Prevalence and influencing factors of pandemic fatigue among Chinese public in Xi'an city during COVID-19 new normal: a cross-sectional study.

Objective: This study aimed to assess Chinese public pandemic fatigue and potential influencing factors using an appropriate tool and provide suggestions to relieve this fatigue. Methods: This study used a stratified sampling method by age and region and conducted a cross-sectional questionnaire survey of citizens in Xi'an, China, from January to February 2022. A total of 1500 participants completed the questionnaire, which collected data on demographics, health status, coronavirus disease 2019 (COVID-19) stressors, pandemic fatigue, COVID-19 fear, COVID-19 anxiety, personal resiliency, social support, community resilience, and knowledge, attitude, and practice toward COVID-19. Ultimately, 1354 valid questionnaires were collected, with a response rate of 90.0%. A binary logistic regression model was used to examine associations between pandemic fatigue and various factors. Result: Nearly half of the participants reported pandemic fatigue, the major manifestation of which was "being sick of hearing about COVID-19" (3.353 ± 1.954). The logistic regression model indicated that COVID-19 fear (OR = 2.392, 95% CI = 1.804-3.172), sex (OR = 1.377, 95% CI = 1.077-1.761), the pandemic's impact on employment (OR = 1.161, 95% CI = 1.016-1.327), and COVID-19 anxiety (OR = 1.030, 95% CI = 1.010-1.051) were positively associated with pandemic fatigue. Conversely, COVID-19 knowledge (OR = 0.894, 95% CI = 0.837-0.956), COVID-19 attitude (OR = 0.866, 95% CI = 0.827-0.907), COVID-19 practice (OR = 0.943, 95% CI = 0.914-0.972), community resiliency (OR = 0.978, 95% CI = 0.958-0.999), and health status (OR = 0.982, 95% CI = 0.971-0.992) were negatively associated with pandemic fatigue. Conclusion: The prevalence of pandemic fatigue among the Chinese public was prominent. COVID-19 fear and COVID-19 attitude were the strongest risk factors and protective factors, respectively. These results indicated that the government should carefully utilize multi-channel promotion of anti-pandemic policies and knowledge.

Online Learning Performances of Children and Adolescents With Attention Deficit Hyperactivity Disorder During the COVID-19 Pandemic.

To investigate attention deficit hyperactivity disorder (ADHD) core symptoms that impair executive function (EF), emotional state, learning motivation, and the family and parenting environment of children and adolescents with ADHD, both with and without severe difficulties. This will be explored within an online learning environment during the period of COVID-19 pandemic. A total of 183 ADHD children diagnosed using DSM-V criteria were selected and divided into 2 groups high difficulties during online learning (HDOL) and low difficulties during online learning (LDOL) according to the answer of Home Quarantine Investigation of the Pandemic (HQIP). The participants filled out a set of questionnaires to assess their emotional state and learning motivation, and their parents also filled out the questionnaires about ADHD core symptoms, EF, and family and parenting environment. Compared with ADHD children in the LDOL group, the children in the HDOL group had significant symptoms of inattention, hyperactivity, oppositional defiant, behavioral and emotional problems according to the Swanson, Nolan, and Pelham Rating Scale (SNAP). They also had more severely impaired EF according to the Behavior Rating Inventory of Executive Function (BRIEF), more difficulties and disturbances in the family by the Chinese version of Family Environment Scale (FES-CV), and lower parenting efficacy and satisfaction by Parenting Sense of Competence (PSOC). With regard to the self-rating questionnaires of children and adolescents, the HDOL group reported lower learning motivation according to the Students Learning Motivation Scale (SLMS). By Screening for Child Anxiety-Related Emotional Disorders and Depression Self-Rating Scale for Children (DSRSC), those in HDOL presented more negative emotions. The HDOL group spent significantly more time on both video games and social software per day and significantly less time on multiple activities per week, when compared to those in the LDOL group. This study demonstrated that ADHD children and adolescents with HDOL had more inattention-related behaviors, more severe emotional problems and EF impairment, weaker learning motivation, and poorer family and parenting environment. Meanwhile, digital media use should be supervised and appropriate extracurricular activities should be encouraged by parents and schools.

Influences of digital media use on children and adolescents with ADHD during COVID-19 pandemic.

OBJECTIVE: To explore the influences of digital media use on the core symptoms, emotional state, life events, learning motivation, executive function (EF) and family environment of children and adolescents diagnosed with attention deficit hyperactivity disorder (ADHD) during the novel coronavirus disease 2019 (COVID-19) pandemic. METHOD: A total of 192 participants aged 8-16 years who met the diagnostic criteria for ADHD were included in the study. Children scoring higher than predetermined cut-off point in self-rating questionnaires for problematic mobile phone use (SQPMPU) or Young's internet addiction test (IAT), were defined as ADHD with problematic digital media use (PDMU), otherwise were defined as ADHD without PDMU. The differences between the two groups in ADHD symptoms, EF, anxiety and depression, stress from life events, learning motivation and family environment were compared respectively. RESULTS: When compared with ADHD group without PDMU, the group with PDMU showed significant worse symptoms of inattention, oppositional defiant, behavior and emotional problems by Swanson, Nolan, and Pelham Rating Scale (SNAP), more self-reported anxiety by screening child anxiety-related emotional disorders (SCARED) and depression by depression self-rating scale for children (DSRSC), more severe EF deficits by behavior rating scale of executive function (BRIEF), more stress from life events by adolescent self-rating life events checklist (ASLEC), lower learning motivation by students learning motivation scale (SLMS), and more impairment on cohesion by Chinese version of family environment scale (FES-CV). The ADHD with PDMU group spent significantly more time on both video game and social media with significantly less time spend on physical exercise as compared to the ADHD without PDMU group. CONCLUSION: The ADHD children with PDMU suffered from more severe core symptoms, negative emotions, EF deficits, damage on family environment, pressure from life events, and a lower motivation to learn. Supervision of digital media usage, especially video game and social media, along with increased physical exercise, is essential to the management of core symptoms and associated problems encountered with ADHD.

Toward the Impact of Non-pharmaceutical Interventions and Vaccination on the COVID-19 Pandemic With Time-Dependent SEIR Model.

The outbreak of COVID-19, caused by the SARS-CoV-2 coronavirus, has been declared a pandemic by the World Health Organization (WHO) in March, 2020 and rapidly spread to over 210 countries and territories around the world. By December 24, there are over 77M cumulative confirmed cases with more than 1.72M deaths worldwide. To mathematically describe the dynamic of the COVID-19 pandemic, we propose a time-dependent SEIR model considering the incubation period. Furthermore, we take immunity, reinfection, and vaccination into account and propose the SEVIS model. Unlike the classic SIR based models with constant parameters, our dynamic models not only predicts the number of cases, but also monitors the trajectories of changing parameters, such as transmission rate, recovery rate, and the basic reproduction number. Tracking these parameters, we observe the significant decrease in the transmission rate in the U.S. after the authority announced a series of orders aiming to prevent the spread of the virus, such as closing non-essential businesses and lockdown restrictions. Months later, as restrictions being gradually lifted, we notice a new surge of infection emerges as the transmission rates show increasing trends in some states. Using our epidemiology models, people can track, timely monitor, and predict the COVID-19 pandemic with precision. To illustrate and validate our model, we use the national level data (the U.S.) and the state level data (New York and North Dakota), and the resulting relative prediction errors for the infected group and recovered group are mostly lower than 0.5%. We also simulate the long-term development of the pandemic based on our proposed models to explore when the crisis will end under certain conditions.