Research article

Self-perceived loneliness and depression during the Covid-19 pandemic: a two-wave replication study

Authors
  • Alessandro Carollo orcid logo (Department of Psychology and Cognitive Science, University of Trento, Rovereto, Italy)
  • Andrea Bizzego orcid logo (Department of Psychology and Cognitive Science, University of Trento, Rovereto, Italy)
  • Giulio Gabrieli orcid logo (School of Social Sciences, Nanyang Technological University, Singapore, Singapore)
  • Keri Ka-Yee Wong orcid logo (Department of Psychology and Human Development, University College London, London, UK)
  • Adrian Raine orcid logo (Departments of Criminology, Psychiatry, and Psychology, University of Pennsylvania, Philadelphia, PA, USA)
  • Gianluca Esposito orcid logo (Department of Psychology and Cognitive Science, University of Trento, Rovereto, Italy)

Abstract

The global Covid-19 pandemic has forced countries to impose strict lockdown restrictions 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. We tested a) the impact of the chosen model on the identification of the most time-sensitive variable in the period spent in lockdown. Two new machine learning models – namely, support vector regressor (SVR) and multiple linear regressor (MLR) were adopted to identify the most time-sensitive variable in the 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 generalisable to the second wave of the 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. In both SVR and MLR models, depressive symptoms resulted to be the most time-sensitive variable during the lockdown period. Statistical analysis of depressive symptoms by week of lockdown resulted in a U-shaped pattern between weeks 3 and 7 of Wave 1 of the UK national lockdown. Furthermore, although the sample size by week in Wave 2 was too small to have a meaningful statistical insight, a graphical U-shaped distribution between weeks 3 and 9 of lockdown was observed. Consistent with past studies, these preliminary results suggest that self-perceived loneliness and depressive symptoms may be two of the most relevant symptoms to address when imposing lockdown restrictions.

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

How to Cite: Carollo, A., Bizzego, A., Gabrieli, G., Wong, K. K.-Y., Raine, A., & Esposito, G. (2022). Self-perceived loneliness and depression during the Covid-19 pandemic: a two-wave replication study. UCL Open Environment, 4. https://doi.org/10.14324/111.444/ucloe.000051

Rights: © 2022 The Authors.

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Published on
03 Nov 2022
Peer Reviewed

Introduction

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel and highly pathogenic coronavirus that originated in bats and was hosted by pangolins before the spillover to humans [14]. SARS-CoV-2 disease was first documented in the Hubei province of China in December 2019 and has since rapidly spread throughout the world with the World Health Organisation declaring it a pandemic on 11th March 2020 [5]. As of September 2021, over 224 million people have been infected by Covid-19 and more than 4.6 million deaths have been reported globally [6].

With no available vaccine to prevent Covid-19, many countries were initially forced to adopt lockdown restrictions, which greatly impacted the environments in which people were legally allowed to work, play and socialise – all in the effort to slow down the spread of the invisible virus. Across countries, restrictions varied in period, length and strictness – but all mandates resulted in reduced physical contact between humans in environments that people are used to experiencing. In particular, the UK’s first lockdown announced on 23rd March 2020 imposed a ‘must-stay-home’ order [7], forcing many individuals to renegotiate the home environment as simultaneously also a place of play, learning, rest and socialising. Leaving the house was allowed only once a day for essentials only such as shopping, exercising, medical needs, caring duties and essential travel for work [8]. These restrictions were accompanied by physical distancing measures, which were aimed at reducing the person-to-person transmission of the virus by encouraging the population to stay at least 2 m away from others [9]. Although these policies were effective at reducing the number of new cases and the spread of the airborne virus, individuals had to endure long periods of social isolation, reduced activity in confined indoor spaces, scepticism towards others and little to no contact with others (e.g., friends, parents, siblings, partners), which may have had short- and longer-term impacts on their health.

