MATERIALS AND METHODS: This is an observational cross-sectional study. A protocol gathering sociodemographic variables as well as depression, anxiety and suicidality and conspiracism was assembled, and data were collected anonymously and online from April 2020 through March 2021. The sample included 12,488 subjects from 11 countries, of whom 9,026 were females (72.2%; aged 21.11 ± 2.53), 3,329 males (26.65%; aged 21.61 ± 2.81) and 133 "non-binary gender" (1.06%; aged 21.02 ± 2.98). The analysis included chi-square tests, correlation analysis, ANCOVA, multiple forward stepwise linear regression analysis and Relative Risk ratios.
RESULTS: Dysphoria was present in 15.66% and probable depression in 25.81% of the total study sample. More than half reported increase in anxiety and depression and 6.34% in suicidality, while lifestyle changes were significant. The model developed explained 18.4% of the development of depression. Believing in conspiracy theories manifested a complex effect. Close to 25% was believing that the vaccines include a chip and almost 40% suggested that facemask wearing could be a method of socio-political control. Conspiracism was related to current depression but not to history of mental disorders.
DISCUSSION: The current study reports that students are at high risk for depression during the COVID-19 pandemic and identified specific risk factors. It also suggested a role of believing in conspiracy theories. Further research is important, as it is targeted intervention in students' groups that are vulnerable both concerning mental health and conspiracism.
METHODS: Initially, after 2 weeks of in-patient detoxification, 120 patients with alcohol use disorder will be randomized into three groups (VRET, ACT, and TAU control groups) via stratified permuted block randomization in a 1:1:1 ratio. Baseline assessment (t0) commences, whereby all the participants will be administered with sociodemographic, clinical, and alcohol use characteristics questionnaire, such as Alcohol Use Disorder Identification Test (AUDIT), Penn Alcohol Craving Scale (PACS), Hamilton Anxiety Rating Scale (HAM-A), and Hamilton Depression Rating Scale (HAM-D), while event-related potential (ERP) detection in electroencephalogram (EEG) will also be carried out. Then, 4 weeks of VRET, ACT, and non-therapeutic supportive activities will be conducted in the three respective groups. For the subsequent three assessment timelines (t1, t2, and t3), the alcohol use characteristic questionnaire, such as AUDIT, PACS, HAM-D, HAM-A, and ERP monitoring, will be re-administered to all participants.
DISCUSSION: As data on the effects of non-pharmacological interventions, such as VRET and ACT, on the treatment of alcohol craving and preventing relapse in alcohol use disorder are lacking, this RCT fills the research gap by providing these important data to treating clinicians. If proven efficacious, the efficacy of VRET and ACT for the treatment of other substance use disorders should also be investigated in future.
CLINICAL TRIAL REGISTRATION: NCT05841823 (ClinicalTrials.gov).
METHOD: A cross-sectional study using convenience sampling recruited 230 caregivers of children with ASD aged 4 to 18 years from selected autism centres in Kuching, Sarawak. The caregivers completed the Aberrant Behaviour Checklist-2 and the Zarit Burden Interview.
RESULTS: Univariate analysis revealed a significant difference in caregiver burden for children with ASD receiving medications (p = 0.013), registered with the Social Welfare Department (p = 0.036), and having siblings with ASD (p = 0.046). About 40% of the children exhibited at least one domain of problem behaviour. More than half of the caregivers (53.9%) experienced burden, with the majority experiencing mild burden. Positive associations were seen between irritability (r = 0.458, p
METHODS: An online survey comprising the YFAS 2.0, mYFAS 2.0, Weight Self-Stigma Questionnaire (WSSQ) and International Physical Activity Questionnaire-Short Form (IPAQ-SF) were used to assess food addiction, self-stigma, and physical activity.
RESULTS: All participants (n = 687; mean age = 24.00 years [SD ± 4.48 years]; 407 females [59.2%]) completed the entire survey at baseline and then completed the YFAS 2.0 and mYFAS 2.0 again three months later. The results of confirmatory factor analysis (CFA) indicated that the YFAS 2.0 and mYFAS 2.0 both shared a similar single-factor solution. In addition, both the YFAS 2.0 and mYFAS 2.0 reported good internal consistency (Cronbach's α = 0.90 and 0.89), good test-retest reliability (ICC = 0.71 and 0.69), and good concurrent validity with the total scores being strongly associated with the WSSQ (r = 0.54 and 0.57; p < 0.01), and less strongly associated with BMI (r = 0.17 and 0.13; p < 0.01) and IPAQ-SF (r = 0.23 and 0.25; p < 0.01).
DISCUSSION: Based on the findings, the Taiwan versions of the YFAS 2.0 and mYFAS 2.0 appear to be valid and reliable instruments assessing food addiction.
METHODS: We quantified the directed information flow using partial directed coherence (PDC) and the topological networks by graph theory measures at four frequency bands (delta, theta, alpha, and beta). The PDC assesses the causal interactions between neuronal units of the brain network. Besides, the graph theory of the complex network identifies the topological structure of the network. Resting-state electroencephalogram (EEG) data were recorded for 66 patients with different severities of SAD (22 severe, 22 moderate, and 22 mild) and 22 demographically matched healthy controls (HC).
RESULTS: PDC results have found significant differences between SAD groups and HCs in theta and alpha frequency bands (p < 0.05). Severe and moderate SAD groups have shown greater enhanced information flow than mild and HC groups in all frequency bands. Furthermore, the PDC and graph theory features have been used to discriminate three classes of SAD from HCs using several machine learning classifiers. In comparison to the features obtained by PDC, graph theory network features combined with PDC have achieved maximum classification performance with accuracy (92.78%), sensitivity (95.25%), and specificity (94.12%) using Support Vector Machine (SVM).
DISCUSSION: Based on the results, it can be concluded that the combination of graph theory features and PDC values may be considered an effective tool for SAD identification. Our outcomes may provide new insights into developing biomarkers for SAD diagnosis based on topological brain networks and machine learning algorithms.