Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/109073
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dc.contributorSchool of Nursing-
dc.creatorCai, H-
dc.creatorZhao, YJ-
dc.creatorHe, F-
dc.creatorLi, SY-
dc.creatorLi, ZL-
dc.creatorZhang, WY-
dc.creatorZhang, Y-
dc.creatorCheung, T-
dc.creatorNg, CH-
dc.creatorSha, S-
dc.creatorXiang, YT-
dc.date.accessioned2024-09-19T03:12:57Z-
dc.date.available2024-09-19T03:12:57Z-
dc.identifier.urihttp://hdl.handle.net/10397/109073-
dc.language.isoenen_US
dc.publisherNature Publishing Groupen_US
dc.rights© The Author(s) 2023en_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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.en_US
dc.rightsThe following publication Cai, H., Zhao, YJ., He, F. et al. Internet addiction and residual depressive symptoms among clinically stable adolescents with major psychiatric disorders during the COVID-19 pandemic: a network analysis perspective. Transl Psychiatry 13, 186 (2023) is available at https://doi.org/10.1038/s41398-023-02468-5.en_US
dc.titleInternet addiction and residual depressive symptoms among clinically stable adolescents with major psychiatric disorders during the COVID-19 pandemic : a network analysis perspectiveen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume13-
dc.identifier.doi10.1038/s41398-023-02468-5-
dcterms.abstractTo assess the inter-relationships between residual depressive symptoms (RDS) and Internet addiction (IA) using network analysis among clinically stable adolescents with major psychiatric disorders during the COVID-19 pandemic. RDS and IA were assessed using the Patient Health Questionnaire-9 (PHQ-9) and the Internet Addiction Test (IAT), respectively. Central symptoms and bridge symptoms in the network model were examined. A total of 1,454 adolescents met the study criteria and were included in the analyses. The prevalence of IA was 31.2% (95% CI: 28.8%-33.6%). In the network analysis, the nodes IAT15 (“Preoccupation with the Internet”), PHQ2 (“Sad mood”), and PHQ1 (“Anhedonia”) were the most central symptoms in the IA-RDS network model. Bridge symptoms included IAT10 (“Sooth disturbing about your Internet use”), PHQ9 (“Suicide ideation”), and IAT3 (“Prefer the excitement online to the time with others”). Additionally, PHQ2 (“Sad mood”) was the main node linking “Anhedonia” to other IA clusters. Internet addiction was common among clinically stable adolescents with major psychiatric disorders during the COVID-19 pandemic. Core and bridge symptoms identified in this study could be prioritized as targets for the prevention and treatment of IA in this population.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationTranslational psychiatry, 2023, v. 13, 186-
dcterms.isPartOfTranslational psychiatry-
dcterms.issued2023-
dc.identifier.scopus2-s2.0-85160882680-
dc.identifier.pmid37270593-
dc.identifier.eissn2158-3188-
dc.identifier.artn186-
dc.description.validate202409 bcch-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextNational Science and Technology Major Project for an investigational new drug; National Natural Science Foundation of China; Beijing Municipal Science & Technology Commission; University of Macauen_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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