Considering the impact of social isolation on people’s physical and mental health [1013], we hypothesised that lockdown measures, specifically lockdown duration (in days), may impact several important aspects of an individual’s daily life. Globally, studies have documented links between restrictions and poorer mental health, such as more post-traumatic stress symptoms, anxiety, depression, insomnia and trust in others [1418]. Similarly, in a previous data-driven study, we identified that, by using a machine learning model, self-perceived loneliness was most impacted by the time spent in lockdown, over and above other mental health indicators [19]. Further statistical analyses were conducted to assess the variations in participants’ levels of self-perceived loneliness as a function of time spent in lockdown (in weeks). Specifically, participants from the UK who took part in the study during week 6 of the national lockdown reported significantly lower levels of self-perceived loneliness compared to their counterparts who completed the survey during week 3 of the lockdown. Likewise, lower levels of self-perceived loneliness were observed for participants who completed the survey in weeks 4 and 6 of the Greek national lockdown. This pattern of results together with a graphical inspection suggested the existence of a U-shaped distribution in self-perceived loneliness levels by weeks in lockdown in both the UK and Greece. An effect of restrictions on an individual’s perceived loneliness during the first lockdown period was replicated and substantiated by other Covid-19 studies in the literature [2023].

Building on previous findings, the current study aims to replicate and extend on the previous results. In particular, the current study consists of two parts. In the first part, the work aims to test whether the identification of the most time-sensitive variable by Carollo et al. [19] depended on the chosen machine learning model. To do so, we applied two new machine learning models on the same set of UK data from the first lockdown period to identify the most time-sensitive variable. In this way, we wanted to verify if, when changing the predictive model, new variables with different patterns of time-sensitivity could be identified and studied under a statistical approach. This would provide insight into other time-sensitive variables that might have been overlooked by the previously adopted model – namely, the RandomForest model. In the second part, the study aims to test whether the documented distribution of self-perceived loneliness levels by week in lockdown depended on the specific wave of lockdown. To do so, we graphically analysed self-perceived loneliness distribution by week on data from the second UK national lockdown, with data collected from the UCL–Penn Global COVID Study between 17th October 2020 and 31st January 2021 [24]. The current study provides the opportunity to uncover other aspects that may be significantly influenced by the lockdown restrictions in both the first and second waves of lockdown.

Methods

Questionnaire

The current study is based on survey data from the UCL–Penn Global COVID Study, a 12-month study of Covid-19’s impact on mental health in adults conducted between 17th April 2020 and 31st July 2021 [24]. Specifically, this study will use data from Wave 1 collected between 17th April 2020 and 10th July 2020, and data from Wave 2 collected between 17th October 2020 and 31st January 2021. Briefly, the survey was available in eight languages and anyone 18 years and above with access to the survey link through several social media channels (www.GlobalCOVIDStudy.com, email, LinkedIn, WhatsApp, Instagram, Facebook and Reddit) was able to take part in the study. Participants received a randomised presentation of 13 standardised questionnaires assessing mental health including self-perceived loneliness, anxiety, depression, aggression, physical health, social relationships (empathy), living conditions and background variables. For this study, 12 indices derived from the previous questionnaires were included in the analytic sample (see Table 1). As an index of internal reliability, Cronbach’s alpha was computed over the scores based on multiple items.

Table 1.

Variables that are computed to quantify participants’ mental and physical health and living environment during lockdown

