Journal of Anxiety Disorders最新文献

Social anxiety disorder (SAD) affects up to 1 in 8 individuals over their lifetime and is characterized by an intense fear of social situations involving unfamiliar people or possible scrutiny. This retrospective observational study reviewed published literature from PubMed and analyzed data from Reddit using social media listening (SML) to understand the lived experience of individuals with SAD. SML offers a complement to traditional methods such as semi-structured interviews, particularly for individuals for whom interacting with strangers is challenging. A stepwise analysis, aligned with US Food and Drug Administration Patient-Focused Drug Development guidelines, was performed to develop a conceptual model for SAD focused on adolescents and young adults. A conceptual model provides a visual overview of the interrelationships between disease-related symptoms and their impact from patients' perspective. Natural language processing techniques and machine learning approaches were employed to extract symptoms and impacts from SML posts. After a targeted literature search, eleven qualitative research publications and 535,544 posts from 118,040 Reddit users were included. Clinical and patient experts then confirmed the content in the conceptual model covering three key symptom domains (physical, negative automatic thoughts, and emotions) and two impact domains (social functioning and occupational/educational functioning). This study captures the value of SML by eliciting spontaneous insights that may not emerge in traditional approaches and proposes a comprehensive conceptual model to support future research and the optimization of Clinical Outcome Assessments in SAD clinical trials.

Climate anxiety has emerged as a significant global psychological and social response to climate change, potentially shaping public engagement and support for climate-related technologies and policies. Here we develop a framework for analyzing online climate anxiety using social media data from China based on 177,232 geo-referenced Weibo posts from 2010 to 2024. The analysis began with the investigation of climate anxiety themes using climate-anxious dictionaries and machine learning methods. Next, the emotional intensity of climate anxiety was assessed through the semantic similarity-based scoring approach. Finally, statistical models were applied to measure the factors influencing climate anxiety. Four major findings are arrived. First, extreme weather events (52.36 %) and livelihood and resource insecurity (22.87 %) were the most discussed and concerning themes, with a notable increase in discussions during summer and autumn. Second, the intensity of climate anxiety has risen significantly. The average intensity increased from 4.42 during the period of 2010-2017 to 7.08 during 2018-2024, with a further notable rise to 7.49 in the more recent period from 2020 to 2024. Third, regions such as Beijing (8.70), Guangdong (8.31), and Zhejiang (7.94) exhibited the highest levels of climate anxiety. Fourth, the intensity of climate anxiety is associated with key demographic and regional factors. Specifically, younger individuals and those residing in climate-vulnerable or informationally developed regions exhibited stronger emotional responses. The framework provides a scalable method for tracking the spatiotemporal dynamics of collective climate anxiety online. The findings demonstrate that digital expressions of climate anxiety constitute a measurable indicator of public concern and carry significant implications for anticipating societal responses and designing targeted communication within climate governance.

The identification of predictors of treatment response in cognitive behavioural therapy (CBT) for social anxiety disorder (SAD) could help to improve treatment outcomes. This study sought to identify which specific social anxiety symptoms measured pre-treatment were associated with post-treatment outcomes. A pre-registered retrospective cohort study was conducted including a sample of N = 1315 patients treated with CBT for SAD in routine clinical practice. The sample was partitioned into training and test subsets (random 50:50 split) to evaluate the model's out-of-sample performance. The outcome was a reliable and clinically significant improvement (RCSI) in anxiety after treatment. A Bayesian network model based on symptoms measured by the Social Phobia Inventory (SPIN) was developed in the training sample (n = 658) using 10-fold cross-validation for variable selection. Predictive accuracy was evaluated in an external test sample (n = 657) using an area under the receiver operating characteristic curve (AUC). Predictive accuracy reduced when comparing performance in the training (AUC = 0.67) to test samples (AUC = 0.58) with moderate out-of-sample prediction shrinkage (6.76 %). The variable selection process yielded a network of four key interrelated SAD symptoms: avoiding public speaking, fear of embarrassment, avoiding criticism and fear of being observed. Identifying important SAD symptoms at assessment enables these to be targeted during CBT to potentially help maximise treatment efficiency and effectiveness.

Background: Social interactions and stable individual differences shape an individual's sense of belongingness -a core need of being accepted by others. Indeed, a sense of belonging is postulated-and found-to affect and be affected by socializing. A diminished sense of belonging and reduced socializing characterizes social anxiety (SA). However, the impact of SA on the temporal dynamics between socializing and belongingness remains unclear.

Method: Using ecological momentary assessment (EMA), we examined the impact of SA on socializing-belongingness dynamics. Participants (N = 116) provided real-time reports on social interactions (three times a day) and belongingness (five times a day) across a 15-17-day period.

Results: SA moderated the relationship between socializing and belongingness. Individuals with low SA exhibited a maximizing pattern, with belongingness increasing continuously as socializing increased. In contrast, individuals with high SA showed a satiation effect, in which belongingness plateaued after a certain amount of socializing. Finally, both SA and socializing prospectively predicted perceived belongingness.

Conclusions: Our findings reveal person-specific dynamics of belongingness, with high levels of SA associated with reduced benefits from social engagement. These findings suggest that enhancing social opportunities and assisting individuals in recognizing optimal levels of socializing may jointly contribute to more fulfilling intra- and interpersonal functioning in SA.