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1.
Nat Sci Sleep ; 13: 1921-1930, 2021.
Article in English | MEDLINE | ID: covidwho-1503595

ABSTRACT

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.

2.
Am J Addict ; 30(6): 585-592, 2021 11.
Article in English | MEDLINE | ID: covidwho-1416264

ABSTRACT

BACKGROUND AND OBJECTIVES: The prevalence of problematic Internet use (PIU) in the post-COVID-19 pandemic era is not known. This cross-sectional study aimed to determine the prevalence of PIU among baccalaureate nursing students (hereafter: nursing students) in the post-COVID-19 era. METHODS: A total of 1070 nursing students were consecutively invited to participate in this study from the nursing schools of five universities. PIU and quality of life (QOL) were assessed using the Internet Addiction Test (IAT) and the World Health Organization Quality of Life Scale Brief Version (WHOQOL-BREF), respectively. t Tests, χ2 , tests, and Kruskal-Wallis tests were used to compare basic demographic and clinical characteristics between participants with and without PIU. Binary logistic regression analysis was used to examine independent correlates. RESULTS: The prevalence of PIU was 23.3% (95% confidence interval [CI]: 20.7%-25.8%). Multiple logistic regression analysis revealed that second- (p = .024) and third-year (p = .012) students were more likely to suffer from PIU compared with first year students. Students with more severe depressive (p = .014) and anxiety symptoms (p = .011) were independently and significantly associated with more severe PIU. After controlling for covariates, nursing students with PIU had a lower overall QOL score (p = .002). CONCLUSION AND SCIENTIFIC SIGNIFICANCE: Problematic Internet use (PIU) was common among nursing students in the post-COVID-19 era. Considering the negative impact of PIU on QOL and academic performance, regular screening should be conducted and effective interventions implemented for nursing students with PIU. This was the first study on the prevalence of PIU among nursing students in the post-COVID-19 era. The findings of this study could help health professionals and education authorities to understand the patterns of PIU and its influence on QOL among nursing students and to allocate health resources and develop effective measures to reduce the risk of PIU in this population.

3.
J Affect Disord ; 294: 753-760, 2021 Nov 01.
Article in English | MEDLINE | ID: covidwho-1322168

ABSTRACT

BACKGROUND: The 2019 coronavirus disease (COVID-19) pandemic has impacted the mental health and well-being of medical personnel, including nursing students. Network analysis provides a deeper characterization of symptom-symptom interactions in mental disorders. The aim of this study was to elucidate characteristics of anxiety and depressive symptom networks of Chinese nursing students during the COVID-19 pandemic. METHOD: A total of 932 nursing students were included. Anxiety and depressive symptom were measured using the seven-item Generalized Anxiety Disorder Scale (GAD-7) and two-item Patient Health Questionnaire (PHQ-2), respectively. Central symptoms and bridge symptoms were identified via centrality indices and bridge centrality indices, respectively. Network stability was examined using the case-dropping procedure. RESULTS: Irritability, Uncontrollable worry, Trouble relaxing, and Depressed mood had the highest centrality values. Three bridge symptoms (Depressed mood, Nervousness, and Anhedonia) were also identified. Neither gender nor region of residence was associated with network global strength, distribution of edge weights or individual edge weights. LIMITATIONS: Data were collected in a cross-sectional study design, therefore, causal relations and dynamic changes between anxiety and depressive symptoms over time could not be inferred. Generalizability of findings may be limited to Chinese nursing students during a particular phase of the current pandemic. CONCLUSIONS: Irritability, Uncontrollable worry, Trouble relaxing, and Depressed mood constituted central symptoms maintaining the anxiety-depression network structure of Chinese nursing students during the pandemic. Timely, systemic multi-level interventions targeting central symptoms and bridge symptoms may be effective in alleviating co-occurring experiences of anxiety and depression in this population.


Subject(s)
COVID-19 , Students, Nursing , Anxiety/epidemiology , Cross-Sectional Studies , Depression/epidemiology , Humans , Pandemics , SARS-CoV-2
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