Score Description Reference Domain Cronbach’s alpha (CI 95%) Observed range
Mild Activity Difference Difference between days of mild physical activity post- and pre-Covid-19 lockdown International Physical Activity Questionnaire – Short Form (IPAQ-SF, 6-items) [25] Physical Activity Not applicable [−7, 6]
Mild Activity Time Difference Difference between minutes of mild physical activity post- and pre-Covid-19 lockdown International Physical Activity Questionnaire – Short Form (IPAQ-SF, 6-items) [25] Physical Activity Not applicable [−480, 510]
Moderate Activity Difference Difference between days of moderate physical activity post- and pre-Covid-19 lockdown International Physical Activity Questionnaire – Short Form (IPAQ-SF, 6-items) [25] Physical Activity Not applicable [−6, 7]
Sleep Quality Self-reported sleep quality and quantity, where higher scores reflect better sleep quality Pittsburgh Sleep Quality Index (2-items) [26], Epworth Sleepiness Scale [27], Subjective and Objective Sleepiness Scale [28] Sleep Quality 0.73 (0.70–0.77) [7, 23]
Empathy Self-reported affective, cognitive, and somatic empathy, where higher scores reflect higher empathy Cognitive, Affective, Somatic Empathy Scale (CASES, 30-items) [29] Empathy 0.87 (0.85–0.88) [29, 60]
Anxiety Higher scores reflect higher anxiety General Anxiety Disorder-7 (GAD-7) [30] Anxiety 0.89 (0.88–0.91) [0, 20]
Depression Higher scores reflect higher depression Patient Health Questionnaire-9 (PHQ-9, 9-items) [31] Depression 0.87 (0.86–0.89) [0, 22]
Perceived Loneliness Higher scores reflect higher perceived loneliness Loneliness Questionnaire (LQ, 20-items) [32] Perceived Loneliness 0.94 (0.93–0.95) [23, 71]
Living Conditions/Environment Higher scores reflect more chaotic home environments Chaos, Hubbub, and Order Scale and Health Risk Behaviours (CHAOS, 6-items) [33] Demographic Information 0.66 (0.62–0.67) [6, 24]
Beliefs Perceived effectiveness of government guidelines on social distancing, schools closing, face masks and gloves as protection. Higher scores reflect stronger beliefs Summed 9-items on Covid-19 beliefs Worries and Beliefs 0.81 (0.78–0.83) [19, 45]
Schizotypal Traits Higher scores reflect more schizotypal traits Schizotypal Personality Questionnaire–Brief [34] Social Suspicions and Schizotypal Traits 0.73 (0.70–0.77) [0, 19]
Reactive-Proactive Aggression Higher score reflects more aggression Reactive-Proactive Aggression Questionnaire [35] Aggression 0.86 (0.84–0.87) [0, 21]

    Cronbach’s alpha was computed on multiple-item scores and it refers to the scores collected during the first wave of lockdown.

This study received ethical approval from the University College London Institute of Education Research Ethics Committee (REC 1331; April 2020).

Participants

Participants from the first wave of lockdown

During the first period of lockdown, a total of 2276 adults from 66 different countries participated in the study. We excluded participants who: i) dissented to take part (n = 32), had incomplete (n = 712) or missing data (n = 165); ii) did not complete the survey within 2 days from the start date (n = 76); iii) filled in the survey from a country that was different from their original country of residence (n = 132). Criterion ii) was applied to exclude possible confounds in the amount of time passed from the start to the end of survey completion. This was a particularly key point in the data processing procedure as we were interested in the effects that the amount of time in lockdown had on people’s mental and physical health. Similarly, criterion iii) was applied to exclude confounds of different types of lockdown restrictions that were adopted by the various countries of the world. All of these participants were excluded from the final analysis.

In contrast to Carollo et al. [19], the current study examined UK participants only. After also excluding the participants who completed the survey after week 9 of lockdown (n = 40), the analytic sample (N = 435) had the following demographic features: female = 345 (79.31%), male = 81 (18.62%), non-binary = 4 (0.92%), prefer not to say = 2 (0.46%), self-identified = 3 (0.69%); age: range = 18–88 years, mean = 37.62, standard deviation (SD) = 13.83 (missing = 1).

Participants from the second wave of lockdown

With regard to the second wave of lockdown, 2280 participants completed the survey. The same exclusion criteria described in the section above were applied to Wave 2 data. Thus, 1341 and 140 participants were excluded because they had incomplete and missing data, respectively. Another 206 were excluded because they did not complete the survey within 2 days. Finally, 43 did not fill in the survey from their original country of residence and, therefore, were excluded from the analysis.

To be consistent with the sample used in our previous study, the statistical analysis applied to uncover the pattern of self-perceived loneliness in Wave 2 was conducted uniquely on the UK participants (n = 263). The sample had the following demographic features: female = 216 (82.13%), male = 39 (14.83%), non-binary = 5 (1.90%), prefer not to say = 2 (0.76%), self-identified = 1 (0.38%); age: range = 18–89 years, mean = 38.28, SD = 13.74 (missing = 2).

Data analysis

All the scripts for the data analysis are available at the following link: https://doi.org/10.5522/04/20183858. Prior to data analysis, we computed the variable ‘Weeks in lockdown’ for each participant in both Wave 1 and Wave 2 of the UK national lockdown. The variable ‘Weeks in lockdown’ corresponds to the difference between the date in which the UK adopted lockdown preventive measures (either the beginning of the first or the second lockdown wave) and the survey completion date. This new numerical variable referred to the week of lockdown into which the single participant completed the survey. Table 2 reports the number of participants by week across the first and second waves of the UK national lockdown.

