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Record W4407049928

Social Jetlag Changes During the COVID-19 Pandemic as a Predictor of Insomnia – A Multi-National Survey Study

2021· article· en· W4407049928 on OpenAlexaboutno aff
Brandão LEM, Teemu Martikainen, Ilona Merikanto, Brigitte Holzinger, Morin Cm, Elsi Cá, Bolstad CJ, Damien Léger, Frances Chung, Giuseppe Plazzi, Yves Dauvilliers, Kentaro Matsui, Luigi De Gennaro, Mariuz Sieminski, Nejamkis Mr, Chan NY, Yun Kwok Wing, Moa Sa, Yuichi Inoue, Markku Partinen, Christian Benedict, Bjørn Bjorvatn, Jonathan Cedernaes

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2021
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PandemicInsomnia2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PsychologyVirologyMedicinePsychiatryInternal medicineOutbreakDiseaseInfectious disease (medical specialty)
DOInot available

Abstract

fetched live from OpenAlex

Luiz Eduardo Mateus Brandão,1 Teemu Martikainen,1 Ilona Merikanto,2– 4 Brigitte Holzinger,5,6 Charles M Morin,7– 9 Colin A Espie,10,11 Courtney J Bolstad,12 Damien Leger,13 Frances Chung,14 Giuseppe Plazzi,15 Yves Dauvilliers,16 Kentaro Matsui,17,18 Luigi De Gennaro,19– 21 Mariusz Sieminski,22 Michael R Nadorff,12,23 Ngan Yin Chan,24 Yun Kwok Wing,24 Sérgio Arthuro Mota-Rolim,25– 27 Yuichi Inoue,28 Markku Partinen,29,30 Christian Benedict,31 Bjorn Bjorvatn,32,33 Jonathan Cedernaes1,34 1Department of Medical Sciences, Uppsala University, Uppsala, Sweden; 2Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, Finland; 3Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland; 4Orton Orthopaedic Hospital, Helsinki, Finland; 5ZK-Schlafcoaching, Medical University Vienna, Vienna, Austria; 6Institute for Consciousness and Dream Research, Vienna, Austria; 7École de Psychologie, Université Laval, Québec, Canada; 8Centre d’étude des troubles du sommeil, Université Laval, Québec, Canada; 9Centre de recherche CERVO/Brain Research Center, Université Laval, Québec, Canada; 10Sleep & Circadian Institute, University of Oxford, Oxford, UK; 11Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; 12Department of Psychology, Mississippi State University, Starkville, MS, USA; 13APHP, VIFASOM, Hôtel-Dieu, Centre du Sommeil et de la Vigilance, Université de Paris, Paris, France; 14Department of Anesthesia and Pain Medicine, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada; 15Department of Biomedical, Metabolic and Neural Science, University of Modena and Reggio Emilia, Modena, Italy; 16Sleep-Wake Disorders Center, Department of Neurology, Gui-de-Chauliac Hospital, Institute for Neurosciences of Montpellier INM, INSERM, University of Montpellier, Montpellier, France; 17National Center of Neurology and Psychiatry National Institute of Mental Health, Department of Clinical Laboratory and Department of Sleep-Wake Disorders, Tokyo, Japan; 18Tokyo Women’s Medical University, Department of Psychiatry, Tokyo, Japan; 19Department of Psychology, Sapienza University of Rome, Rome, Italy; 20IRCCS Fondazione Santa Lucia, Rome, Italy; 21IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy; 22Department of Emergency Medicine, Medical University of Gdansk, Gdansk, Poland; 23Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA; 24Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, People’s Republic of China; 25Brain Institute, Federal University of Rio Grande do Norte, Natal, RN, Brazil; 26Physiology and Behaviour Department, Federal University of Rio Grande do Norte, Natal, RN, Brazil; 27Onofre Lopes University Hospital, Federal University of Rio Grande do Norte, Natal, RN, Brazil; 28Department of Somnology, Tokyo Medical University, Tokyo, Japan; 29Helsinki Sleep Clinic, Vitalmed Research Center, Terveystalo Biobank and Research, Helsinki, Finland; 30Department of Neurosciences, Clinicum, University of Helsinki, Helsinki, Finland; 31Sleep Science Laboratory, Department of Neuroscience, Uppsala University, Uppsala, Sweden; 32Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway; 33Norwegian Competence Center for Sleep Disorders, Haukeland University Hospital, Bergen, Norway; 34Department of Medicine, Division of Endocrinology, Metabolism, and Molecular Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USACorrespondence: Jonathan Cedernaes Email jonathan.cedernaes@medsci.uu.sePurpose: Lifestyle and work habits have been drastically altered by restrictions due to the COVID-19 pandemic. Whether the associated changes in sleep timing modulate the risk of suffering from symptoms of insomnia, the most prevalent sleep disorder, is however incompletely understood. Here, we evaluate the association between the early pandemic-associated change in 1) the magnitude of social jetlag (SJL) – ie, the difference between sleep timing on working vs free days – and 2) symptoms of insomnia.Patients and Methods: A total of 14,968 anonymous participants (mean age: 40 years; 64% females) responded to a standardized internet-based survey distributed across 14 countries. Using logistic multivariate regression, we examined the association between the degree of social jetlag and symptoms of insomnia, controlling for important confounders like social restriction extension, country specific COVID-19 severity and psychological distress, for example.Results: In response to the pandemic, participants reported later sleep timing, especially during workdays. Most participants (46%) exhibited a reduction in their SJL, whereas 20% increased it; and 34% reported no change in SJL. Notably, we found that both increased and decreased SJL, as a result of the COVID-19 pandemic, were associated with later sleep midpoint (indicating a later chronotype) as well as more recurrent and moderate-to-severe symptoms of insomnia (about 23– 54% higher odds ratio than subjects with unchanged SJL). Primarily those with reduced SJL shifted their bedtimes to a later timepoint, compared with those without changes in SJL.Conclusion: Our findings offer important insights into how self-reported changes to the stability of sleep/wake timing, as reflected by changes in SJL, can be a critical marker of the risk of experiencing insomnia-related symptoms – even when individuals manage to reduce their social jetlag. These findings emphasize the clinical importance of analyzing sleep-wake regularity.Keywords: BNSQ, ISI, midsleep, MSFsc, PHQ-4, psychological distress

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.646
GPT teacher head0.671
Teacher spread0.025 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2021
Admission routes1
Has abstractyes

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