Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/109439
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dc.contributorDepartment of Applied Social Sciences-
dc.creatorLiu, T-
dc.creatorWang, YH-
dc.creatorNg, ZLY-
dc.creatorZhang, W-
dc.creatorWong, SMY-
dc.creatorWong, GHY-
dc.creatorLum, TYS-
dc.date.accessioned2024-10-18T06:10:40Z-
dc.date.available2024-10-18T06:10:40Z-
dc.identifier.urihttp://hdl.handle.net/10397/109439-
dc.language.isoenen_US
dc.publisherNature Publishing Groupen_US
dc.rights© The Author(s) 2024en_US
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4. 0/.en_US
dc.rightsThe following publication Liu, T., Wang, YH., Ng, Z.L.Y. et al. Comparison of networks of loneliness, depressive symptoms, and anxiety symptoms in at-risk community-dwelling older adults before and during COVID-19. Sci Rep 14, 14737 (2024) is available at https://doi.org/10.1038/s41598-024-65533-z.en_US
dc.titleComparison of networks of loneliness, depressive symptoms, and anxiety symptoms in at-risk community-dwelling older adults before and during COVID-19en_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume14-
dc.identifier.doi10.1038/s41598-024-65533-z-
dcterms.abstractNetwork analysis provides an innovative approach to examining symptom-to-symptom interactions in mental health, and adverse external conditions may change the network structures. This study compared the networks of common risk factors and mental health problems (loneliness, depressive symptoms, and anxiety symptoms) in community-dwelling older people before and during COVID-19. Older adults (aged ≥ 60) at risk for depression were recruited through non-governmental organizations. Loneliness, depressive symptoms and anxiety symptoms were measured using the three-item Loneliness Scale (UCLA-3), nine-item Patient Health Questionnaire (PHQ-9), and seven-item Generalized Anxiety Disorder Scale (GAD-7), respectively. Data from 2549 (before) and 3506 (during COVID-19) respondents were included using propensity score matching. Being restless (GAD-7-item5) was most central, indicated by Expected Influence, in both pre and during COVID-19 networks despite low severity (mean score). The network during COVID-19 had higher global strength and edge variability than the pre-pandemic network, suggesting easier symptom spread and potentially more complex symptom presentation. In addition, feeling isolated from others (UCLA-3-item3) had stronger connections with feeling worthless/guilty (PHQ-9-item6) and anticipatory anxiety (GAD-7-item7) during COVID-19 than before. These findings may enhance our knowledge of the symptom structure of common mental health problems and the impacts of the pandemic. Targeting central symptoms may offer novel preventive strategies for older people.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationScientific reports, 2024, v. 14, 14737-
dcterms.isPartOfScientific reports-
dcterms.issued2024-
dc.identifier.scopus2-s2.0-85197108185-
dc.identifier.eissn2045-2322-
dc.identifier.artn14737-
dc.description.validate202410 bcch-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Othersen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextHong Kong Jockey Club Charities Trusten_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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