A Hidden Markov Modeling Approach Combining Objective Measure of Activity and Subjective Measure of Self-Reported Sleep to Estimate the Sleep-Wake Cycle

• Activity Number: 190 - Novel Approaches for Assessment of Health Outcomes and Multi-Cohort Data Integration Using Wearable Devices in Large-Scale Biomedical Studies
• Type: Topic Contributed
• Date/Time: Monday, August 8, 2022 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Semhar Ogbagaber* and Yifan Cui and Kaigang Li and Ronald Iannotti and Paul S. Albert
• Companies: Bristol Myers Squib and National University of Singapore and Colorado School of Public Health and The CDM Group, Bethesda and National Cancer Institute
• Keywords: wearable technology; hidden Markov model; actigraphy; sleep-wake cycle; physical activity

Abstract

Characterizing the sleep-wake cycle in adolescents is an important prerequisite to better understand the association of abnormal sleep patterns with subsequent clinical and behavioral outcomes. The aim of this research was to develop hidden Markov models (HMM) that incorporate both objective (actigraphy) and subjective (sleep log) measures to estimate the sleep-wake cycle using data from the NEXT longitudinal study, a large population-based cohort study. The model was estimated with a negative binomial distribution for the activity counts, self-reported measures were dichotomized (for each one-minute interval) and subject to misclassification. We assumed that the unobserved sleep-wake cycle follows a two-state Markov chain with transitional probabilities varying according to a circadian rhythm. Maximum-likelihood estimation using a backward-forward algorithm was applied. The algorithm was used to reconstruct the sleep-wake cycle from sequences of self-reported sleep and activity data. Furthermore, we conduct simulations to examine the properties of this approach under different observational patterns including using both and partially observed measurements on each individual.