Predictive Factors of Hospitalization and Emergency Visits Among Children With Asthma.

Introduction A chronic diverse inflammatory disease, asthma affects millions of people worldwide. To control asthma, standardized care is essential. Children with asthma who receive appropriate care have lower emergency room (ER) visits and hospital stays as well as a higher quality of life than children who do not receive appropriate care. We aim to evaluate the predictive variables of hospitalization and ER visits in children with asthma. Methodology In 2022 and 2023, a cross-sectional descriptive study was carried out on children with asthma and their caregivers who were attending primary health care clinics in the eastern region of Saudi Arabia. We used the Childhood Asthma Control Test (C-ACT) to evaluate asthma control. A C-ACT score of less than 19 indicates uncontrolled childhood asthma. To investigate the relationships between the risk factors and the rate of ER visits and hospitalizations, we performed a multiple logistic descriptive analysis. Results In this study, 124 asthmatic children from primary health care centers matched the inclusion criteria. The majority of children had atopy, and their mean age was 10.8±3.4 years. Concerning the risk factors linked to ER visits and hospitalization, there is evidence that not following up with physicians, using more frequent and short-acting beta-agonists, exposure to smoke and household pets, and poor asthma control are linked to increased rates of both ER visits and hospitalizations. Conclusion Better asthma control in children and adolescents may be achieved by providing inexpensive asthma care services, more thorough parental and child education, and effective symptom management. These measures can help reduce exacerbations of asthma and the consequences that accompany them.

Depression and Anxiety in Patients With Polycystic Ovary Syndrome: A Cross-Sectional Study in Saudi Arabia.

INTRODUCTION: Polycystic ovary syndrome (PCOS) is the most common endocrine disorder in females of childbearing age. It causes irregular menstruation, infertility, acne vulgaris, androgenic alopecia, and hirsutism. It is associated with a higher risk of mental disorders. This study aimed to determine the prevalence of depression and anxiety among females with PCOS and the factors associated with these disorders. METHODS: This cross-sectional study was conducted between 15th January and 19th November 2023. We invited Saudi women to do an online survey. We sent the survey link privately, got their permission, and explained the research to ensure privacy and reliability. Females with a previous psychiatric history were excluded. Depression, Anxiety, and Stress Scale-21 Items (DASS-21) were used to assess depression, anxiety, and distress. One-way analysis of variance (ANOVA) and two-sample t-tests were used to identify determinants of depression and anxiety. RESULTS: About 967 females participated, of whom 474 (49%) were married, and 358 (37%) had a healthy weight. About 367 (37.9%) of participants were diagnosed with PCOS, and it was associated with age (26-35 years), divorce, and family history of PCOS (p < 0.05). About 112 (30.5%) of PCOS patients experienced extremely severe depression, and 144 (39.2%) had extreme anxiety. People in the age range of 15-25 years had a higher risk of depression and stress (p < 0.05). Divorced participants faced a higher risk of depression, anxiety, and stress than singles (p < 0.05). Those advised on diet and healthy lifestyles exhibited a higher risk of depression, anxiety, and stress than those who were not (p < 0.05). Additionally, being overweight was associated with a higher risk of depression (p < 0.05). CONCLUSION: The prevalence of PCOS was found to be 37.9% in our study, which may seem higher compared to the existing literature on PCOS. It is associated with being in the age group of 26-35 years, being divorced, and having a positive family history. Almost two-thirds of females with PCOS had depression, anxiety, and stress. Factors associated with the three disorders include divorce and management with diet and lifestyle modifications. Depression and stress were associated with young age. High body mass index (BMI) was associated with depression.

Association Between Time Spent on Electronic Devices and Body Mass Index in Young Adults.

