Examining Anxiety, Sleep Quality, and Physical Activity as Predictors of Depression among University Students from Saudi Arabia during the Second Wave of the COVID-19 Pandemic.

Conducted during the second wave of the pandemic, this cross-sectional study examined the link between sleep quality, physical activity, exposure, and the impact of COVID-19 as predictors of mental health in Saudi undergraduate students. A convenience sample of 207 participants were recruited, 89% of whom were females and 94% were single. The measures included questionnaires on the level of exposure and the perceived impact of COVID-19, a physical activity measure, GAD-7, PHQ-9, and PSQI. The results indicated that approximately 43% of participants exhibited moderate anxiety, and 50% were at risk of depression. Overall, 63.93% of students exposed to strict quarantine for at least 14 days (n = 39) exhibited a high risk of developing depression (χ2(1) = 6.49, p < 0.05, ϕ = 0.18). A higher risk of depression was also found in students whose loved ones lost their jobs (χ2(1) = 4.24, p < 0.05, ϕ = 0.14). Moreover, there was also a strong association between depression and anxiety (ß = 0.33, p < 0.01), sleep quality (ß = 0.32, p < 0.01), and the perceived negative impact of COVID-19 on socio-economic status (ß = 0.26, p < 0.05), explaining 66.67% of depression variance. Our study highlights the socio-economic impact of this pandemic and the overwhelming prevalence of depression.

COVID-19 Vaccination-Related Sentiments Analysis: A Case Study Using Worldwide Twitter Dataset.

COVID-19 pandemic has caused a global health crisis, resulting in endless efforts to reduce infections, fatalities, and therapies to mitigate its after-effects. Currently, large and fast-paced vaccination campaigns are in the process to reduce COVID-19 infection and fatality risks. Despite recommendations from governments and medical experts, people show conceptions and perceptions regarding vaccination risks and share their views on social media platforms. Such opinions can be analyzed to determine social trends and devise policies to increase vaccination acceptance. In this regard, this study proposes a methodology for analyzing the global perceptions and perspectives towards COVID-19 vaccination using a worldwide Twitter dataset. The study relies on two techniques to analyze the sentiments: natural language processing and machine learning. To evaluate the performance of the different lexicon-based methods, different machine and deep learning models are studied. In addition, for sentiment classification, the proposed ensemble model named long short-term memory-gated recurrent neural network (LSTM-GRNN) is a combination of LSTM, gated recurrent unit, and recurrent neural networks. Results suggest that the TextBlob shows better results as compared to VADER and AFINN. The proposed LSTM-GRNN shows superior performance with a 95% accuracy and outperforms both machine and deep learning models. Performance analysis with state-of-the-art models proves the significance of the LSTM-GRNN for sentiment analysis.

COVID-19: A new challenge for mental health and policymaking recommendations.

The coronavirus disease 2019 (COVID-19) infection has emerged lately, leading to a serious public health threat. The clinical features associated with COVID-19 are yet to be conclusively documented. Caution is needed when interpreting the severity of the symptoms as most of the diagnosed patients are those attending clinical assessments. Features of COVID-19 are far from understood. There is a suggested increased risk of COVID-19 infection among people with mental health disorders, which is primarily attributable to the challenges associated with limited resources. There are a variety of reasons why individuals with mental health disorders are more susceptible to infectious diseases. There is currently no specific recommended antiviral treatment. The interventions now used are supportive treatments to alleviate the symptoms and invasive mechanical ventilation. In this review, we discuss the adverse events associated with COVID-19 vaccinations. We further highlight the need to develop guidelines and recommendations for managing patients with mental health. It is evident from this review, there is a need to provide training programs with interprofessional, multidisciplinary communication channels.

Demographics, comorbidities, and outcomes among young and middle-aged COVID-19 patients in Saudi Arabia.

The impact of different sociodemographic and clinical characteristics on the COVID-19-related morbidity and mortality rates have been studied extensively around the world; however, there is a dearth of data on the impact of different clinical and sociodemographic variables on the COVID-19-related outcomes in Saudi Arabia. This study aimed to identify those at high risk of worse clinical outcomes, such as hospitalization and longer length of stay (LOS) among young and middle-aged adults (18 to 55 years). In this questionnaire-based cross-sectional study, 706 patients with real-time polymerase chain reaction (RT-PCR) confirmed COVID-19 infection were interviewed. Patients' demographic characteristics, dietary habits, medical history, and lifestyle choices were collected through phone interviews. Patients with chronic health conditions, such as diabetes and hypertension, reported a higher rate of hospitalization, ICU admission, oxygen-support needs, and a longer period of recovery and LOS. Multiple logistic regression showed that diabetes, hypertension, and pulmonary disease (e.g., asthma and chronic obstructive pulmonary disease (COPD)) were associated with a higher risk of hospitalization and longer LOS. Multiple logistic regression showed that symptoms of breathlessness, loss of smell and/or taste, diarrhea, and cough were associated with a longer recovery period. Similarly, breathlessness, vomiting, and diarrhea were associated with higher rates of hospitalization. The findings of this study confirm the similarity of the factors associated with worse clinical outcomes across the world. Future studies should use more robust designs to investigate the impact of different therapies on the COVID-19-related morbidity and mortality in Saudi Arabia.