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

Abstract

\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.

Covid-19 pandemic: our relationships, environment, and health

“…Unsurprisingly, loneliness emerges as an important factor in more than one way as reported in the series articles. For example, in Carollo et al [ 4 ], the authors used novel machine learning techniques to understand the impact of lockdown duration and restrictions on adults from Greece and the UK and revealed that loneliness and depression were key symptoms that fluctuated during the pandemic. Even prior to the pandemic, there were discussions about a worldwide ‘loneliness epidemic’.…”