COVID-19 and mental health of individuals with different personalities

  1. Eugenio Proto
  2. Anwen Zhang

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Several studies have been devoted to establishing the effects of the COVID-19 pandemic on mental health across gender, age, and ethnicity. However, much less attention has been paid to the differential effect of COVID-19 according to different personalities. We do this using the UK Household Longitudinal Study (UKHLS), a large-scale panel survey representative of the UK population. The UKHLS allows us to assess the mental health of the same respondent before and during the COVID-19 period based on their “Big Five” personality traits and cognitive skills. We find that during the COVID-19 period, individuals who have more extravert and open personality traits report a higher mental health deterioration, while those scoring higher in agreeableness are less affected. The effect of openness is particularly strong: One more SD predicts up to 0.23 more symptoms of mental health deterioration in the 12-item General Health Questionnaire (GHQ-12) test during the COVID-19 period. In particular, for females, cognitive skills and openness are strong predictors of mental health deterioration, while for non-British White respondents, these predictors are extraversion and openness. Neuroticism strongly predicts worse mental health cross-sectionally, but it does not lead to significantly stronger deterioration during the pandemic. The study’s results are robust to the inclusion of potential confounding variables such as changes in physical health, household income, and job status (like unemployed or furloughed).

Article activity feed

  1. Version published to 10.1073/pnas.2109282118
  2. ScreenIT

    SciScore for 10.1101/2021.05.24.21257581: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.

  3. Version published to 10.1101/2021.05.24.21257581v1 on medRxiv