Night shifts, insomnia, anxiety, and depression among Chinese nurses during the COVID-19 pandemic remission period: A network approach

Abstract

BackgroundThe outbreak of the COVID-19 pandemic imposed a heavy workload on nurses with more frequent night shifts, which led to higher levels of insomnia, depression, and anxiety among nurses. The study aimed to describe the symptom-symptom interaction of depression, anxiety, and insomnia among nurses and to evaluate the impact of night shifts on mental distress via a network model.MethodsWe recruited 4,188 nurses from six hospitals in December 2020. We used the Insomnia Severity Index, Patient Health Questionnaire-9, and Generalized Anxiety Disorder Scale-7 to assess insomnia, depression, and anxiety, respectively. We used the gaussian graphical model to estimate the network. Index expected influence and bridge expected influence was adapted to identify the central and bridge symptoms within the network. We assessed the impact of night shifts on mental distress and compared the network structure based on COVID-19 frontline experience.ResultsThe prevalence of depression, anxiety, and insomnia was 59, 46, and 55%, respectively. Nurses with night shifts were at a higher risk for the three mental disorders. “Sleep maintenance” was the central symptom. “Fatigue,” “Motor,” “Restlessness,” and “Feeling afraid” were bridge symptoms. Night shifts were strongly associated with sleep onset trouble. COVID-19 frontline experience did not affect the network structure.Conclusion“Sleep maintenance,” “Fatigue,” “Motor,” and “Restlessness” were important in maintaining the symptom network of anxiety, depression, and insomnia in nurses. Further interventions should prioritize these symptoms.

Clinical symptoms, thyroid dysfunction, and metabolic disturbances in first-episode drug-naïve major depressive disorder patients with suicide attempts: A network perspective

“…We estimated and visualized the network using Rpackage "qgraph" and "bootnet" (41). We estimated the network using the default of the EBICglasso model, which was widely used in psychological network models (42).g was set to 0.5, which made the network more sparse and strikes a balance between sensitivity and specificity in preserving true edges. The network consisted of "nodes" (i.e., metabolic parameters, thyroid function, and clinical symptoms) and "edges" (i.e., pairwise correlations between two nodes after controlling for other variables within the network).…”

Prevalence and sociodemographic factors of depression, anxiety and stress in Saudi Arabia: a survey of respiratory therapists

“…Furthermore, Peng et al. indicated that night and rotating shifts were highly correlated with a greater probability of developing depression and anxiety ( 63 ). According to a recently published systematic review, nursing staff who worked night shifts were more likely to have depressive symptoms than their morning shift counterparts ( 64 ).…”

A network analysis of the long-term quality of life and mental distress of COVID-19 survivors 1 year after hospital discharge

“…The Rpackage "bootnet" and "qgraph" was used to estimate and visualize the network. Following previous studies (14,19), the Gaussian graphical model using extended Bayesian Information Criterion (EBIC) model selection was employed. The cormethod was set to "spearman" as the data were ordinal.…”

“…To date, emerging studies have documented the network structure of mental distress (mainly depression and anxiety) in different populations such as the general population (16), adolescents (17), college students (18), and healthcare workers (19,20) during the COVID-19 pandemic. However, to our knowledge, only two studies assessed the network of mental distress in COVID-19 patients (21,22).…”