Background: Difficulties in emotion regulation (DER) are widely considered to underlie anxiety and depression. Given the prevalence of anxiety and depression in adolescents and the fact that adolescence is a key period for the development of emotion regulation ability, it is important to examine how DER is related to anxiety and depression in adolescents in clinical settings. Methods: In the present study, we assessed 209 adolescents in clinical settings using the Difficulties in Emotion Regulation Scale (DERS) and the Hospital Anxiety and Depression Scale (HADS) and examined the associations between six components of DER and 14 symptoms of anxiety and depression. We used network analysis, constructed circular and multidimensional scaling (MDS) networks, and calculated network centrality, bridge centrality, and stability of centrality indices. Results: The results showed that: (1) The global centrality index shows that the Strategy component (i.e., lack of access to strategies) is the center in the whole network, ranking highest in strength, closeness, betweenness, and expected influence. (2) The MDS network showed a closeness of anxiety and depression symptoms, while Awareness component (i.e., lack of emotional awareness) stayed away from other DER components, but Awareness is close to some depression symptoms. (3) The bridge nodes of three groups, Strategy from DERS, Worry and Relax from anxiety symptoms, and Cheerful and Slow from depression symptoms, had the strongest relationships with the other groups. Conclusion: Lack of access to strategies remains in the center not only in DER but also in the DER-anxiety-depression network, while lack of awareness is close to depression but not to anxiety. Worrying thoughts and inability to relax are the bridging symptoms for anxiety, while lack of cheerful emotions and slowing down are the bridging symptoms for depression. These findings suggest that making emotion regulation strategies more accessible to patients and reducing these bridging symptoms may yield the greatest rewards for anxiety and depression therapy.

Artificial Intelligence (AI) appears to be making important advances in the prediction and diagnosis of mental disorders. Researchers have used visual, acoustic, verbal, and physiological features to train models to predict or aid in the diagnosis, with some success. However, such systems are rarely applied in clinical practice, mainly because of the many challenges that currently exist. First, mental disorders such as depression are highly subjective, with complex symptoms, individual differences, and strong socio-cultural ties, meaning that their diagnosis requires comprehensive consideration. Second, there are many problems with the current samples, such as artificiality, poor ecological validity, small sample size, and mandatory category simplification. In addition, annotations may be too subjective to meet the requirements of professional clinicians. Moreover, multimodal information does not solve the current challenges, and within-group variations are greater than between-group characteristics, also posing significant challenges for recognition. In conclusion, current AI is still far from effectively recognizing mental disorders and cannot replace clinicians’ diagnoses in the near future. The real challenge for AI-based mental disorder diagnosis is not a technical one, nor is it wholly about data, but rather our overall understanding of mental disorders in general.

Background: Adolescence is a pivotal stage vulnerable to mental health problems such as anxiety and depression. Although self-acceptance and social comparison are known to affect adolescent mental health, their interactive and moderating roles are not fully understood. Aims: To explore the role of self-acceptance, social comparison and attributional style in predicting these mental health outcomes among adolescents in clinical settings. Method: A cross-sectional study was conducted on a sample of 242 adolescents. Participants completed measures assessing self-acceptance, social comparison, attributional style and mental health outcomes (depression and anxiety). Mediation models and multi-group analysis were used to examine the relationships among these variables. Results: Our findings demonstrated a significant relationship between self-acceptance, social comparison, depression and anxiety (rs = 0.32–0.88). Specifically, lower self-acceptance and higher social comparison were associated with higher levels of depression and anxiety. Additionally, individuals with external attributional tendencies reported higher depression (Cohen's d = 0.61) and anxiety (d = 0.58) compared with those with internal tendencies. Mediation modelling showed that social comparison is a mediator between self-acceptance and depression (effect size −0.04, 95% CI −0.08 to −0.01) and anxiety (effect size −0.06, 95% CI −0.10 to −0.02). Crucially, multi-group analysis showed that the impact of social comparison on mental health outcomes varied significantly based on attributional style. Conclusions: These findings underscore the importance of considering self-acceptance, social comparison and attributional style in understanding and addressing mental health challenges during adolescence. This could inform the development of targeted interventions to promote mental health and well-being among adolescents. However, further research is needed to confirm these findings in diverse populations and to explore the underlying mechanisms in greater detail.

Background: In recent years, depression in early adulthood has become an urgent global public health concern. The university years mark a transitional period from adolescence to adulthood. Young people are required to face academic and life pressures independently, which increases the risk of mental health problems in university.Purpose: The main goal of the current study was to explore the sex differences in depression, body dissatisfaction, sleep quality, and exercise intensity among university students in southern China and to analyze the factors affecting the level of depression among university students.Methods: In total, 1,258 university students aged 18–23 years were recruited for this study. All participants completed anthropometric measurements, the Self-rating Depression Scale, Physical Activity Rating Scale, and Pittsburgh Sleep Quality Index. Body dissatisfaction levels were measured using sex-appropriate silhouettes.Results: Compared with young women, young men had higher exercise intensity and sleep quality, whereas young women’s body dissatisfaction and depression levels were significantly higher than those of young men. Sleep quality score (β = 0.34, p < 0.01), sex (β = 0.15, p < 0.01), physical activity score (β = −0.14, p < 0.01), and body dissatisfaction (β = 0.14, p < 0.01) were significant predictive factors of the Self-rating Depression Scale score.Conclusion: Low levels of physical dissatisfaction have a positive effect on depression, and high levels of physical activity and quality sleep can also improve depressive symptoms. At the same time, increasing body satisfaction has the effect of increasing physical activity and improving sleep quality. Therefore, there is great potential to prevent and ameliorate depression by reducing body dissatisfaction.

Left-behind children(LBC) in rural China are at increased risk for mental health problems, including depression. This study aimed to estimate the prevalence of depression and identify key associate factors among Chinese rural LBC. A cross-sectional study was conducted using data from 36,612 LBC aged 6 to 18 years old across 596 data collection sites in Nanchong, Sichuan Province, China. Participants completed questionnaires assessing individual factors, family parenting situation, living events, and health-related data. Depression was measured using the Center for Epidemiological Studies-Depression Scale (CES-D). T-tests, chi-square, and logistic regression were performed to identify factors associated with depression. The overall prevalence of depression among LBC was 6.75%. Logistic regression analysis revealed that family parenting situations, such as being from blended families (OR = 1.45) or being cared for by other family members (OR = 1.64), and dissatisfaction with the parenting situation (OR = 1.57) were significantly associated with higher odds of depression. Living events, including being misunderstood (OR = 1.82) and having disputes (OR = 1.48), and health-related factors, such as chronic diseases and regular medication use (OR = 2.38), also increased the risk of depression. This study highlights the high prevalence of depression among Chinese rural LBC and identifies key associate factors, including family parenting situation, negative living events and health-related factors. Targeted interventions and policies addressing these factors are needed to promote the mental health of this vulnerable population.