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0769 Multidimensional Sleep Health in Adolescents from the General Population: Definition, Thresholds and Construct Validity

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The RU-SATED model – regularity, satisfaction, alertness, timing, efficiency, and duration – captures the 24-hour experience of sleep to asses multidimensional sleep health (MSH). However, most prior evidence comes from… Click to show full abstract

The RU-SATED model – regularity, satisfaction, alertness, timing, efficiency, and duration – captures the 24-hour experience of sleep to asses multidimensional sleep health (MSH). However, most prior evidence comes from middle-aged adults. We provide updated MSH data in adolescents by leveraging objective and self-reported sleep measures. We studied 377 adolescents (16.4±2.3 yr; 46.4% female; 21.5% racial/ethnic minority) from the Penn State Child Cohort, a randomly-selected population-based sample. Each MSH domain was categorized as “good” or “poor” using cut-offs informed by prior studies and expert consensus. Good cut-offs, assigned a score of 1, that were derived from actigraphy-measured data included: the standard deviation of sleep midpoint ≤1-h (RU), mean of sleep midpoint 2:00-4:00 (T), mean value of sleep efficiency ≥85% (E), and mean total sleep time ≥7.5-h (D). Good cut-offs derived from self-reported rating scales included the absence of insomnia symptoms (S) or excessive daytime sleepiness (A). Values considered poor based on these cut-offs were assigned a score of 0. Scores were summed across all domains to obtain a composite score ranging from 0 to 6, with higher scores indicating better MSH. Morningness and Tanner staging were self-reported, while Sleep and Arousal clusters scores on the Pediatric Behavior Scale were parent-reported. The mean composite score was 3.03 ± 1.30 and domains A and D were most commonly rated as poor (64.5% and 65.3%, respectively). Younger age (r=-0.13, p< 0.05) and identifying as non-Hispanic white (r=-0.14, p< 0.01) were significantly associated with higher MSH scores, while sex (r=-0.04, p=0.40), Tanner staging (r=-0.06, p=0.29) or BMI percentile (r=-0.07, p=0.15) were not. Greater morningness (r=-0.29, p< 0.01), less disturbed sleep (r=-0.28, p< 0.01) and higher arousal (r=-0.21, p< 0.01) scores were associated with higher MSH scores. Our data-driven approach can be used to assess MSH in the adolescent population. Our definition captures previously identified health disparities in MSH in adults and shows optimal construct validity against self-reports of circadian preference and parent observations of adolescents’ degree of sleep disturbance and arousal. Improving sleep duration and daytime alertness appear to continue to be the most relevant domains impacting overall MSH in adolescents. NIH (R01HL136587,UL1TR000127)

Keywords: health; cut offs; multidimensional sleep; sleep health; population; msh

Journal Title: SLEEP
Year Published: 2023

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