Search

Objective: To systematically evaluate the effect of Acceptance and Commitment Therapy (ACT) on depressive disorders. Methods: The electronic databases of Web of Science Core Collection, Pubmed, EMBASE, Cochrane Library, PsycInfo, CNKI, Wanfang and Weipu were used to select relevant publications. Screening, data extraction, and quality assessment were undertaken following PRISMA guidelines for preferred reporting of systematic reviews and meta-analysis. RevMan5.4 was used for meta-analysis. Results: 11 studies with a total of 962 patients were included. Random-effects model analysis showed that ACT could effectively reduce the level of depressive symptoms in patients with depressive disorders (SMD = − 1.05, 95% CI: − 1.43–− 0.66, P < 0.00001), improve psychological flexibility (MD = 4.84, 95% CI: 2.70–6.97, P < 0.00001), and have good maintenance effect (SMD = − 0.70, 95% CI: − 1.15–− 0.25, P = 0.002). All differences were statistically significant. Conclusions: ACT not only improves depressive symptoms and psychological flexibility, but also has a good maintenance effect, and it is particularly effective in Chinese patients. Large randomized controlled trials are needed to validate the findings from this meta-analysis. [ABSTRACT FROM AUTHOR]

Background: According to data from the National Health Commission in 2018, more than 30 million children and adolescents in China suffered from mental health problems of varying degrees, with depression accounting for the largest proportion. Life events occur at every stage of child and adolescent development. Many studies have found a relationship between life events and depression in children and adolescents, but few studies have further explored the mediating and moderating variables that influence this relationship. Based on theoretical and empirical research on social support and cognitive styles, this study established a two-stage moderated mediating model to test whether social support mediates life events to depression, and cognitive style moderates the two mediating pathways. Methods: We recruited 3540 participants from primary and secondary schools in Chongqing and collected 2814 valid data. All the participants completed self-report measures of life events, depression, social support and cognitive styles. The moderated mediation model was examined using SPSS PROCESS model 58. Results: (1) There were significant positive correlations between life events and depression. (2) Social support mediates the relationship between life events and depression in children and adolescents. (3) Cognitive style moderates life events to social support and social support to depression. Limitations: This is a cross-sectional study and the questionnaire is self-reported. Conclusions: In children and adolescents, life events can influence depression through the mediating role of social support and cognitive styles could moderate its two mediating pathways. [ABSTRACT FROM AUTHOR]

Background: Although the 2000s observed enormous changes in China, little is known about the variation in the prevalence of mental disorders. The study compared the prevalence in a Chinese population between 2004 and 2015.Methods: Multistage stratified random sampling methods were used to identify primary sampling sites for cross-sectional surveys in 2004 and 2015 in Shandong, China. In 2004 and 2015, 22,718 and 28,194 adults, respectively, completed an expanded version of the General Health Questionnaire, then 5,402 and 9,420 adults, respectively, were administered a Chinese version of the Structured Clinical Interview for Diagnostic and Statistical Manual-IV axis I disorders.Results: The adjusted 1-month prevalence of any mental disorders was 18•7% (95% CI: 16•7-20•8) and 17•1% (95% CI:15•9-18•4) in 2004 and 2015, respectively. However, the prevalence of major depressive disorders increased from 1•5% (95% CI: 1•2 -1•8) in 2004 to 2•3% (95% CI: 1•9-2•8) in 2015; meanwhile the prevalence of alcohol abuse disorders were becoming more common among men and urban residents. Although mood and anxiety disorders were more prevalent in women, a much more prevalent alcohol abuse disorders for men contributed to a higher overall prevalence among men than among women. Compared to that in urban residents, the overall prevalence in rural residents declined more, and it was lower in 2015 than in 2004.Limitations: The results may not apply to the population from other regions.Conclusions: Despite of the stable overall prevalence, mental disorders beyond psychotic disorders should be focused on, especially alcohol abuse and major depressive disorders. [ABSTRACT FROM AUTHOR]

Background: To investigate the mental health status of Chinese residents during the epidemic of COVID-19, as well as to identify the positive and negative factors and regulatory effect of negative cognitive processing bias on mental health. Methods: A total of 60,199 residents in China were surveyed via an internet-based survey containing a general questionnaire, such as the self-rating depression scale, the state anxiety inventory, and the negative cognitive processing bias questionnaire. An ordered multiple logistic regression analysis model was used to analyze the collected data. Results: The survey revealed mild, moderate, and severe depressive symptoms in 62.65, 11.33, and 6.14% participants, respectively, and mild, moderate, and severe anxiety symptoms in 33.21, 41.27, and 22.99% participants, respectively. Multiple logistic regression analysis showed that factors, such as female gender, being older than 55 years, high school education level, medical staff, marital conflicts, negative attention bias, rumination, and death growth rate, positively affected depression and anxiety symptoms. The good family functionality, democratic working atmosphere, and a myriad of social activities negatively affected the level of depressive and anxiety symptoms. Conclusion: Chinese residents exhibited a high prevalence of anxiety and depressive symptoms during the epidemic. Thus, psychological interventions should focus on the vulnerable groups, and cognitive training should focus on reducing the negative cognitive processing bias. This might be an effective way to alleviate the mental stress of the general public during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

