (Doubly HDPHMM) framework that (1) infers the number of hidden activity states for both individuals and the study population using HDP priors, eliminating the assumption of a fixed number of states that may not suit all subpopulations, and (2) allows flexible incorporation of covariates such as health outcomes in state-specific distributions, enabling simultaneous individual and population-level inference. Using NHANES 2011-2014 actigraphy data, our model distinguishes sleep, sedentary, and physically active behaviors, revealing nuanced within- and between-individual variations and offering insights into complex and heterogeneous rest-activity patterns.

24-Hour Actigraphy Data

Hidden Markov Models

Nonparametric Bayesian

Rest-Activity Behaviors 

Section on Bayesian Statistical Science