ABSTRACT
Background: Of around 2 billion Muslims worldwide, approximately 1.5 billion observe Ramadan fasting (RF) month. Those that observe RF have diverse cultural, ethnic, social, and economic backgrounds and are distributed over a wide geographical area. Sleep is known to be significantly altered during the month of Ramadan, which has a profound impact on human health. Moreover, sleep is closely connected to dietary and lifestyle behaviors. Methods: This cross-sectional study collected data using a structured, self-administered electronic questionnaire that was translated into 13 languages and disseminated to Muslim populations across 27 countries. The questionnaire assessed dietary and lifestyle factors as independent variables, and three sleep parameters (quality, duration, and disturbance) as dependent variables. We performed structural equation modeling (SEM) to examine how dietary and lifestyle factors affected these sleep parameters. Results: In total, 24,541 adults were enrolled in this study. SEM analysis revealed that during RF, optimum sleep duration (7-9 h) was significantly associated with sufficient physical activity (PA) and consuming plant-based proteins. In addition, smoking was significantly associated with greater sleep disturbance and lower sleep quality. Participants that consumed vegetables, fruits, dates, and plant-based proteins reported better sleep quality. Infrequent consumption of delivered food and infrequent screen time were also associated with better sleep quality. Conflicting results were found regarding the impact of dining at home versus dining out on the three sleep parameters. Conclusion: Increasing the intake of fruits, vegetables, and plant-based proteins are important factors that could help improve healthy sleep for those observing RF. In addition, regular PA and avoiding smoking may contribute to improving sleep during RF.
ABSTRACT
The case report describes a post-COVID-19 patient with severe right upper quadrant (RUQ) pain, moderate epigastric pain, high troponin levels, and nonspecific ST-segment and T-wave changes on electrocardiogram (ECG).
ABSTRACT
This study examined mental wellbeing and associated factors among Nigerian adults who observed Ramadan intermittent fasting (RIF) amid the COVID-19 pandemic. A cross-sectional online study was conducted among 770 adult Nigerians who observed RIF. Using pre-tested, web-based questionnaires, data about mental wellbeing (depression, anxiety), spirituality, and intrinsic religiosity were collected using validated generalised anxiety disorder-2 (GAD-2) and Patient Health Questionnaire-2 (PHQ-2), four-item spiritual wellbeing index (4-ISWBI) and the Muslim intrinsic religiosity questionnaire. Respondents' mental wellbeing before and during Ramadan was compared. The factors associated with the feeling of depression and anxiety were determined using multinomial regression analysis. When compared to mental wellbeing prior to Ramadan, observing RIF by Nigerian adults were associated with improved mental wellbeing.
ABSTRACT
When will the coronavirus end? Are the current precautionary measures effective? To answer these questions it is important to forecast regularly and accurately the spread of COVID-19 infections. Different time series forecasting models have been applied in the literature to tackle the pandemic situation. The current research efforts developed few of these models and validates its accuracy for selected countries. It becomes difficult to draw an objective comparison between the performance of these models at a global scale. This is because, the time series trend for the infection differs between the countries depending on the strategies adopted by the healthcare organizations to decrease the spread. Consequently, it is important to develop a tailored model for a country that allows healthcare organizations to better judge the effect of the undertaken precautionary measures, and provision more efficiently the needed resources to face this disease. This paper addresses this void. We develop and compare the performance of the time series models in the literature in terms of root mean squared error and mean absolute percentage error.