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Characterizing the sleep-wake cycle in adolescents is an importantprerequisite to better understand the association of abnormal sleep patternswith subsequent clinical and behavioral outcomes. The aim of this research wasto develop hidden Markov models (HMM) that incorporate both objective(actigraphy) and subjective (sleep log) measures to estimate the sleep-wakecycle using data from the NEXT longitudinal study, a large population-basedcohort study. The model was estimated with a negative binomial distribution forthe activity counts (1-minute epochs) to account for overdispersion relative toa Poisson process. Furthermore, self-reported measures were dichotomized (foreach one-minute interval) and subject to misclassification. We assumed that theunobserved sleep-wake cycle follows a two-state Markov chain with transitionalprobabilities varying according to a circadian rhythm. Maximum-likelihoodestimation using a backward-forward algorithm was applied to fit thelongitudinal data on a subject by subject basis. The algorithm was used toreconstruct the sleep-wake cycle from sequences of self-reported sleep andactivity data. Furthermore, we conduct simulations to examine the properties ofthis approach under different observational patterns including both completeand partially observed measurements on each individual.

A hidden Markov modeling approach combining objective measure of  activity and subjective measure of self-reported sleep to estimate the  sleep-wake cycle