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Sleep and Big Data: harnessing data, technology and analytics for monitoring sleep and improving diagnostics, prediction, and interventions- an era for "Sleep-Omics?"

Over the last 30 years, there have been impressive advances in characterizing the physiological bases of sleep and circadian rhythms and their variation across populations and health outcomes. The remarkable maturation of research in sleep and circadian biology is exemplified by an exponential increase in sleep-focused peer-reviewed publications, and that a recent Nobel prize was awarded for the elucidation of circadian machinery underscores the importance of sleep and circadian science. In clinical sleep medicine, there have been improvements in defining sleep disorders—with evidence-based guidelines for screening, treating and managing sleep apnea, insomnia, restless legs syndrome, narcolepsy, and circadian rhythm and other sleep disorders. Accredited sleep centers—delivering guidelinebased sleep care—have steadily grown, closing the gap between the number of people suffering from a sleep disorder versus those appropriately treated. The recognition of sleep health as a public health priority is reflected by the inclusion of sleep health goals in the Center for Disease Control’s Healthy People 2020 report. Nonetheless, there remain fundamental knowledge gaps. Notably, there is a need to better understand the basic functions of sleep, as well as how to modulate sleep as a novel clinical intervention for improving cognition, mood, and longevity. Critical challenges in optimizing treatment of sleep disorders include the substantive heterogeneity of physiological endotypes, symptoms, comorbidities, treatment responses, prognoses, and genetic risk factors among individuals labeled with the same diagnostic code. As clinicians, we sometimes fail to find treatments that adequately resolve the sleepiness experienced by many patients with sleep apnea, or that improve both the nighttime and daytime symptoms that patients with insomnia experience, and may struggle to identify the causes and effective treatment strategies for patients labeled with “idiopathic hypersomnia.” Our diagnostic tools suffer from an over-reliance on manual and subjectively ascertained quantification of individual “events” during sleep records (apneas and arousals) and overly simplistic approaches to summarizing continuous physiological phenomena into discrete categories (e.g. sleep epochs). Traditional diagnostic approaches—developed using a knowledge base over 50 years old—not only limit the richness of sleep data routinely collected over hours if not days, but also contribute to costs and inefficiencies that could be improved with smarter utilization of more automated and sensitive procedures. Together, these gaps point to a critical need to leverage larger sets of multilevel data more comprehensively and rigorously to better define the complex and dynamic processes underlying sleep and wake states, the dysregulation of which results in sleep-related disorders. A further need is to better define disease subgroups, which ultimately may lead to tailored interventions that meet the goals of precision medicine. Advanced computational and analytical tools open the possibility to interrogate complex physiological and clinical problems at a scale XX