Associations between dimensions of behaviour, personality traits, and mental-health during the COVID-19 pandemic in the United Kingdom

  1. Adam Hampshire
  2. Peter J. Hellyer
  3. Eyal Soreq
  4. Mitul A. Mehta
  5. Konstantinos Ioannidis
  6. William Trender
  7. Jon E. Grant
  8. Samuel R. Chamberlain

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Abstract

The COVID-19 pandemic (including lockdown) is likely to have had profound but diverse implications for mental health and well-being, yet little is known about individual experiences of the pandemic (positive and negative) and how this relates to mental health and well-being, as well as other important contextual variables. Here, we analyse data sampled in a large-scale manner from 379,875 people in the United Kingdom (UK) during 2020 to identify population variables associated with mood and mental health during the COVID-19 pandemic, and to investigate self-perceived pandemic impact in relation to those variables. We report that while there are relatively small population-level differences in mood assessment scores pre- to peak-UK lockdown, the size of the differences is larger for people from specific groups, e.g. older adults and people with lower incomes. Multiple dimensions underlie peoples’ perceptions, both positive and negative, of the pandemic’s impact on daily life. These dimensions explain variance in mental health and can be statistically predicted from age, demographics, home and work circumstances, pre-existing conditions, maladaptive technology use and personality traits (e.g., compulsivity). We conclude that a holistic view, incorporating the broad range of relevant population factors, can better characterise people whose mental health is most at risk during the COVID-19 pandemic.

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  1. Version published to 10.1038/s41467-021-24365-5
  2. Version published to 10.1101/2020.06.18.20134635v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.06.18.20134635: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The study was approved by the Imperial College Research Ethics Committee (17IC4009).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data processing and statistical analysis: Analyses were conducted in MATLAB R2020a.
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)

    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:
    Primary limitations of this study pertain to cross-sectional analysis albeit within the context of a longitudinal study. This is somewhat mitigated by the large scale of data and matching demographics for Pre and Mid pandemic timeframes, and by the rigorous multivariate statistical approaches deployed. We intend to recontact this cohort at 3, 6 and 12 months to plot change in the idiosyncratic impact of the pandemic and its aftermath in vivo19,20 including more detailed assessment of identified vulnerable sub-groups. We also note that the current paper focused on data from people aged 16 and above. It is vital that research also examines younger people using this and related methodologies adapted for that purpose.2

    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.

  4. Version published to 10.1101/2020.06.18.20134635v1 on medRxiv