Determinants of sleep quality in adults during the COVID-19 pandemic: COVID-Inconfidentes , a population-based study

This article has been Reviewed by the following groups

Read the full article See related articles

Abstract

Background

The coronavirus disease 2019 (COVID-19) pandemic has had a negative effect on the health and behavior of the world’s population.

Objectives

To evaluate sleep quality and its associated factors in adults during the COVID-19 pandemic in Brazil.

Methods

This is a population-based serological survey of 1762 adults collected from October to December 2020 in the Iron Quadrangle region, Brazil. To measure sleep quality, we used the Pittsburgh Sleep Quality Index questionnaire and socio-demographic, health, health related behaviors, anxiety, vitamin D, weight gain/loss, and pandemic characteristics were assessed using a structured questionnaire. Univariate and multivariate analyses were performed to identify the factors associated with sleep quality.

Results

More than half of the individuals evaluated had poor sleep quality (52.5%). In multivariate analysis, factors related to sleep quality included living alone (OR=2.36; 95%CI: 1.11-5.00), anxiety disorder (OR=2.22; 95%CI: 1.20-4.14), 5.0% weight loss during the pandemic (OR=1.66; 95%CI: 1.01-2.76), weight gain of 5.0% (OR=1.90; 95%CI: 1.08-3.34), insufficient vitamin D scenario (OR=1.47; 95%CI: 1.01-2.12), and symptoms of COVID-19 (OR=1.94; 95%CI: 1.25-3.01).

Conclusions

Our study revealed that more than half of the participants had poor sleep quality during the COVID-19 outbreak, and the factors associated with poor sleep quality were related to the pandemic, such as insufficient vitamin D scenario and weight change.

Article activity feed

  1. SciScore for 10.1101/2021.09.29.21264305: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.


    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).


    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    The main limitations of this study are the variables obtained by self-report, which can lead to underestimation of risk behaviors or overestimation of protective behaviors, due to differences in the perception of each individual about the pandemic and associated factors. However, the assessment of sleep quality needs to be performed subjectively, since it considers intrinsic factors to the individuals’ perception of their sleep. Furthermore, the sample design brings robustness to the study and favors the analysis of the COVID-19 scenario in the two municipalities of the Iron Quadrangle region. Thus, this study allows us to evaluate the relationship between the quality of sleep and factors related to the pandemic, providing subsidies for decision making, in a chaotic socio-sanitary and epidemiological context, to reduce the worsening of health conditions.

    Results from TrialIdentifier: No clinical trial numbers were referenced.


    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.


    Results from JetFighter: We did not find any issues relating to colormaps.


    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.


    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.