Abstract
Objectives: At the peak of COVID-19, adolescent life was disrupted as schools adapted their instructional approaches such as online, in-person, or hybrid instruction. We and others have previously commented on how these shifts facilitated longer, later and (more developmentally appropriate) sleep. Here, we report how sleep contributed to associations between remote instruction and broader academic well-being (e.g., cognitive function, school connectedness, and stress). Methods: Adolescents from all 50 U.S. states (n = 4068) completed online self-report surveys in fall 2020. Instructional approach was operationalized from fully in-person instruction to fully asynchronous online education. Sleep parameters included sleep timing and duration, sleep disturbances, and sleep-related impairments. Perceived academic well-being was defined as cognitive function, school connectedness, and school-related stress. Sleep and perceived academic well-being are examined across instructional approaches, in their association, and in structural models. Results: Sleep and perceived academic well-being differed between hybrid and online instruction groups. Less variable or disturbed sleep was associated both with in-person instruction, and with positive outcomes in cognitive function, school connectedness, and stress domains. Sleep mediated a substantial portion of variance in perceived academic well-being attributable to instructional approach. Conclusion: These data highlight the need to protect both healthy sleep and in-person instruction. Appropriate sleep timing and duration, fewer sleep disturbances and sleep-related impairments accounted for a substantial degree of variance in the association between remote instruction on academic outcomes. While many students experienced "lost learning" because of COVID-19, this study joins a broader discussion of ensuring developmentally appropriate school-start times to support both sleep and achievement. (c) 2024 National Sleep Foundation. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.