Prevalence and Factors Associated with Depression and Anxiety of Hospitalized Patients with COVID-19

ObjectiveThe 2019 coronavirus disease (COVID-19) epidemic has raised international concern. Mental health is becoming an issue that cannot be ignored in our fight against it. This study aimed to explore the prevalence and factors linked to anxiety and depression in hospitalized patients with COVID-19. MethodsA total of 144 patients diagnosed with COVID-19 were included in this study. We assessed depression and anxiety symptoms using the Hospital Anxiety and Depression Scale (HADS), and social support using the Perceived Social Support Scale (PSSS) among patients at admission. Multivariate linear regression analyses were performed to identify factors associated with symptoms of anxiety and depression. ResultsOf the 144 participants, 34.72% and 28.47% patients with COVID-19 had symptoms of anxiety or depression, respectively. The bivariate correlations showed that less social support was correlated with more anxious (r=-0.196, p<0.05) and depressive (r=-0.360,p<0.05) symptoms among patients with COVID-19. The multiple linear regression analysis showed that gender ({beta}=1.446, p=0.034), age ({beta}=0.074, p=0.003), oxygen saturation ({beta} =-2.140, p=0.049), and social support ({beta} =-1.545, p=0.017) were associated with anxiety for COVID-19 patients. Moreover, age ({beta}=0.084, p=0.001), family infection with SARS-CoV-2 ({beta} =1.515, p=0.027) and social support ({beta} =-2.236, p<0.001) were the factors associated with depression. ConclusionHospitalized patients with COVID-19 presented features of anxiety and depression. Mental concern and appropriate intervention are essential parts of clinical care for those who are at risk.

Unsupervised Hierarchical Dynamic Parsing and Encoding for Action Recognition.

Generally, the evolution of an action is not uniform across the video, but exhibits quite complex rhythms and non-stationary dynamics. To model such non-uniform temporal dynamics, in this paper, we describe a novel hierarchical dynamic parsing and encoding method to capture both the locally smooth dynamics and globally drastic dynamic changes. It parses the dynamics of an action into different layers and encodes such multi-layer temporal information into a joint representation for action recognition. At the first layer, the action sequence is parsed in an unsupervised manner into several smooth-changing stages corresponding to different key poses or temporal structures by temporal clustering. The dynamics within each stage are encoded by mean-pooling or rank-pooling. At the second layer, the temporal information of the ordered dynamics extracted from the previous layer is encoded again by rank-pooling to form the overall representation. Extensive experiments on a gesture action data set (Chalearn Gesture) and three generic action data sets (Olympic Sports, Hollywood2, and UCF101) have demonstrated the effectiveness of the proposed method.