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

Alessandro Carollo, Andrea Bizzego, Giulio Gabrieli … (2021)

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.

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DOI:: 10.14293/S2199-1006.1.SOR-SOCSCI.AKD3GL.v1.RWMIQY

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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.

ScienceOpen disciplines: Psychology,Clinical Psychology & Psychiatry,Public health

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

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I enjoyed reading the paper and think that this may be an excellent opportunity to present the learning approach and its utility in the field of mental health.

I would suggest the Authors emphasize this uniqueness. This paper is an excellent opportunity to introduce this method and show its advantage compared to the methods usually used in the field.

Nevertheless, for this aim, the machine learning approach must be described profoundly, and all the assumptions and characteristics must be explicated using an appropriate scientific language that may be easily understood.

Line 178, what are the differences between the models used, Random Forest and Support Vector Regressor? Why may it be interesting to study if two different models produce the same results?

Line 186, Please describe the Mean Squared Error. Is there any cutoff or value range that may allow the reader to understand the present study's findings?

Line 194, please describe the parameter C. What does it represent? Is there any cutoff or value range that may allow the reader to understand the present study's findings?

Line 224, Figure 1, please describe the metric used for the importance

Line 259, based on which data it can be said that depression symptoms were the best at predicting lockdown duration in weeks?

Line 102, is that randomized in the order of the questionnaires?

Lines 137 and 163, please, also describe the age range

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 significant statical results found.

What is the utility to having found a U-shape?

May it be interesting to verify the invariance of the results across age or gender?

I think that the sample size for each week in the second wave is too small to allow any comparison also with a non-parametric test.