Objective: To evaluate the association between time spent on electronic devices (TSED) and body mass index in young adults. Methods: This cross-sectional study was conducted from June to December 2021 on 1877 students (aged 18-22 yrs) from multiple health Colleges of Imam Abdulrahman Bin Faisal University. The main tools of the study were 1) Body mass index (BMI) and an online questionnaire. The subjects were categorized into 3 main groups based on their TSED: 1) Low TSED < 2 hours/day, 2) Medium TSED= 3 to 5 hours/day, 3) Excessive TSED ≥ 6 hours /day. Based on BMI, subjects were categorized into three main groups: Normal and underweight (BMI ≤ 24.9), overweight (BMI > 25-29.9), and obese (BMI > 30). Results: Participants' average age was 20 ± 2 years. The average BMI was 23.5 k/m2. The % of students falling into the categories of normal weight, overweight, and obesity was 69.2%, 19.05%, and 11.7% respectively. The average TSED of study participants was 8.2 ± 3.45 hrs /24 hours. 71.15% of participants indicated a TSED of ≥6 hrs/24 hours and 23.71% reported a TSED of 3-5 hrs/24 hours. Only 3.15% of participants reported TSED of ≤2 hrs/24 hours. Although a rise in the mean BMI was observed with an increase in the TSED, but this difference was not statistically significant. The pairwise wise comparison also failed to demonstrate any difference in BMI between different categories of TSED. Furthermore, no significant positive correlation was found between increased BMI and excessive TSED (P = 0.37). Conclusion: A high percentage of young adults (31.2%) were overweight or obese, but excessive TSED was not significantly associated with increased BMI in this study population. Further studies are recommended to identify the effects of other factors in causing increased BMI in young adults.

Computational Intelligence-Based Model for Exploring Individual Perception on SARS-CoV-2 Vaccine in Saudi Arabia.

Countries around the world are facing so many challenges to slow down the spread of the current SARS-CoV-2 virus. Vaccination is an effective way to combat this virus and prevent its spreading among individuals. Currently, there are more than 50 SARS-CoV-2 vaccine candidates in trials; only a few of them are already in use. The primary objective of this study is to analyse the public awareness and opinion toward the vaccination process and to develop a model that predicts the awareness and acceptability of SARS-CoV-2 vaccines in Saudi Arabia by analysing a dataset of Arabic tweets related to vaccination. Therefore, several machine learning models such as Support Vector Machine (SVM), Naïve Bayes (NB), and Logistic Regression (LR), sideways with the N-gram and Term Frequency-Inverse Document Frequency (TF-IDF) techniques for feature extraction and Long Short-Term Memory (LSTM) model used with word embedding. LR with unigram feature extraction has achieved the best accuracy, recall, and F1 score with scores of 0.76, 0.69, and 0.72, respectively. However, the best precision value of 0.80 was achieved using SVM with unigram and NB with bigram TF-IDF. However, the Long Short-Term Memory (LSTM) model outperformed the other models with an accuracy of 0.95, a precision of 0.96, a recall of 0.95, and an F1 score of 0.95. This model will help in gaining a complete idea of how receptive people are to the vaccine. Thus, the government will be able to find new ways and run more campaigns to raise awareness of the importance of the vaccine.

Effects of Mobile Use on Subjective Sleep Quality.

PURPOSE: The objective of this study was to find out the association between mobile use and physiological parameters of poor sleep quality. It also aimed to find out the prevalence of mobile-related sleep risk factors (MRSRF) and their effects on sleep in mobile users. MATERIALS AND METHODS: This cross-sectional study was conducted on 1925 students (aged 17-23yrs) from multiple Colleges of Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia. The study tools used were Pittsburgh sleep quality index (PSQI) and MRSRF online questionnaires. RESULTS: The mean age (±SD) of participants was 19.91 ± 2.55 years. Average mobile screen usage time was 8.57±4.59/24 hours, whereas average mobile screen usage time in the bed after the lights have been turned off was 38.17±11.7 minutes. Only 19.7% of subjects used airplane mode, while 70% kept the mobile near the pillow while sleeping. The blue light filter feature was used by only 4.2% of the participants. "Screen usage time of ≥8 hours" was positively correlated with sleep disturbances and decrease in the length of actual sleeping time (p =0.023 and 0.022). "Using the mobile for at least 30 minutes (without blue light filter) after the lights have been turned off" showed positive correlation with poor sleep quality, daytime sleepiness, sleep disturbances and increased sleep latency (p= 0.003, 0.004 and 0.001). "Keeping the mobile near the pillow while sleeping" was also positively correlated with daytime sleepiness, sleep disturbances and increased sleep latency (p =0.003, 0.004 and 0.001). CONCLUSION: This study concludes that using mobile screen ≥8 hours/24 hours, using the mobile for at least 30 minutes before sleeping after the lights have been turned off and keeping the mobile near the pillow are positively associated with poor sleep quality. Moreover, we observed that MRSRF were highly prevalent amongst the mobile users.