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      Adolescents’ trajectories of depression and anxiety symptoms prior to and during the COVID-19 pandemic and their association with healthy sleep patterns

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          Abstract

          The COVID-19 pandemic has seen a rise in anxiety and depression among adolescents. This study aimed to investigate the longitudinal associations between sleep and mental health among a large sample of Australian adolescents and examine whether healthy sleep patterns were protective of mental health in the context of the COVID-19 pandemic. We used three waves of longitudinal control group data from the Health4Life cluster-randomized trial (N = 2781, baseline M age = 12.6, SD =  0.51; 47% boys and 1.4% ‘prefer not to say’). Latent class growth analyses across the 2 years period identified four trajectories of depressive symptoms: low-stable (64.3%), average-increasing (19.2%), high-decreasing (7.1%), moderate-increasing (9.4%), and three anxiety symptom trajectories: low-stable (74.8%), average-increasing (11.6%), high-decreasing (13.6%). We compared the trajectories on sociodemographic and sleep characteristics. Adolescents in low-risk trajectories were more likely to be boys and to report shorter sleep latency and wake after sleep onset, longer sleep duration, less sleepiness, and earlier chronotype. Where mental health improved or worsened, sleep patterns changed in the same direction. The subgroups analyses uncovered two important findings: (1) the majority of adolescents in the sample maintained good mental health and sleep habits ( low-stable trajectories), (2) adolescents with worsening mental health also reported worsening sleep patterns and vice versa in the improving mental health trajectories. These distinct patterns of sleep and mental health would not be seen using mean-centred statistical approaches.

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          Most cited references49

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          National Sleep Foundation’s sleep time duration recommendations: methodology and results summary

          The objective was to conduct a scientifically rigorous update to the National Sleep Foundation's sleep duration recommendations.
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            An Introduction to Latent Class Growth Analysis and Growth Mixture Modeling

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              Latent Class Analysis: A Guide to Best Practice

              Latent class analysis (LCA) is a statistical procedure used to identify qualitatively different subgroups within populations who often share certain outward characteristics. The assumption underlying LCA is that membership in unobserved groups (or classes) can be explained by patterns of scores across survey questions, assessment indicators, or scales. The application of LCA is an active area of research and continues to evolve. As more researchers begin to apply the approach, detailed information on key considerations in conducting LCA is needed. In the present article, we describe LCA, review key elements to consider when conducting LCA, and provide an example of its application.
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                Author and article information

                Contributors
                serena.bauducco@oru.se
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                10 May 2024
                10 May 2024
                2024
                : 14
                : 10764
                Affiliations
                [1 ]Flinders University, ( https://ror.org/01kpzv902) Adelaide, Australia
                [2 ]Center for Health and Medical Psychology, Örebro University, ( https://ror.org/05kytsw45) Fakultetsgatan 1, 701 82 Örebro, Sweden
                [3 ]The Matilda Centre for Research in Mental Health and Substance Use, The University of Sydney, ( https://ror.org/0384j8v12) Sydney, Australia
                [4 ]GRID grid.1004.5, ISNI 0000 0001 2158 5405, The Woolcock Institute, , Macquarie University, ; Sydney, Australia
                [5 ]WINK Sleep Pty Ltd, Adelaide, Australia
                [6 ]Sleep Cycle AB, Gothenburg, Sweden
                Author information
                http://orcid.org/0000-0002-1485-8564
                Article
                60974
                10.1038/s41598-024-60974-y
                11087504
                38730014
                acbc4877-9ee0-49e0-953c-a44814159763
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 2 August 2023
                : 29 April 2024
                Funding
                Funded by: Örebro University
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                risk factors,depression,anxiety
                Uncategorized
                risk factors, depression, anxiety

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