Table 2.

Number of participants from the UK by week during the first and second period of lockdown

Wave of lockdown Before week 3 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9 After week 9 TOT
Wave 1 0 42 100 80 76 110 23 4 0 435
Wave 2 244 5 2 3 1 0 0 4 4 263

Using data from Waves 1 and 2 of the UCL–Penn Global COVID Study and the same health variables across both time-points, we conducted two sets of analyses to answer our research questions. To test whether the identification of the most time-sensitive variable in Carollo et al. [19] depended on the chosen machine learning model, we used Wave 1 data and we adopted a data-driven machine learning approach. As compared to the RandomForest model adopted in Carollo et al. [19], in the current work we used two different machine learning models to identify the most time-sensitive variable (out of the 12 indices included). The distribution of scores by week of the identified most time-sensitive variable was then examined through a statistical approach with significance tests corrected for multiple comparisons.

To test whether the U-shaped pattern of self-perceived loneliness found in Carollo et al. [19] was unique to Wave 1 of the lockdown, we used Wave 2 data to conduct a graphical inspection of the distribution of scores by week in lockdown.

Data-driven and statistical replication of the results in Wave 1

The current paper first adopted a machine learning approach to test whether the identification of the most time-sensitive variable in Carollo et al. [19] was specific to the RandomForest model or whether we would replicate the result using new models – namely, support vector regressor (SVR) [36] and multiple linear regressor (MLR). While RandomForest’s predictions are based on the creation of an ensemble of decision trees from the input variables, SVR is rooted in the derivation of a best-fit hyperplane and MLR on linear relations between variables. Data from 12 variables of interest (outlined in Table 1) were included in the models to predict the independent variable ‘Weeks in lockdown’. The assumption behind this approach was that the independent variable ‘Weeks in lockdown’ would modulate, to a different extent, the scores of the dependent variables included in the dataset. Particularly, the most time-sensitive variable would be strongly modulated by time in lockdown and its scores would systematically co-vary with the variable ‘Weeks in lockdown’. Therefore, the most time-sensitive variable would also be the most informative and important for the model when trying to predict ‘Weeks in lockdown’. Under these assumptions, first, we applied a standardised 10 × 5-fold cross-validation scheme to train the SVR and the MLR on 75% of the data. Once the models were established, we then applied them to the remaining 25% of data, the ‘testing set’ data. The cross-validation and the train-test split procedures are common practice in machine learning as they help to control the model’s overfitting by evaluating the model’s performances on unseen data [37]. Overall, the models’ accuracy was assessed by comparing real and predicted values. In particular, the models’ performances were evaluated by mean squared error (MSE), which consists of the average squared difference between predicted and real values. Thus, a lower MSE value corresponds to a higher overlap between the real and predicted data. For every training iteration, the variables were ranked by their absolute coefficient value to reflect their influence on the model’s built. On all the training importance rankings, we computed a Borda count to determine the most important and informative variable for the model’s prediction of the weeks in lockdown. The Borda count is a method to derive a single list summarising the information coming from a set of lists [38]. For the SVR model, by comparing the several training evaluation iterations, we derived the optimal hyper-parameter C. In SVR, the parameter C is a cost regularisation parameter which determines the trade-off cost between minimising the training error and minimising model complexity [39]. The resulting optimised C parameter was equal to the value of 0.01, and it was implemented in the final model. The final models (i.e., SVR with C parameter set at 0.01 and the MLR) were then trained by using all the data from the training set and their performances were evaluated on the testing set data.

Next, focusing on the most time-sensitive variable identified with the SVR and MLR models, we applied a multipair Kruskal–Wallis test to assess whether the variable scores changed over the lockdown period. The Kruskal–Wallis test represents the non-parametric counterpart of analysis of variance. The Kruskal–Wallis test was chosen because it requires fewer assumptions to be conducted as compared to its parametric counterpart [40]. In this study, scores from participants belonging to weeks 3 (since at the beginning of the data collection, the UK lockdown was already started) to 7 were compared. As the study had a cross-sectional design across waves of lockdown, participants were grouped by the ‘Week in lockdown’ variable. ‘Week in lockdown’ groups were compared in terms of scores reported for the identified most time-sensitive variable. In this way, a significant result in the multipair Kruskal–Wallis test would indicate that levels of the identified variable significantly differed by ‘Weeks in lockdown’ for at least two groups of weeks. If the multipair Kruskal–Wallis test suggested the existence of significant weekly variations, we conducted multiple pairwise Kruskal–Wallis tests with a Bonferroni correction to compare week 7 scores to other weeks. Eta (η 2)-squared was computed to estimate the magnitude of significant results [41,42].

