Abstract
The pattern of sleep stages across a night (sleep architecture) is influenced by biological, behavioral, and clinical variables. However, traditional measures of sleep architecture such as stage proportions, fail to capture sleep dynamics. Here we quantify the impact of individual differences on the dynamics of sleep architecture and determine which factors or set of factors best predict the next sleep stage from current stage information. We investigated the influence of age, sex, body mass index, time of day, and sleep time on static (e.g. minutes in stage, sleep efficiency) and dynamic measures of sleep architecture (e.g. transition probabilities and stage duration distributions) using a large dataset of 3202 nights from a non-clinical population. Multi-level regressions show that sex effects duration of all Non-Rapid Eye Movement (NREM) stages, and age has a curvilinear relationship for Wake After Sleep Onset (WASO) and slow wave sleep (SWS) minutes. Bayesian network modeling reveals sleep architecture depends on time of day, total sleep time, age and sex, but not BMI. Older adults, and particularly males, have shorter bouts (more fragmentation) of Stage 2, SWS, and they transition less frequently to these stages. Additionally, we showed that the next sleep stage and its duration can be optimally predicted by the prior 2 stages and age. Our results demonstrate the potential benefit of big data and Bayesian network approaches in quantifying static and dynamic architecture of normal sleep.
Highlights
Sleep is a dynamic, multi-dimensional process that reflects lifespan developmental changes in physical and mental health, as well as day-to-day state fluctuations
Using multi-level regression models, we predicted sleep efficiency, minutes in stage, and Rapid Eye Movement (REM) latency in a stepwise procedure from age, sex, and higher order terms while controlling for total sleep time (TST), and sleep onset time. While these analysis fail to capture dynamic trends, they remain useful as global summary statistics and for comparison with previous literature[6]
Regression parameters for the final best fitting models are reported in S1 Table, and each relationship is shown graphically in Fig 2
Summary
Multi-dimensional process that reflects lifespan developmental changes in physical and mental health, as well as day-to-day state fluctuations. Alterations in stage patterns and durations (i.e., sleep architecture), are seen in insomnia[1], narcolepsy[2], sleep apnea[3] as well as depression[4] and schizophrenia[5]. Not all deviations from prototypical sleep are indicators of pathology; individual factors such as age[6], Body Mass Index (BMI)[7], and sex[8] contribute to sleep architecture, and differences are reported after. Big data analysis of sleep architecture and the effects of individual differences
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
