Summary This study aimed to evaluate changes in sleep during the COVID‐19 outbreak, and used data‐driven approaches to identify distinct profiles of changes in sleep‐related behaviours. Demographic, behavioural and psychological factors associated with sleep changes were also investigated. An online population survey assessing sleep and mental health was distributed between 3 April and 24 June 2020. Retrospective questions were used to estimate temporal changes from before to during the outbreak. In 5,525 Canadian respondents (67.1% females, 16–95 years old: Mean ± SD = 55.6 ± 16.3 years), wake‐up times were significantly delayed relative to pre‐outbreak estimates ( p < .001, = 0.04). Occurrences of clinically meaningful sleep difficulties significantly increased from 36.0% before the outbreak to 50.5% during the outbreak (all p < .001, g ≥ 0.27). Three subgroups with distinct profiles of changes in sleep behaviours were identified: “Reduced Time in Bed”, “Delayed Sleep” and “Extended Time in Bed”. The “Reduced Time in Bed” and “Delayed Sleep” subgroups had more adverse sleep outcomes and psychological changes during the outbreak. The emergence of new sleep difficulties was independently associated with female sex, chronic illnesses, being employed, family responsibilities, earlier wake‐up times, higher stress levels, as well as heavier alcohol use and television exposure. The heterogeneity of sleep changes in response to the pandemic highlights the need for tailored interventions to address sleep problems.

IntroductionThe negative impacts of COVID-19 have rippled through every facet of society. Understanding the multidimensional impacts of this pandemic is crucial to identify the most critical needs and to inform targeted interventions. This population survey study aimed to investigate the acute phase of the COVID-19 outbreak in terms of perceived threats and concerns, occupational and financial impacts, social impacts and stress between 3 April and 15 May 2020.Methods6040 participants are included in this report. A multivariate linear regression model was used to identify factors associated with stress changes (as measured by the Cohen’s Perceived Stress Scale (PSS)) relative to pre-outbreak retrospective estimates.ResultsOn average, PSS scores increased from low stress levels before the outbreak to moderate stress levels during the outbreak (p<0.001). The independent factors associated with stress worsening were: having a mental disorder, female sex, having underage children, heavier alcohol consumption, working with the general public, shorter sleep duration, younger age, less time elapsed since the start of the outbreak, lower stress before the outbreak, worse symptoms that could be linked to COVID-19, lower coping skills, worse obsessive–compulsive symptoms related to germs and contamination, personalities loading on extraversion, conscientiousness and neuroticism, left wing political views, worse family relationships and spending less time exercising and doing artistic activities.ConclusionCross-sectional analyses showed a significant increase from low to moderate stress during the COVID-19 outbreak. Identified modifiable factors associated with increased stress may be informative for intervention development.Trial registration numberNCT04369690; Results.

Background Understanding the multifaceted impacts of the Coronavirus-19 (COVID-19) outbreak as it unfolds is crucial to identify the most critical needs and to inform targeted interventions. Methods This population survey study presents cohort characteristics and baseline observations linked to the acute-mid phase of the COVID-19 outbreak in terms of perceived threats and concerns, occupational and financial impacts, social impacts and stress as measured by the Cohen's Perceived Stress Scale (PSS) collected cross-sectionally between April 3 and May 15, 2020. A multivariate linear regression model was used to identify factors associated with stress changes relative to pre-outbreak estimates. Results 6,040/6,685 (90.4%) participants filled out at least 1/3 of the survey and were included in the analyses. On average, PSS scores increased from 12.9+6.8 before the outbreak to 14.9+8.3 during the outbreak (p<0.001). The independent factors associated with stress worsening were: having a mental disorder, female sex, having underage children, heavier alcohol consumption, working with the general public, shorter sleep duration, younger age, less time elapsed since the start of the outbreak, lower stress before the outbreak, worse symptoms that could be linked to COVID-19, lower coping skills, worse obsessive-compulsive symptoms related to germs and contamination, personalities loading on extraversion, conscientiousness and neuroticism, left-wing political views, worse family relationships, and spending less time exercising and doing artistic activities. Interpretation Cross-sectional analyses showed a significant increase from average low to moderate stress during the COVID-19 outbreak. Identified modifiable factors associated with an increase in stress may be informative for intervention development.

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La modélisation paréquations structurelles avec MPlus (Caron, 2019) est un ouvrage de référence qui, divisé en trois parties, aborde les fonctions de base de Mplus (Partie 1 -Les rudiments), le traitement de données avec Mplus (Partie 2 -Traitement de données), ainsi que l'exécution d'un ensemble d'analyses statistiques impliquant de la modélisation paréquations structurelles en utilisant Mplus (Partie 3 -Les analyses). Reconnaissant l'utilisation croissante de la modélisation paréquations structurelles dans le domaine des sciences sociales, ainsi que le nombre limité de ressourceséducatives disponiblesà ce sujet, nous proposons un guide d'accompagnement pour la troisième partie du livre de Caron (2019), quiétendra son applicationà l'interface de programmation R. R est une interface de programmation libre d'accès et très versatile qui, similairementà Mplus, permet la réalisation d'analyses statistiques impliquant de la modélisation paŕ equations structurelles. L'objectif de cet article est de présenter la traduction en langage de programmation R des syntaxes de la troisième partie du livre de Caron (2019) ainsi que les résultats des sorties associées. Puisque cet article viseà servir de guide complémentaire au livre de Caron (2019), les bases théoriques sous-tendant les analyses statistiques qui y sont couvertes ainsi que l'interprétation des résultats issus de celles-ci ne font pas l'objet du présent article. Cet article couvre les analyses suivantes : la régression logistique, l'analyse de trajectoire, l'analyse factorielle exploratoire et confirmatoire, la médiation, la modération, la médiation modérée, l'analyse de classes latentes, l'analyse de modèles autorégressifs et autorégressifs croisés, l'analyse de trajectoire latente, et l'analyse de groupes multiples.

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