Graphical replication of the results in Wave 2

To test whether the distribution of weekly self-perceived loneliness levels was unique to Wave 1 of lockdown, a graphical qualitative inspection was conducted on Wave 2 data. Again, participant’s self-perceived loneliness scores were clustered by week of lockdown and the distribution of scores from weeks 3 to 9 was inspected with boxplots. It is worth noting that, considering the limited sample size that was available for Wave 2 from weeks 3 to 9, no statistically meaningful insight could be derived from the comparisons of groups, so the second part of the study can only have a qualitative and descriptive significance, and must be considered as a preliminary approach.

Results

Replication of the results in Wave 1

MSEs for the SVR performances were 2.04 and 2.29 for the training and test data, respectively. For the MLR, MSEs were 1.97 and 2.39 for the training and test data, respectively. While both models’ performances on the training set are slightly worse than in Carollo et al. [19], the performances on the test are in line with the previous paper. Furthermore, depression scores were found to be the most informative for both the SVR and MLR’s training, above and beyond the other variables in the models (see Fig. 1).

Figure 1
Figure 1

Normalised average importance of the selected variables when training a SVR model (on the left) and a MLR (on the right) on data from the first lockdown period. The importance of the variables was derived from the trained predictive models as the absolute value of the variables’ weights or coefficients for the SVR and MLR, respectively.

A closer look at boxplots representing depressive symptoms divided by week in lockdown suggests that, from weeks 3 to 7, the median score decreased in the first period (week 3 to week 4) and then increased again (from week 4 to week 7; see Fig. 2). A decrease followed by an increase in scores suggests a U-shaped pattern for depressive symptoms in the first wave of the UK lockdown.

Figure 2
Figure 2

Symptoms of Depression reported by week during the first UK national lockdown.

A Kruskal–Wallis test confirmed that at least 1 week (in the period from the 3rd to the 7th week of lockdown) differed significantly from the others in terms of depressive symptoms (H = 22.03, P < 0.001, η 2 = 0.042). Specifically, symptoms between week 4 and week 7 (H = 22.52, P < 0.001, η 2 = 0.050), and between week 5 and week 7 (H = 9.69, P = 0.002, η 2 = 0.020) were statistically different. Conversely, the comparisons between week 3 to week 7 (H = 4.64, P = 0.031), and week 6 to week 7 (H = 4.02, P = 0.045) were not significant after applying the Bonferroni bias-correction.

Qualitative replication of the results in Wave 2

A graphical inspection of boxplots with self-perceived loneliness scores divided by week suggests that, between weeks 3 and 9 of Wave 2 of the UK national lockdown, another U-shaped pattern could be reported. Specifically, participants who took part at the study during the 4th and 5th weeks of lockdown reported lower levels of self-perceived loneliness than did participants in the survey during week 3. Although there were not enough participants for weeks 6, 7 and 8, self-perceived loneliness scores during week 9 were reportedly higher again (see Fig. 3).

Figure 3
Figure 3

Reports of Perceived Loneliness by week during the second UK national lockdown.

