Sleep Disturbances in Frontline Health Care Workers During the COVID-19 Pandemic: Social Media Survey Study

  1. Nancy H Stewart
  2. Anya Koza
  3. Serena Dhaon
  4. Christiana Shoushtari
  5. Maylyn Martinez
  6. Vineet M Arora

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Abstract

During the COVID-19 pandemic, health care workers are sharing their challenges, including sleep disturbances, on social media; however, no study has evaluated sleep in predominantly US frontline health care workers during the COVID-19 pandemic.

Objective

The aim of this study was to assess sleep among a sample of predominantly US frontline health care workers during the COVID-19 pandemic using validated measures through a survey distributed on social media.

Methods

A self-selection survey was distributed on Facebook, Twitter, and Instagram for 16 days (August 31 to September 15, 2020), targeting health care workers who were clinically active during the COVID-19 pandemic. Study participants completed the Pittsburgh Sleep Quality Index (PSQI) and Insomnia Severity Index (ISI), and they reported their demographic and career information. Poor sleep quality was defined as a PSQI score ≥5. Moderate-to-severe insomnia was defined as an ISI score >14. The Mini-Z Burnout Survey was used to measure burnout. Multivariate logistic regression tested associations between demographics, career characteristics, and sleep outcomes.

Results

A total of 963 surveys were completed. Participants were predominantly White (894/963, 92.8%), female (707/963, 73.4%), aged 30-49 years (692/963, 71.9%), and physicians (620/963, 64.4%). Mean sleep duration was 6.1 hours (SD 1.2). Nearly 96% (920/963, 95.5%) of participants reported poor sleep (PSQI). One-third (288/963, 30%) reported moderate or severe insomnia. Many participants (554/910, 60.9%) experienced sleep disruptions due to device use or had nightmares at least once per week (420/929, 45.2%). Over 50% (525/932, 56.3%) reported burnout. In multivariable logistic regressions, nonphysician (odds ratio [OR] 2.4, 95% CI 1.7-3.4), caring for patients with COVID-19 (OR 1.8, 95% CI 1.2-2.8), Hispanic ethnicity (OR 2.2, 95% CI 1.4-3.5), female sex (OR 1.6, 95% CI 1.1-2.4), and having a sleep disorder (OR 4.3, 95% CI 2.7-6.9) were associated with increased odds of insomnia. In open-ended comments (n=310), poor sleep was mapped to four categories: children and family, work demands, personal health, and pandemic-related sleep disturbances.

Conclusions

During the COVID-19 pandemic, nearly all the frontline health care workers surveyed on social media reported poor sleep, over one-third reported insomnia, and over half reported burnout. Many also reported sleep disruptions due to device use and nightmares. Sleep interventions for frontline health care workers are urgently needed.

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  1. Version published to 10.2196/27331
  2. ScreenIT

    SciScore for 10.1101/2021.01.19.21250128: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The University of Chicago Institutional Review Board reviewed and approved the protocol.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Survey Design: Study data were collected and managed using REDCap (Research Electronic Data Capture) hosted at the University of Chicago.
    REDCap
    suggested: (REDCap, RRID:SCR_003445)
    College Station TX: StataCorp LLC with P< 0.05 used to indicate statistical significance.
    StataCorp
    suggested: (Stata, RRID:SCR_012763)

    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:
    Although social media platforms are widely utilized in 2020, a limitation of this study may be that recruitment through social media platforms may not be a representative sample of HCW in the U.S. Because the focus of our survey was on sleep, it is possible that HCW who were more likely to have a sleep disturbance completed the survey. Given the prevalence of sleep disorders in the general U.S. population of 8 to 19%,32,33 our sample does not appear to over-represent those with sleep disorders. There were not enough non-physician HCW in our sample to characterize the sleep quality for non-physician workers (e.g., physician’s assistant, nurse practitioner, nurse, pharmacist, allied health workers). We also asked subjects to complete the survey questions by reflecting on a time when they experienced the greatest clinical intensity and risk of COVID-19 transmission; this may have led to inconsistencies in our data. All data were self-reported, and we do not have objective measures of sleep duration and quality in this sample. We also do not have longitudinal data on our sample, limiting our ability to draw longitudinal conclusions. Furthermore, when utilizing social media data, it is difficult to capture the accurate outreach and link clicks; e.g., if an individual shares the link but not our original post, we cannot calculate the outreach and metrics. Therefore, the actual metrics may be higher than those we were capable of measuring.

    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.

    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.

  3. Version published to 10.2196/preprints.27331
  4. Version published to 10.1101/2021.01.19.21250128v1 on medRxiv