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

UCL open environment Pub Date : 2022-11-03 eCollection Date: 2022-01-01 DOI:10.14324/111.444/ucloe.000051
Alessandro Carollo, Andrea Bizzego, Giulio Gabrieli, Keri Ka-Yee Wong, Adrian Raine, Gianluca Esposito
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引用次数: 2

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.

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新冠肺炎大流行期间自我感觉的孤独和抑郁:一项两波复制研究。
全球新冠肺炎疫情迫使各国实施严格的封锁限制和强制居家令,对个人健康产生不同影响。结合数据驱动的机器学习范式和统计方法,我们之前的论文记录了第一次封锁期间(2020年4月17日至7月17日)英国和希腊人口自我感知孤独水平的U型模式。目前的论文旨在通过关注英国第一波和第二波封锁的数据来测试这些结果的稳健性。我们测试了a)所选模型对识别封锁期间最具时间敏感性的变量的影响。采用两个新的机器学习模型,即支持向量回归器(SVR)和多元线性回归器(MLR),从第1波(n=435)中识别英国数据集中最具时间敏感性的变量。在研究的第二部分,我们测试了b)在英国第一次全国封锁中发现的自我感知孤独模式是否适用于英国第二波封锁(2020年10月17日至2021年1月31日)。为了做到这一点,英国封锁第二波(n=263)的数据被用来对自我感知孤独感得分的逐周分布进行图形检查。在SVR和MLR模型中,抑郁症状是封锁期间最具时间敏感性的变量。按封锁周对抑郁症状的统计分析显示,在英国全国封锁第1波的第3周至第7周之间,抑郁症状呈U型。此外,尽管第2波中每周的样本量太小,无法进行有意义的统计分析,但在封锁的第3周和第9周之间观察到了图形U型分布。与过去的研究一致,这些初步结果表明,在实施封锁限制时,自我感知的孤独感和抑郁症状可能是最相关的两种症状。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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