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Introduction: Previous coronavirus, 2019 (COVID-19) research has applied network analysis to examine relationships between psychopathological symptoms but rarely extended to potential risk and protective factors or the influence of COVID-19 infection history. This study examined complex inter-relationships between psychopathological symptoms, COVID-19-related stressors, perceived social support, and COVID-19 infection history among Chinese university/college students during the peak of fifth pandemic wave using a network analysis approach. Methods: A Least Absolute Shrinkage and Selection Operator-regularized partial correlation network using Gaussian graphical model was constructed in 1,395 Chinese university/college students in Hong Kong who completed a survey between 15 March and 3 April, 2022. Depressive, anxiety, and acute/traumatic stress symptoms were measured by Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, and Impact of Event Scale-6, respectively. COVID-19-related stressors and perceived social support were measured. Network differences by COVID-19 infection history (COVID-network vs. no_COVID-network) and network communities were examined. Results: Our results showed that the most influential nodes were depressed mood, uncontrollable worries, and uncontrollable thoughts about COVID-19. The main bridging symptoms were concentration problems and psychomotor problems. The COVID-network, comprising participants with a history of COVID-19 infection only, was significantly stronger than the no_COVID-network. Perceived social support and stress from conflicts with family/friends formed a unique community with negative cognition and suicidal idea in the COVID-network only. Conclusion: Our findings indicate that specific interventions targeting interpersonal conflicts and concentration problems as well as facilitating stress buffering effects of social support may represent effective strategies to reduce psychological distress in university/college students during COVID-19 and should be considered for future pandemic preparedness.
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Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a growing prevalence of sleep problems associated with significant behavioral problems and more severe autism clinical presentation. Little is known about the relationships between autism traits and sleep problems in Hong Kong. Therefore, this study aimed to examine whether children with autism have increased sleep problems than non-autistic children in Hong Kong. The secondary objective was to examine the factors associated with sleep problems in an autism clinical sample. Methods: This cross-sectional study recruited 135 children with autism and 102 with the same age range of non-autistic children, aged between 6 and 12 years. Both groups were screened and compared on their sleep behaviors using the Children's Sleep Habits Questionnaire (CSHQ). Results: Children with autism had significantly more sleep problems than non-autistic children [t (226.73) = 6.20, p < 0.001]. Bed -sharing [beta = 0.25, t (165) = 2.75, p = 0.07] and maternal age at birth [beta = 0.15, t (165) = 2.05, p = 0.043] were significant factors associated with CSHQ score on the top of autism traits. Stepwise linear regression modeling identified that only separation anxiety disorder (beta = 4.83, t = 2.40, p = 0.019) best-predicted CSHQ. Conclusion: In summary, autistic children suffered from significantly more sleep problems and co-occurring separation anxiety disorder brings greater sleep problems as compared to non-autistic children. Clinicians should be more aware of sleep problems to provide more effective treatments to children with autism.
