Self-perceived loneliness and depression during the Covid-19 pandemic: a two-wave replication study
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 le...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
| Language: | English |
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2022
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| Online Access: | https://hdl.handle.net/10356/163044 |
| _version_ | 1811678606154268672 |
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| author | Carollo, Alessandro Bizzego, Andrea Gabrieli, Giulio Wong, Keri Ka-Yee Raine, Adrian Esposito, Gianluca |
| author2 | School of Social Sciences |
| author_facet | School of Social Sciences Carollo, Alessandro Bizzego, Andrea Gabrieli, Giulio Wong, Keri Ka-Yee Raine, Adrian Esposito, Gianluca |
| author_sort | Carollo, Alessandro |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T02:55:56Z |
| format | Journal Article |
| id | ntu-10356/163044 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:55:56Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1630442023-04-04T01:04:04Z Self-perceived loneliness and depression during the Covid-19 pandemic: a two-wave replication study Carollo, Alessandro Bizzego, Andrea Gabrieli, Giulio Wong, Keri Ka-Yee Raine, Adrian Esposito, Gianluca School of Social Sciences Division of Psychology Social sciences::Psychology Social sciences Covid-19 Depression Loneliness Machine Learning Lockdown SARS-Cov-2 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. Published version UCL Global Engagement Fund - 563920.100.177785. 2022-11-22T07:49:29Z 2022-11-22T07:49:29Z 2022 Journal Article Carollo, A., Bizzego, A., Gabrieli, G., Wong, K. K., Raine, A. & Esposito, G. (2022). Self-perceived loneliness and depression during the Covid-19 pandemic: a two-wave replication study. UCL Open Environment, 4(20), 1-11. https://dx.doi.org/https://doi.org/10.14324/111.444/ucloe.000051 2632-0886 https://hdl.handle.net/10356/163044 10.14324/111.444/ucloe.000051 20 4 1 11 en UCL Open Environment © 2022 The Authors. Creative Commons Attribution Licence (CC BY) 4.0 International licence https://creativecommons.org/licenses/by/4.0/. This is an open access article distributed under the terms of the Creative Commons Attribution Licence (CC BY) 4.0 https://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited. application/pdf |
| spellingShingle | Social sciences::Psychology Social sciences Covid-19 Depression Loneliness Machine Learning Lockdown SARS-Cov-2 Carollo, Alessandro Bizzego, Andrea Gabrieli, Giulio Wong, Keri Ka-Yee Raine, Adrian Esposito, Gianluca Self-perceived loneliness and depression during the Covid-19 pandemic: a two-wave replication study |
| title | Self-perceived loneliness and depression during the Covid-19 pandemic: a two-wave replication study |
| title_full | Self-perceived loneliness and depression during the Covid-19 pandemic: a two-wave replication study |
| title_fullStr | Self-perceived loneliness and depression during the Covid-19 pandemic: a two-wave replication study |
| title_full_unstemmed | Self-perceived loneliness and depression during the Covid-19 pandemic: a two-wave replication study |
| title_short | Self-perceived loneliness and depression during the Covid-19 pandemic: a two-wave replication study |
| title_sort | self perceived loneliness and depression during the covid 19 pandemic a two wave replication study |
| topic | Social sciences::Psychology Social sciences Covid-19 Depression Loneliness Machine Learning Lockdown SARS-Cov-2 |
| url | https://hdl.handle.net/10356/163044 |
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