Discussion

This study applying a machine learning approach alongside a statistical approach to data from Waves 1 (17 April to 31 July 2020) and 2 (17 October 2020 to 31 January 2021) of the UCL–Penn Global COVID Study [24] identifies the mental health variable(s) most influential in predicting the UK lockdown duration, and how the variable varies by week. This gives an indication of how people were fairing when confined in the limited, often shared, space in which they have to work, learn, play and rest. With the aim of replicating and extending the results from our previous paper [19], we applied a support vector regressor (SVR) model and a multiple linear regressor (MLR) model instead of a RandomForest model to predict participants’ weeks in lockdown. Based on the variables’ importance ranking, depressive symptoms, over and above the other 11 health indices, were the most important variable for both the SVR and MLR models when determining the model best-fit to the data and were the best at predicting lockdown duration in weeks. Depressive symptoms were therefore identified by both the SVR and MLR models as the most time-sensitive variable in the dataset. As the focus of the study was not to assess the variables’ predictive capability per se, it is worth noting that the low model performance did not affect the reliability of the variable importance ranking and, therefore, the identification of the most time-sensitive variable in the dataset [19]. Specifically, depressive symptoms reported across the 9 lockdown weeks resulted in a U-shaped pattern where symptoms were lowest during weeks 4 and 5 compared to week 7.

Variation in the population’s depressive symptoms during lockdown has been reported by past studies as depressive symptoms have been a key mental health issue during the Covid-19 pandemic [4346]. Specifically, Ammar et al. [47] compared the scores pre- and post-lockdown in symptoms of depression and found higher depressive symptoms as a result of home confinement. Notably, this study relied on self-report ratings of depression from participants internationally (e.g., Asia, Europe and Africa), thus further substantiating the reliability of our finding. This is not surprising, given that social isolation is a common precursor of poorer mental and physical health [48], with increased risk for depression [4951]. In another study by Delmastro et al. [52] of the lockdown in Italy, people living alone, or not being allowed to leave the house to go to work, tended to have higher depressive symptoms. Like self-perceived loneliness, symptoms of depression have varied during the first UK lockdown. Self-report data from the United States during their first 3 months of lockdown also showed that self-perceived loneliness was positively correlated with depression and suicide ideation at various time-points [53]. In fact, during the Covid-19 pandemic, self-perceived loneliness – a discrepancy between desired and perceived social connection – seemed to be one of the most important risk factors for depression (and anxiety) [54], and social trust [18]. Specifically, higher perceived social support during lockdown – in other words, lower self-perceived loneliness – was associated with lower depressive symptoms [55]. After such periods, instead, self-perceived loneliness appeared to act as a moderator between stress and depression [56].

While the limited sample size by week in Wave 2 data did not allow the statistical approach adopted in Carollo et al. [19] to be used, a graphical U-shaped pattern of self-perceived levels of loneliness seems to emerge again across the lockdown weeks. Again, qualitatively, the self-perceived levels of loneliness were low during weeks 4 and 5, and highest during the 3rd and 9th weeks of the lockdown period. These results have to be considered only as a qualitative and preliminary insight, as the sample size collected for the weeks of interest did not allow any meaningful statistical inference to be made. In fact, graphical disparities among scores might be mere random variation and they might not reflect real differences. Nonetheless, our study findings suggest that local and nationwide initiatives to help reduce self-perceived loneliness and increase solidarity and community cohesion may be helpful at improving people’s mental health during lockdowns.

In conclusion, both self-perceived loneliness and depressive symptoms appear to follow U-shaped curves across periods of lockdown (although no statistical test was computed over scores of self-perceived loneliness by week in the second wave of the UK lockdown). Knowing the unfolding of these trajectories might be helpful for conveying adequate support to the population in lockdown with the right timing. People might also be made aware of the possible fluctuations in self-perceived loneliness and depressive symptoms throughout the lockdown period. Overall, this knowledge can help manage expectations in populations and support systems to ensure that resources are allocated effectively, especially in future lockdown environments. Of course, ‘why’ both perceived levels of loneliness and depression follow U-shaped patterns will necessarily involve the examination of individual-level characteristics (e.g., age, gender), or other variables, that were not assessed and explored in the current study. For the same aim, a longitudinal investigation – opposed to the cross-sectional design of the current study – could also provide useful results. Furthermore, to fully pursue the replication aims of the current study, it would be useful to apply the same machine learning and statistical approach across different data sources. As we did not find any dataset similar enough to the one we adopted, the results from the current paper can only be considered as preliminary. Although these are limitations, the present study also has some clear strengths. First of all, a wide range of mental and physical variables could be studied in a data-driven fashion thanks to the adopted machine learning approach. In this way, we were able to identify and, in a second phase, statistically characterise the index that varied the most accordingly to the time spent in lockdown. Moreover, given the differences across lockdown restrictions, cross-cultural comparisons of the impacts of Covid-19 on populations are challenging. Thus, a strength of the current study is to focus just on the UK. Generally, the study highlighted the importance of considering the potential weekly variation in mental health across a wide range of variables and the variation that may exist across individuals and countries with different lockdown restrictions.

