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

    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.

      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


      The manuscript faces a topical issue regarding the interrelations between mental health assessment and the lockdown duration. The gathered data are of great interest and constitute a strength point of the study. In addition, the application of machine learning techniques   in such a context represents an added value. However, many methodological problems considerably mitigate my enthusiasm.   My major concerns regard the poor readability of the study and the choice to model as outcome variable the lockdown duration. The poor readability is mainly due to lack of specifications, descriptions and details that make the analyses hard to reproduce. A paramount purpose of a scientific paper should be the reliability and the reproducibility of the results through a detailed description of the applied models and methods (it should be very useful to add -maybe- in supplementary materials the code or pseudo-code used for the analysis). The second concern regards the SVR approach and in particular the choice to predict the lockdown duration. It is not clear the rationale for which the mental health variables should predict the lockdown duration! It will be reasonable to assess the reverse relation, i.e. the relation between the duration of lockdown (as predictor of) and mental health variables (as outcomes/dependent variables).


      Other major and minor comments and suggestions are reported below.




      Abstract is quite difficult to read. It is the first part on which a reader focus his/her attention so it has to  convey clearly the main information. Please try to re-edit the abstract with well separated subsections of background, methods, results and conclusions

      • lines 18-19: Please clarify here that this study exclude Greek sample
      • line 19: aim a) is not clear, dependence on...what? Please specify
      • line 27: the most important variable in... predicting…what? please clarify



      • lines 74-65. From a statistical point of view,  the sentence "found a statistically significant U-shaped pattern" does not make sense without further specifications. Did the authors test the U-shape with a kolmogorov test for distributions?
      • Lines 81-83. Aim a) seems a validation of a previous applied method and as such, It should have been done in the previous paper. Paramount purposes of a scientific paper are the reliability and robustness of results (i.e. results should be robust in terms of methodological approach or model used). If the Authors in this study will find  different results from their previous one, they are denying themselves. Please explain better or provide a justification for this controversial aim.
      • Line 86-87: please change the sentence “unique opportunity”. Actually, every researcher should be able to replicate a previous study, this is a prerogative of a scientific paper!



      Table 1

      • to improve readability and interpretability of the assessment scale, please provide the range for each of them in table 1
      • it is not clear why some instrument have Cronbach'a alpha value and other not. Please explain. Moreover, the use of Cronbach'a alpha (for evaluating the internal consistency) should be described in somewhere in the Data  analysis section.


      Participants section

      • line 127-132 should be part of a methodological/data analysis section and not of participants section
      • lines 135-138: the sentences reported in these lines allude the presence in table 2 of demographic features, please re-edit (same holds for lines 160-163)



      Data Analysis section

      • line 169: Different model with respect to...which one?
      • line 169: data-driven not data-drive
      • Line 170: most influential in...what? maybe influential in explaining (or for) Self-Perceived Loneliness and Depression I guess.  Please explain.
      • Lines 179-185. Paramount targets of a scientific paper are the readability and (mainly) the reproducibility. The authors should provide all the necessary details and explanation to: i) easily understand the purpose and the results of each applied methods, and ii) to replicate the analyses. Please explain: 1) which is the final purpose of the SVR, 2) the choice of 10x15 values for the cross-validation, 3)  the choice of splitting in 75% vs 25%  for training and test set. Moreover, nowhere is specified the output/dependent  variable of SVR.
      • lines 189-192 are unclear, please explain.
      • Lines 196-202. Kruskal-wallis test is the corresponding non-parametric test of ANOVA test for comparing independent samples. Here the Authors declare to compare variable changes over time, i.e. to compare correlated data (?) If so, the Kruskal-wallis is not the right test to use. If the Authors want to compare same variable, evaluated on same sample, across time they have to use the Friedman test. Differently, if the Authors want to compare independent sample, this should be better explained.



      -lines 221-222. I am sorry, but I can see a clear U-shape in figure2. Please explain.



      -line 254. The reader has to reach the discussion section to know which is the outcome variable under investigation with SVR: the lockdown duration. Moreover, is not clear the rationale for which the mental health variables should predict the lockdown duration. It would be reasonable to assess the reverse relation, i.e. the relation between the duration of lockdown (as predictor of) and mental health variables (as outcomes/dependent variables). In fact, seems to me that the true intention of the Authors to assess the reverse relation is revealed by the statement in lines 304-306. It is worth to note this point that appears crucial. The nature of variables cannot be ignored. The lockdown duration cannot be a random variable since it is measured without error and it is the same for all subjects involved in the survey. Conversely, the mental health variables are random variables because they vary among subjects.  In light of this, the whole paper medialisation should be rethought by considering the mental health variables as the main outcomes (the target variables) in relation with/affected by lockdown duration.







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