In-school multidimensional sleep health assessed via BEDReST showed stronger inverse associations with continuous metabolic syndrome scores (B = -0.60, p<0.05) compared to the RU-SATED framework.
Observational (n=341)
Do multidimensional sleep health scores (BEDReST vs RU-SATED) correlate with cardiometabolic markers in adolescents, and does assessment context (in-school vs on-break) modify this association?
The BEDReST multidimensional sleep health framework shows stronger associations with cardiometabolic risk markers in adolescents compared to RU-SATED, particularly when assessed during the school term.
Effect estimate: B = -0.60
p-value: p=<0.05
Abstract Introduction Adolescents experience shifts in circadian timing that conflict with early school start times, often leading to chronic sleep restriction and irregular patterns, which have been linked to adverse cardiometabolic outcomes. Multidimensional sleep health (MSH) frameworks offer a comprehensive approach to evaluating sleep. We previously developed a data-driven, multivariable MSH framework, BEDReST, and in this study, we examined associations between MSH scores and metabolic syndrome (MetS), comparing BEDReST to RU-SATED and evaluating whether assessment context (in-school vs. on-break) modifies these associations. Methods Data from 341 adolescents (16.3±2.2 yr; 47% female; 22.8% racial/ethnic minority) were analyzed. Two MSH scores were examined: RU-SATED and BEDReST (Breathing, Efficiency, Duration, Regularity, Satisfaction, and Timing), measured by polysomnography, actigraphy, and self-report. Both scores ranged from 0 to 6, with higher values indicating better sleep health. Linear regression models examined associations between MSH scores and a continuous metabolic syndrome (cMetS) score, where higher values indicate greater metabolic burden, stratified by assessment timing (in-school vs. on-break). Additional models assessed associations with key cardiometabolic markers, including blood pressure, fasting glucose and insulin, glucose-to-insulin ratio (GIR), and LDL cholesterol. All models were adjusted for age, sex, race/ethnicity, and socioeconomic status. Results MSH assessed while in-school showed stronger inverse associations with cMetS with BEDReST demonstrating larger effects (B = –0.60, p 0.05) than RU-SATED (B = -0.21, p0.05). BEDReST was also associated with lower waist circumference (B = -2.82, p 0.05), while RU-SATED was not (B = -0.84, p0.05). While on-break, both MSH scores were associated with improved insulin sensitivity, though BEDReST consistently showed stronger effects (BEDReST: HOMA-IR B = -0.15, p 0.05; GIR B = 2.71, p=0.05; RU-SATED: HOMA-IR B = -0.11, p 0.05; GIR B = 1.70, p=0.07), whereas associations with all other outcomes were weaker. Conclusion MSH assessed while in-school showed stronger associations with MetS and waist circumference than on-break, with BEDReST consistently demonstrating larger effects than RU-SATED. While on-break, both frameworks captured associations with insulin sensitivity, with BEDReST showing stronger effects. These findings highlight the importance of contextual factors and demonstrate the added value of a multivariable, data-driven approach to assessing MSH in adolescents. Support (if any) R01HL136587, UL1TR002014, TL1TR002016
Nyhuis et al. (2026) conducted an observational in Metabolic syndrome (n=341). BEDReST sleep health framework vs. RU-SATED sleep health framework was evaluated on Continuous metabolic syndrome (cMetS) score (B = -0.60, p=<0.05). In-school multidimensional sleep health assessed via BEDReST showed stronger inverse associations with continuous metabolic syndrome scores (B = -0.60, p<0.05) compared to the RU-SATED framework.