Perceived Stress, Hope, and Health Outcomes Among Medical Staff in China During the COVID-19 Pandemic.

This study investigated the buffering role of hope between perceived stress and health outcomes among front-line medical staff treating patients with suspected COVID-19 infection in Shenzhen, China. In the cross-sectional study with online questionnaires, medical staff's perceived stress, anxiety, depression, sleep quality, and hope were measured by the 10-item Chinese Perceived Stress Scale, Hospital Anxiety and Depression Scale, the Pittsburgh Sleep Quality Index, and the Locus-of-Hope Scale, respectively. A total of 319 eligible front-line medical staff participated. The prevalence of anxiety (29.70%), depression (28.80%), poor sleep quality (38.90%) indicated that a considerable proportion of medical staff experienced mood and sleep disturbances during the COVID-19 pandemic. Internal locus-of-hope significantly moderated the effects of stress on anxiety, depression, and sleep quality. Moreover, external family locus-of-hope and external peer locus-of-hope significantly moderated the association between perceived stress and depression. The prevalence of symptoms indicates that both mental and physical health outcomes of front-line medical staff deserve more attention. Internal and external locus-of-hope functioned differently as protective factors for medical staffs' health and might be promising targets for intervention.

When Patients Recover From COVID-19: Data-Driven Insights From Wearable Technologies.

Coronavirus disease 2019 (COVID-19) is known as a contagious disease and caused an overwhelming of hospital resources worldwide. Therefore, deciding on hospitalizing COVID-19 patients or quarantining them at home becomes a crucial solution to manage an extremely big number of patients in a short time. This paper proposes a model which combines Long-short Term Memory (LSTM) and Deep Neural Network (DNN) to early and accurately classify disease stages of the patients to address the problem at a low cost. In this model, the LSTM component will exploit temporal features while the DNN component extracts attributed features to enhance the model's classification performance. Our experimental results demonstrate that the proposed model achieves substantially better prediction accuracy than existing state-of-art methods. Moreover, we explore the importance of different vital indicators to help patients and doctors identify the critical factors at different COVID-19 stages. Finally, we create case studies demonstrating the differences between severe and mild patients and show the signs of recovery from COVID-19 disease by extracting shape patterns based on temporal features of patients. In summary, by identifying the disease stages, this research will help patients understand their current disease situation. Furthermore, it will also help doctors to provide patients with an immediate treatment plan remotely that addresses their specific disease stages, thus optimizing their usage of limited medical resources.

COVID-19 Increases Online Searches for Emotional and Health-Related Terms.

BACKGROUND: The COVID-19 pandemic has powerfully shaped people's lives. The current work investigated the emotional and behavioral reactions people experience in response to COVID-19 through their internet searches. We hypothesised that when the prevalence rates of COVID-19 increase, people would experience more fear, which in turn would predict more searches for protective behaviors, health-related knowledge, and panic buying. METHODS: Prevalence rates of COVID-19 in the United States, the United Kingdom, Canada, and Australia were used as predictors. Fear-related emotions, protective behaviors, seeking health-related knowledge, and panic buying were measured using internet search volumes in Google Trends. RESULTS: We found that increased prevalence rates of COVID-19 were associated with more searches for protective behaviors, health knowledge, and panic buying. This pattern was consistent across four countries, the United States, the United Kingdom, Canada, and Australia. Fear-related emotions explained the associations between COVID-19 and the content of their internet searches. CONCLUSIONS: Findings suggest that exposure to COVID-19 prevalence and fear-related emotions may motivate people to search for relevant health-related information so as to protect themselves from the pandemic.