Background The popularity of sleep-tracking wearables has surged worldwide. Yet, there are significant gaps in understanding the real-life implications of this phenomenon. While wearables may offer insights about sleep and promote sleep health awareness, evidence remains mixed on whether they lead to improved sleep outcomes or fuel sleep anxiety. Objective This study aims to (1) determine the prevalence and sociodemographic predictors of using sleep wearables in Canada, (2) evaluate the perceived effects of wearable use on sleep and stress, (3) compare sleep and health care–seeking behaviors in users and nonusers, and (4) investigate the moderating effects of wearable use on the association between sleep and anxiety. Methods An online survey investigating sleep and mental health was distributed to a representative sample of 1200 Canadians. The survey included questions on demographics, wearables use, sleep patterns, health care–seeking behaviors, insomnia (ISI-3 [Insomnia Severity Index-3]), and anxiety (GAD-7 [Generalized Anxiety Disorder-7]) symptoms. Analyses relied on descriptive statistics and logistic regression (aims 1 and 2), multivariate analyses of covariance and chi-squared analyses (aim 3), and multiple regression (aim 4). Results Among the 1200 respondents (n=636, 53% female; aged 16 to 88 years), 19.3% (n=231) reported using a wearable device to monitor sleep. Several sociodemographic variables were associated with an increased likelihood of using wearables including: youth, being retired, being part of a racialized minority group, earning a higher income, having greater health care coverage, having a sleep disorder, and having a mental disorder (χ214=110.2, P<.001). Of all wearable users, nearly 45% felt that using sleep wearables had a positive effect on their sleep (n=102) and stress levels (n=97), while 4.5% (n=10) noted a negative effect. Compared to nonusers, wearable users reported 13 minutes longer sleep onset latency (F1,1151=5.21, P=.02, ƞp2=0.005), slept about 1 hour less (F1,1143=31.60, P<.001, ƞp2=0.027), and endorsed more severe insomnia symptoms (F1,1119=4.04, P<.05, ƞp2=0.004). After adjusting for the presence of sleep disorders, only the differences in sleep duration remained. The proportion of wearable users was almost twice as high in those having informed a health care provider about sleep difficulties (χ22=35.4, P<.001) and in those having used sleep medications (χ23=38.7, P<.001). Wearable use was identified as a moderator of the effect of anxiety symptoms on sleep duration, with wearable users showing a steeper decline in total sleep time as anxiety increased compared to nonusers (F1,1165=17.5, P<.001). Conclusions One in 5 Canadians acknowledged having used sleep wearables. Predictors include younger age, higher income, and having a sleep or mental disorder. Although many individuals reported positive effects of sleep wearables, wearables use strengthened the link between short sleep and anxiety. Expanding our understanding of the factors associated with beneficial versus detrimental use of sleep wearables may help support more informed applications.

Structural Equation Modeling with MPlus (Caron, 2019) is a reference book that, divided into three parts, discusses the basic functions of Mplus (Part 1 - The Basics), processing data with Mplus (Part 2 - Data Processing), and performing a set of statistical analyses involving structural equation modeling using Mplus (Part 3 - The Analyses). Recognizing the increasing use of structural equation modeling in the social sciences, as well as the limited number of educational resources available on this topic, we propose a companion guide to the third part of Caron's (2019) book, which will extend its application to the R programming interface. R is a freely available and very versatile programming interface that, similar to Mplus, allows for statistical analysis involving structural equation modeling. The purpose of this paper is to present the translation of the syntaxes from the third part of Caron's (2019) book into the R programming language, as well as the associated output results. Because this article is intended to serve as a companion guide to Caron's (2019) book, the theoretical underpinnings underlying the statistical analyses covered therein as well as the interpretation of the results from them are not the focus of this article. This article covers the following analyses: logistic regression, path analysis, exploratory and confirmatory factor analysis, mediation, moderation, moderate mediation, latent class analysis, autoregressive and cross-autoregressive model analysis, latent path analysis, and multiple group analysis.

Structural Equation Modeling with MPlus (Caron, 2019) is a reference book that, divided into three parts, discusses the basic functions of Mplus (Part 1 The Basics), processing data with Mplus (Part 2 Data Processing), and performing a set of statistical analyses involving structural equation modeling using Mplus (Part 3 The Analyses). Recognizing the increasing use of structural equation modeling in the social sciences, as well as the limited number of educational resources available on this topic, we propose a companion guide to the third part of Caron's (2019) book, which will extend its application to the R programming interface. R is a free and very versatile programming interface that, like Mplus, allows statistical analysis involving structural equation modeling. The objective of this paper is to present the translation of the syntaxes from the third part of Caron's (2019) book into the R programming language along with the associated output results. Because this article is intended to serve as a companion guide to Caron's (2019) book, the theoretical underpinnings underlying the statistical analyses covered therein as well as the interpretation of the results from them are not the focus of this article. This article covers the following analyses: logistic regression, path analysis, exploratory and confirmatory factor analysis, mediation, moderation, moderate mediation, latent class analysis, autoregressive and cross-autoregressive model analysis, latent path analysis, and multiple group analysis.

Sleep problems are highly co-morbid with psychiatric disorders and are part of the complex and multiple factors contributing to symptoms and functional disability. The current study aimed to determine how sleep problems in the period preceding psychiatric admission relate to profiles of mental health needs in adolescent inpatients. This retrospective study included 424 adolescents (13-17 years) admitted over a five-year period to an acute crisis stabilization unit in a tertiary care pediatric hospital. Adolescents were divided into two age- and sex-matched groups based on the presence of moderate to severe sleep problems. Profiles of mental health needs were assessed at admission using the Child and Adolescent Needs and Strengths - Mental Health Acute (CANS-MH) and a complexity score was calculated as the total number of actionable CANS-MH items. Results showed a positive association between sleep problems and needs pertaining to eating disturbances, adjustment to trauma, and school attendance. Odds ratios for sleep problems increased progressively as the complexity scores increased, reaching a plateau at six needs beyond which odds ratios remained at their highest level. Adolescents with sleep problems were more likely to undergo medication changes during psychiatric hospitalization and were more likely to be discharged with antipsychotic medication. These findings suggest that sleep difficulties in adolescent inpatients may be associated with distinct and more complex profiles of mental health needs. The evaluation of sleep problems early in the course of psychiatric hospitalization may be an important part of the psychiatric assessment process to inform the global treatment plan.

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, ηp2 = 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.

La modélisation par équations 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 par équations structurelles en utilisant Mplus (Partie 3 -- Les analyses). Reconnaissant l'utilisation croissante de la modélisation par équations structurelles dans le domaine des sciences sociales, ainsi que le nombre limité de ressources éducatives disponibles à ce sujet, nous proposons un guide d'accompagnement pour la troisième partie du livre de Caron (2019), qui étendra son application à l'interface de programmation R. R est une interface de programmation libre d'accès et très versatile qui, similairement à Mplus, permet la réalisation d'analyses statistiques impliquant de la modélisation par équations 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 vise à 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.

Introduction The 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.Methods 6040 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.Results On 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.Conclusion Cross-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 number NCT04369690; 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. Findings: 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.Trial Registration: ClinicalTrials.gov (NCT04369690)Funding Statement: None.Declaration of Interests: All authors declare that no competing interests exist.Ethics Approval Statement: This study was approved by the Clinical Trials Ontario - Qualified Research Ethics Board via the Ottawa Health Science Network (Protocol #2131).