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

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            Abstract

            COVID-19 studies to date have documented some of the initial health consequences of lockdown restrictions adopted by many countries. Combining a data-driven machine learning paradigm and a statistical analysis approach, our previous paper documented a U-shape 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. Specifically, we tested a) for the dependence of the chosen model by adopting a new one - namely, support vector regressor (SVR). Furthermore, b) whether the patterns of self-perceived loneliness found in data from the first UK national lockdown could be generalizable to the second wave of the UK lockdown (17 October 2020 to 31 January 2021). The first part of the study involved training an SVR model on the 75% of the UK dataset from wave 1 (n total = 435). This SVR model was then tested on the remaining 25% of data (MSE training = 2.04; MSE test = 2.29), which resulted in depressive symptoms to be the most important variable - followed by self-perceived loneliness. Statistical analysis of depressive symptoms by week of lockdown resulted in a significant U-shape pattern between week 3 to 7 of lockdown. In the second part of the study, data from wave 2 of the UK lockdown (n = 263) was used to conduct a graphical and statistical inspection of the week-by-week distribution of scores regarding self-perceived loneliness. Despite a graphical U-shaped pattern between week 3 and 9 of lockdown, levels of loneliness were not between weeks of lockdown. Consistent with past studies, study findings suggest that self-perceived loneliness and depressive symptoms may be two of the most relevant symptoms to address when imposing lockdown restrictions.

            Content

            Author and article information

            Journal
            UCL Open: Environment Preprint
            UCL Press
            8 October 2021
            Affiliations
            [1 ] Department of Psychology and Cognitive Science, University of Trento, Italy
            [2 ] School of Social Sciences, Nanyang Technological University, Singapore
            [3 ] Department of Psychology and Human Development, University College London, London, UK
            [4 ] Departments of Criminology, Psychiatry, and Psychology, University of Pennsylvania
            [5 ] Department of Psychology and Cognitive Science, University of Trento, Italy; School of Social Sciences, Nanyang Technological University, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
            Author notes
            Article
            10.14324/111.444/000095.v1
            0769d88b-e572-48eb-9a71-23ea1d32cecf

            This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY) 4.0 https://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.

            Funding
            UCL Global Engagement Fund 563920.100.177785

            The datasets generated during and/or analysed during the current study are available in the repository: http://www.doi.org/10.5522/04/16583861

            Psychology, Clinical Psychology & Psychiatry, Public health

            COVID-19, depression, lockdown, loneliness, global study, machine learning, SARS-CoV-2, Health

            Comments

            Date: 11 February 2022

            Handling Editor: Dr Matthew O. Gribble

            Editorial decision: Request revision. The Handling Editor requested revisions; the article has been returned to the authors to make this revision. Teh Editor notes the following to be addressed:

            • What is the connection between this work and the primary themes of this environmental journal? Please be sure to clearly and properly situate the work within Planetary Health, GeoHealth, One Health, or another environmental health paradigm to contextualize your research on loneliness and depression as connected to an environmental challenge. This journal has primarily environmental audiences.
            • Several of the reviewers raised concerns about methodological rigor, in particular regarding the data analysis approach. The investigators should better justify their approach or, if they agree with the reviewer that the assumptions of their approach are inconsistent with the design of the study, use an alternative approach. The investigators do not necessarily need to adopt the specific statistical test recommended by the reviewer, as there are other options that would also be appropriate, but the choice of data analysis approach should be clearly presented with the key assumptions of that approach and why that is suitable for the study design, data structure, and scientific hypothesis (or parameter) of interest.
            • It may be difficult for the authors to respond to the sample size concerns raised regarding the approach chosen for data analysis; some discussion of what the implications of this sample size limitation may be for results and overall study conclusions would be appropriate. Sensitivity analyses to assess robustness of findings to model assumption violations, interpolation over sparse data, etc. are always welcome.
            2022-02-11 15:33 UTC
            +1

            Date: 12 October 2021

            Handling Editor: Prof Dan Osborn and Dr Matthew O. Gribble

            This article is a preprint article and has not been peer-reviewed. It is under consideration following submission to UCL Open: Environment for open peer review.

            2021-10-13 08:38 UTC
            +1

            Date: 12 October 2021

            Handling Editor: Prof Dan Osborn

            This article is a preprint article and has not been peer-reviewed. It is under consideration following submission to UCL Open: Environment for open peer review.

            2021-10-12 15:19 UTC
            +1

            Date: 12 October 2021

            Handling Editor: Prof Dan Osborn

            This article is a preprint article and has not been peer-reviewed. It is under consideration following submission to UCL Open: Environment for open peer review.

            2021-10-12 15:19 UTC
            +1

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