This study extends from the 2015 Shandong Province Epidemiological Survey of Mental Disorders in adults aged 18 and above. Over five years, it investigates pain characteristics and influencing factors in individuals with depressive disorders in Shandong Province. The study encompasses 871 individuals who met DSM-IV criteria for depressive disorders in 2015. Using 1:1:1 matching by gender, age, and residence, 825 non-afflicted individuals were selected as high-risk controls, and 825 screening-negative individuals became low-risk controls. A follow-up study in 2020 involved 1848 participants. Survey tools included a general information questionnaire, General Health Questionnaire-12 (GHQ-12), SCID-I/P, Global Pain Scale (GPS), Quality of Life Questionnaire (QLQ), PSQI, MoCA, and clinical data questionnaire. GPS scores in the current depressive group were higher than in non-current depressive group (Z = 14.36 , P < 0.01). GPS scores in study group exceeded those in high-risk and low-risk control groups (H = 93.71 , P < 0.01). GPS scores in non-remission group were higher than in the remission group (Z = 8.90 , P < 0.01). Regression analysis revealed positive correlations between GPS scores and physical illnesses, current depression, incumbency, GHQ-12 total score, and PSQI total score. Negative correlations were observed with QLQ total score and MoCA total score. The study could not assess pain during the 2015 survey, limiting controlled pain analysis before and after five years. Depression sufferers may experience prolonged heightened pain, potentially relieved when depression subsides. Individual pain is influenced by depression, physical illnesses, sleep quality, quality of life, cognitive function, gender, residence, and occupation. • Pain Persists in Depression: Depressed individuals have prolonged pain, even after remission. • Multifactorial Pain Influence: Pain level in depression linked to depression, physical illness, and sleep quality. • Gender and Location Impact: Female gender and rural residence contribute to higher depression-related pain. • Depression Affects Quality of Life: Pain in depression harms quality of life, warranting comprehensive treatment. • Occupational Status Matters: Occupational status, like incumbency, affects pain levels. [ABSTRACT FROM AUTHOR]

Depressive symptoms are a serious public mental health problem, and dietary intake is often considered to be associated with depressive symptoms. However, the relationship between the quality of dietary carbohydrates and depressive symptoms remains unclear. Therefore, this study aimed to investigate the relationship between high and low-quality carbohydrates and depressive symptoms and to attempt to construct an integrated model using machine learning to predict depressive symptoms. A total of 4982 samples from the National Health and Nutrition Examination Survey (NHANES) were included in this study. Carbohydrate intake was assessed by a 24-h dietary review, and depressive symptoms were assessed using the Patient Health Questionnaire-9 (PHQ9). Variance inflation factor (VIF) and Relief-F algorithms were used for variable feature selection. The results of multivariate linear regression showed a negative association between high-quality carbohydrates and depressive symptoms (β: −0.147, 95 % CI: −0.239, −0.056, p = 0.002) and a positive association between low-quality carbohydrates and depressive symptoms (β: 0.018, 95 % CI: 0.007, 0.280, p = 0.001). Subsequently, we used the XGboost model to produce a comprehensive depressive symptom evaluation model and developed a corresponding online tool (http://8.130.128.194:5000/) to evaluate depressive symptoms clinically. The cross-sectional study could not yield any conclusions regarding causality, and the model has not been validated with external data. Carbohydrate quality is associated with depressive symptoms, and machine learning models that combine diet with socioeconomic factors can be a tool for predicting depression severity. • Exploring the association between high and low carbohydrate diet quality and depressive symptom severity • Building an integrated economic society-diet machine learning model for predicting depression severity • Development of a convenient web-based model for predicting depression severity [ABSTRACT FROM AUTHOR]

Background: Depression is characterized by debilitating symptoms and high recurrence rates, and there are relatively few large-scale epidemiological surveys of depressive disorders conducted in Shandong since 2005. Data from the largest Epidemiological Survey of Mental Disorders conducted in 2015 in Shandong were collected to investigate the prevalence of depressive disorders and associated demographic characteristics in general adult population.Methods: A multi-stage stratified cluster sampling method was adopted to select residents and a two-stage screening and assessment process was used to define the prevalence and characteristics of depressive disorders. Respondents were initially screened using the General Health Questionnaire followed by a structured clinical interview using the DSM-IV criteria.Results: Among 27,489 respondents who completed the survey, 1277 respondents met the diagnostic criteria for depressive disorders. The adjusted prevalence in the last month was 4.86%, among which the prevalence of major depressive disorder, dysthymia, and unspecified depressive disorder were 2.32%, 1.78%, and 0.75%, respectively. 40.35% of depression patients had moderate or severe functional impairment and only 10.65% of patients had visited a psychiatric service. Univariate and multivariate analyses revealed that age, gender, occupation, education, marital status, and urban/rural living were associated with the prevalence.Limitations: The key limitation is that this is a cross-sectional survey therefore cannot draw any causal relationship between risk factors and disease progression.Conclusions: Findings from this largest epidemiological study reveal current prevalence of depressive disorders and associated demographic factors and offers opportunities for policy makers and health-care professionals to improve mental health provision in Shandong. [ABSTRACT FROM AUTHOR]

Abstract: Background: In European and US studies, patients with major depressive disorder (MDD) report more stressful life events (SLEs) than controls, but this relationship has rarely been studied in Chinese populations. Methods: Sixteen lifetime SLEs were assessed at interview in two groups of Han Chinese women: 1970 clinically ascertained with recurrent MDD and 2597 matched controls. Diagnostic and other risk factor information was assessed at personal interview. Odds ratios (ORs) were calculated by logistic regression. Results: 60% of controls and 72% of cases reported at least one lifetime SLE. Fourteen of the sixteen SLEs occurred significantly more frequently in those with MDD (median odds ratio of 1.6). The three SLEs most strongly associated with risk for MDD (OR>3.0) preceded the onset of MDD the majority of the time: rape (82%), physical abuse (100%) and serious neglect (99%). Limitations: Our results may apply to females only. SLEs were rated retrospectively and are subject to biases in recollection. We did not assess contextual information for each life event. Conclusions: More severe SLEs are more strongly associated with MDD. These results support the involvement of psychosocial adversity in the etiology of MDD in China. [Copyright &y& Elsevier]