+1 Recommend
    • Review: found
    Is Open Access

    Review of 'Self-Perceived Loneliness and Depression During the COVID-19 Pandemic: a Two-Wave Replication Study'

    Self-Perceived Loneliness and Depression During the COVID-19 Pandemic: a Two-Wave Replication StudyCrossref
    Average rating:
        Rated 5 of 5.
    Level of importance:
        Rated 5 of 5.
    Level of validity:
        Rated 5 of 5.
    Level of completeness:
        Rated 5 of 5.
    Level of comprehensibility:
        Rated 5 of 5.
    Competing interests:

    Reviewed article

    • Record: found
    • Abstract: found
    • Article: found
    Is Open Access

    Self-Perceived Loneliness and Depression During the COVID-19 Pandemic: a Two-Wave Replication Study

    \hl{\textbf{Background:} The global} COVID-19 \hl{pandemic has forced countries to impose strict} lockdown restrictions \hl{and mandatory stay-at-home orders with varying impacts on individual's health}. Combining a data-driven machine learning paradigm and a statistical approach, our previous paper documented a U-shaped pattern in levels of self-perceived loneliness in both the UK and Greek populations during the first lockdown (17 April to 17 July 2020). The current paper aimed to test the robustness of these results by focusing on data from the first and second lockdown waves in the UK. Methods:We tested a) the impact ofthe chosen model on the identification of the most time-sensitive variable in the period spent in lockdown. Twonew machine learning models- namely, support vector regressor (SVR) and multiple linear regressor (MLR) were adopted to identify the most time-sensitive variable inthe UK dataset from wave 1 (n = 435). In the second part of the study, we tested b) whether the pattern of self-perceived loneliness found in the first UK national lockdown was generalizable to the second wave of UK lockdown (17 October 2020 to 31 January 2021). To do so,data from wave 2 of the UK lockdown (n = 263) was used to conduct a graphical inspection of the week-by-week distribution of self-perceived loneliness scores. Results:In both SVR and MLR models,depressive symptoms resultedto be the most time-sensitivevariable during the lockdown period. Statistical analysis of depressive symptoms by week of lockdown resulted in a U-shapedpattern between week 3 to 7 of wave 1 of the UK nationallockdown. Furthermore,despite the sample size by week in wave 2 was too small for having a meaningful statistical insight, a graphical U-shaped distributionbetween week 3 and 9 of lockdown was observed. Conclusions:Consistent with past studies, these preliminary resultssuggest that self-perceived loneliness and depressive symptoms may be two of the most relevant symptoms to address when imposing lockdown restrictions.

      Review information

      This work has been published open access under Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com.

      Psychology,Clinical Psychology & Psychiatry,Public health
      loneliness,machine learning,COVID-19,global study,SARS-CoV-2,lockdown,Health,depression

      Review text

      Just one comment:


      [R3.8] Line 196, please justify using the non-parametric statistical test or any other tests that will be used and compute the effect size for any significantly statistical results.

      Why was used a non-parametric and not a parametric test? Please also clarify in the paper. Thanks.


      Comment on this review