Developing Accessible Sleep EEG Growth Charts for Young People

ABSTRACT Adolescence is a dynamic developmental stage in which health trajectories can pivot, in both negative and positive directions. A comprehensive understanding of normative adolescent development provides an essential template for detecting early deviations from maturational trajectories, along with actionable windows in which intervention can maximally impact health trajectories. In pediatric medicine, height and weight growth charts are used as a reference to detect abnormal development and alert physicians to intervene. While highly scalable, height/weight charts do not capture the important neurodevelopmental changes occurring over adolescence. To be most informative for adolescent risk detection and intervention, we need to map development in modifiable brain-based metrics that change over adolescence. These measures must be assessable at-scale and predict health outcomes. We propose that reference models of sleep physiology over adolescence carry high potential value for early detection of brain-based health risk and subsequent effective intervention. Sleep macro- and micro-architecture undergoes dramatic change over adolescence; plays an active role in sculpting the structural and functional maturation of the brain; is associated with myriad sociocultural, environmental, and sex/pubertal factors; predicts long-term brain, behavior, and health trajectories; and is modifiable with non-invasive biobehavioral intervention. However, a barrier to wide use of sleep physiology for risk assessment is that polysomnography, the gold standard measure, is a costly method with limited scalability. Thus, we must validate accessible methods that measure sleep at home; in doing so, we will reduce barriers to quality health care, particularly for underserved groups. To improve sleep-based risk assessment in adolescents, our goal is to create normative growth charts for sleep physiological features in typically developing young people using two complementary sleep measurement methods: gold-standard polysomnography and a wearable sleep recording device. First, we will examine age trends in sleep physiology in typically developing young people (N~1300, 9- 26yr) with polysomnography data harmonized across multiple existing cohorts (Aim 1). In tandem, we will use the wearable device to collect home sleep recordings in typically developing young people (N=500, 9-26yr). A subset of this sample (N=250 participants) will also complete polysomnography. We will use measurement-in- error modeling to establish a functional relationship between sleep outcomes derived from polysomnography and the wearable device (Aim 2). Then, we will recapitulate normative age trends in sleep physiology with the data obtained from the wearable device and evaluate potential sociocultural, environmental, and sex/pubertal modifiers of sleep physiology age trends (Aim 3). Completion of our aims will provide an essential normative template for the study of adolescent sleep physiology, tools to improve detection of suboptimal sleep in the real- word, and a comprehensive understanding of how multiple mechanisms influence age-associated sleep patterns.