Cargando…
Network Analysis of Insomnia in Chinese Mental Health Professionals During the COVID-19 Pandemic: A Cross-Sectional Study
PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic is associated with increased risk of insomnia symptoms (insomnia hereafter) in health-care professionals. Network analysis is a novel approach in linking mechanisms at the symptom level. The aim of this study was to characterize the insomnia...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8560171/ https://www.ncbi.nlm.nih.gov/pubmed/34737660 http://dx.doi.org/10.2147/NSS.S326880 |
_version_ | 1784592887028121600 |
---|---|
author | Bai, Wei Zhao, Yanjie An, Fengrong Zhang, Qinge Sha, Sha Cheung, Teris Cheng, Calvin Pak-Wing Ng, Chee H Xiang, Yu-Tao |
author_facet | Bai, Wei Zhao, Yanjie An, Fengrong Zhang, Qinge Sha, Sha Cheung, Teris Cheng, Calvin Pak-Wing Ng, Chee H Xiang, Yu-Tao |
author_sort | Bai, Wei |
collection | PubMed |
description | PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic is associated with increased risk of insomnia symptoms (insomnia hereafter) in health-care professionals. Network analysis is a novel approach in linking mechanisms at the symptom level. The aim of this study was to characterize the insomnia network structure in mental health professionals during the COVID-19 pandemic. PATIENTS AND METHODS: A total of 10,516 mental health professionals were recruited from psychiatric hospitals or psychiatric units of general hospitals nationwide between March 15 and March 20, 2020. Insomnia was assessed with the insomnia severity index (ISI). Centrality index (ie, strength) was used to identify symptoms central to the network. The stability of network was examined using a case-dropping bootstrap procedure. The network structures between different genders were also compared. RESULTS: The overall network model showed that the item ISI7 (interference with daytime functioning) was the most central symptom in mental health professionals with the highest strength. The network was robust in stability and accuracy tests. The item ISI4 (sleep dissatisfaction) was connected to the two main clusters of insomnia symptoms (ie, the cluster of nocturnal and daytime symptoms). No significant gender network difference was found. CONCLUSION: Interference with daytime functioning was the most central symptom, suggesting that it may be an important treatment outcome measure for insomnia. Appropriate treatments, such as stimulus control techniques, cognitive behavioral therapy and relaxation training, could be developed. Moreover, addressing sleep satisfaction in treatment could simultaneously ameliorate daytime and nocturnal symptoms. |
format | Online Article Text |
id | pubmed-8560171 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Dove |
record_format | MEDLINE/PubMed |
spelling | pubmed-85601712021-11-03 Network Analysis of Insomnia in Chinese Mental Health Professionals During the COVID-19 Pandemic: A Cross-Sectional Study Bai, Wei Zhao, Yanjie An, Fengrong Zhang, Qinge Sha, Sha Cheung, Teris Cheng, Calvin Pak-Wing Ng, Chee H Xiang, Yu-Tao Nat Sci Sleep Original Research PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic is associated with increased risk of insomnia symptoms (insomnia hereafter) in health-care professionals. Network analysis is a novel approach in linking mechanisms at the symptom level. The aim of this study was to characterize the insomnia network structure in mental health professionals during the COVID-19 pandemic. PATIENTS AND METHODS: A total of 10,516 mental health professionals were recruited from psychiatric hospitals or psychiatric units of general hospitals nationwide between March 15 and March 20, 2020. Insomnia was assessed with the insomnia severity index (ISI). Centrality index (ie, strength) was used to identify symptoms central to the network. The stability of network was examined using a case-dropping bootstrap procedure. The network structures between different genders were also compared. RESULTS: The overall network model showed that the item ISI7 (interference with daytime functioning) was the most central symptom in mental health professionals with the highest strength. The network was robust in stability and accuracy tests. The item ISI4 (sleep dissatisfaction) was connected to the two main clusters of insomnia symptoms (ie, the cluster of nocturnal and daytime symptoms). No significant gender network difference was found. CONCLUSION: Interference with daytime functioning was the most central symptom, suggesting that it may be an important treatment outcome measure for insomnia. Appropriate treatments, such as stimulus control techniques, cognitive behavioral therapy and relaxation training, could be developed. Moreover, addressing sleep satisfaction in treatment could simultaneously ameliorate daytime and nocturnal symptoms. Dove 2021-10-28 /pmc/articles/PMC8560171/ /pubmed/34737660 http://dx.doi.org/10.2147/NSS.S326880 Text en © 2021 Bai et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). |
spellingShingle | Original Research Bai, Wei Zhao, Yanjie An, Fengrong Zhang, Qinge Sha, Sha Cheung, Teris Cheng, Calvin Pak-Wing Ng, Chee H Xiang, Yu-Tao Network Analysis of Insomnia in Chinese Mental Health Professionals During the COVID-19 Pandemic: A Cross-Sectional Study |
title | Network Analysis of Insomnia in Chinese Mental Health Professionals During the COVID-19 Pandemic: A Cross-Sectional Study |
title_full | Network Analysis of Insomnia in Chinese Mental Health Professionals During the COVID-19 Pandemic: A Cross-Sectional Study |
title_fullStr | Network Analysis of Insomnia in Chinese Mental Health Professionals During the COVID-19 Pandemic: A Cross-Sectional Study |
title_full_unstemmed | Network Analysis of Insomnia in Chinese Mental Health Professionals During the COVID-19 Pandemic: A Cross-Sectional Study |
title_short | Network Analysis of Insomnia in Chinese Mental Health Professionals During the COVID-19 Pandemic: A Cross-Sectional Study |
title_sort | network analysis of insomnia in chinese mental health professionals during the covid-19 pandemic: a cross-sectional study |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8560171/ https://www.ncbi.nlm.nih.gov/pubmed/34737660 http://dx.doi.org/10.2147/NSS.S326880 |
work_keys_str_mv | AT baiwei networkanalysisofinsomniainchinesementalhealthprofessionalsduringthecovid19pandemicacrosssectionalstudy AT zhaoyanjie networkanalysisofinsomniainchinesementalhealthprofessionalsduringthecovid19pandemicacrosssectionalstudy AT anfengrong networkanalysisofinsomniainchinesementalhealthprofessionalsduringthecovid19pandemicacrosssectionalstudy AT zhangqinge networkanalysisofinsomniainchinesementalhealthprofessionalsduringthecovid19pandemicacrosssectionalstudy AT shasha networkanalysisofinsomniainchinesementalhealthprofessionalsduringthecovid19pandemicacrosssectionalstudy AT cheungteris networkanalysisofinsomniainchinesementalhealthprofessionalsduringthecovid19pandemicacrosssectionalstudy AT chengcalvinpakwing networkanalysisofinsomniainchinesementalhealthprofessionalsduringthecovid19pandemicacrosssectionalstudy AT ngcheeh networkanalysisofinsomniainchinesementalhealthprofessionalsduringthecovid19pandemicacrosssectionalstudy AT xiangyutao networkanalysisofinsomniainchinesementalhealthprofessionalsduringthecovid19pandemicacrosssectionalstudy |