Author contributions

Conceptualisation: AB, GG, KKW, GE; data curation: AC, AB, GG, KKW; data analysis, data interpretation, writing: AC, AB; revision: AC, AB, GG, KKW, AR, GE; supervision: GE. All authors read and agreed to the published version of the manuscript.

Funding

UCL Global Engagement Fund - 563920.100.177785.

Open data and materials availability statement

The datasets generated during and/or analysed during the current study are available in the repository: https://doi.org/10.5522/04/20183858.

Declarations and conflicts of interest

Research ethics statement

This study was pre-registered (https://osf.io/4nj3g/) on 17 April 2020 and ethical approval for the COVID-19 Social Study was granted by the University College London Institute of Education Ethics and Review Committee in April 2020 (REC 1331; [24]). The study is GDPR compliant.

Consent for publication statement

The authors declare that research participants’ informed consent to publication of findings – including photos, videos and any personal or identifiable information – was secured prior to publication.

Conflicts of interest statement

The author declares no conflict of interest with this work.

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 Open peer review from GIULIA BALBONI

Review

Review information

DOI:: 10.14293/S2199-1006.1.SOR-SOCSCI.AZRWIM.v1.RWTTIW
License:
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 , COVID-19 , machine learning , global study , lockdown , SARS-CoV-2 , 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.



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This review refers to round of peer review and may pertain to an earlier version of the document.

 Open peer review from GIULIA BALBONI

Review

Review information

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

Review text

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.



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This review refers to round of peer review and may pertain to an earlier version of the document.

 Open peer review from clarissa ferrari

Review

Review information

DOI:: 10.14293/S2199-1006.1.SOR-SOCSCI.ABACKK.v1.RVDNBU
License:
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

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

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

Introduction

  • 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.

Results

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

Discussion

-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.



Note:
This review refers to round of peer review and may pertain to an earlier version of the document.

 Open peer review from YOUYOU WU

Review

Review information

DOI:: 10.14293/S2199-1006.1.SOR-SOCSCI.AOQOQB.v1.RNSZQE
License:
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

Review text

The paper has two goals: the first goal is to replicate a previous using the same dataset but a different machine learning model. The previous finding was that “perceived loneliness”, among 12 mental health indicators, is most related to time into a COVID lockdown in the UK. The second goal is to confirm a u-shape relationship between perceived loneliness and weeks into the lockdown, using a different dataset from the second national lockdown.

My biggest concern is that there is little discussion on the effect size. We only knew about the MSE of the overall model and that “perceived loneliness” is relatively more related to time into lockdown than other variables (but not by how much). The authors mentioned in their previous paper (CITE) that the overall performance is bad, which I’d agree even without comparing the MSE or R2 with other similar machine learning tasks. Therefore, among a collection of highly correlated mental health variables that together are not so related to time into a lockdown, does it really matter that we identify the one that’s slightly more related to time? I’d like to see more justification of how this analysis is meaningful, taking into account effect sizes.

Now assuming the purpose of the analysis is justified, I move on to talk about the mechanics of the machine learning task. The analysis is based on a sample of 435 participants, which is admittedly quite many for a longitudinal study but small for a machine learning task. The authors are quite right on the need to replicate the effect using a different model given the small sample. Going down that route, I’d recommend go as far as replicating it using multiple models beyond the SVR to see if they agree. Having said that, I’d argue it’s more important to replicate the finding across different data sources than using a different model. I hope the authors could search other longitudinal data sources with similar variables and replicate the findings. At the very least, it will be good to know from the paper that there is no other suitable data source for this question and the finding based on this one dataset is preliminary.

If I am reading Table 2 correctly, the sample size seems incredibly small (5 participants from week 3, and 2, 3, and 1 participant from week 4, 5, and 6 for the second analysis. The week-by-week comparison would not be meaningful at all given the small sample. Hence the data from the second wave is not suitable for confirming or rejecting the U-shape finding in the first wave.



Note:
This review refers to round of peer review and may pertain to an earlier version of the document.