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