The prevalence of fatigue among Chinese nursing students in post-COVID-19 era

Mental health problems are common in college students even in the late stage of the coronavirus disease 2019 (COVID-19) outbreak. Network analysis is a novel approach to explore interactions of mental disorders at the symptom level. The aim of this study was to elucidate characteristics of depressive and anxiety symptoms network in college students in the late stage of the COVID-19 outbreak. A total of 3062 college students were included. The seven-item Generalized Anxiety Disorder Scale (GAD-7) and nine-item Patient Health Questionnaire (PHQ-9) were used to measure anxiety and depressive symptoms, respectively. Central symptoms and bridge symptoms were identified based on centrality and bridge centrality indices, respectively. Network stability was examined using the case-dropping procedure. The strongest direct relation was between anxiety symptoms “Nervousness” and “Uncontrollable worry”. “Fatigue” has the highest node strength in the anxiety and depression network, followed by “Excessive worry”, “Trouble relaxing”, and “Uncontrollable worry”. “Motor” showed the highest bridge strength, followed by “Feeling afraid” and “Restlessness”. The whole network was robust in both stability and accuracy tests. Central symptoms “Fatigue”, “Excessive worry”, “Trouble relaxing” and “Uncontrollable worry”, and critical bridge symptoms “Motor”, “Feeling afraid” and “Restlessness” were highlighted in this study. _targeting interventions to these symptoms may be important to effectively alleviate the overall level of anxiety and depressive symptoms in college students.

Background: The COVID-19 pandemic brought about great transformation to medical education mode. Although mobile communication devices played a crucial role in online learning among quarantined university students, the potential smartphone addition problems, negative health behaviors, and psychological symptoms need considerable attention. This study examined the relationship of problematic smartphone use (PSU), sleep quality, and daytime fatigue among medical students.Methods: A web-based survey was conducted in six polyclinic hospitals in Beijing between February and May 2020. 1016 participants (26.01 ± 2.46 years, 65.16% female) completed self-report measurements including Short Version Smartphone Addiction Scale (SAS-SV), Athens Insomnia Scale (AIS), and Subjective Fatigue Scale (FS). Spearman correlation coefficients and multiple regression models were used to analyze the association among PSU, sleep quality, and daytime fatigue. We used structural equation modeling to test the mediating effect of sleep quality between PSU and daytime fatigue.Results: 49.70% of the participants had PSU. Significant positive correlations were found among SAS-SV, AIS, and FS scores (r = 0.35–0.61, PS < 0.001). Subjects with PSU were more likely to report sleep disturbance (β = 1.07, P < 0.001, OR = 2.91, 95%CI = 2.17–3.91), physical fatigue (β = 1.16, P < 0.001, OR = 3.18, 95%CI = 2.45–4.15), and mental fatigue (β = 0.88, P < 0.001, OR = 2.42, 95%CI = 1.86–3.14). The indirect effect of PSU on physical fatigue and mental fatigue mediated by sleep quality accounted for 50.03 and 45.43% of the total effect, respectively.Conclusions: PSU was significantly associated with sleep disturbance and fatigue among medical students during the COVID-19 pandemic. Sleep quality mediated the relationship between PSU and daytime fatigue. Our results provide valuable information for maintaining medical students' health status and constructing online education structures.

The rapid worldwide outbreak of COVID-19 has posed serious and unprecedented challenges to healthcare systems in predicting disease behavior, consequences and resource utilization. Therefore, predicting the Length of Stay (LOS) is necessary to ensure optimal allocate of scarce hospital resources. The purpose of this research was to construct a model for predicting COVID-19 patients' hospital LOS by multiple Machine Learning (ML) algorithms. Using a single-center registry, we studied the records of 1225 laboratory-confirmed COVID-19 hospitalized patients from February 9, 2020, to December 20, 2020. The most important clinical parameters in the COVID-19 LOS prediction were identified with a correlation coefficient at the P-value< 0.2. Then, the prediction models were developed based on seven ML techniques according to selected variables. Finally, to evaluate the performances of those models several standard quantitative measures includes accuracy, sensitivity, specificity and ROC curve were used to evaluate the proposed predictive models. After implementing feature selection, a total of 20 variables was identified as the most relevant predictors to build the prediction models. The results indicated that the best performance belonged to the Support Vector Machine (SVM) algorithm with the mean accuracy of 99.5%, mean specificity of 99.7%, mean sensitivity of 99.4%, and the standard deviation of 1.2. The SVM provided a reasonable level of accuracy and certainty in predicting the LOS in COVID-19 patients and potentially facilitates hospital bed management, turnover and optimized resource allocation.