Emotional dysfunction in avoidant personality disorder and borderline personality disorder:A cross-sectional comparative study

According to the literature, avoidant personality disorder (APD) is often overlooked in research on personality disorders. In the present study, patients with APD were compared to patients with borderline personality disorder (BPD) with respect to emotional dysfunction. Emotional dysfunction was operationalized through the Affect Integration Inventory. Sixty-one patients receiving treatment at specialized outpatient hospital facilities for either BPD (n = 25) or APD (n = 36) (Diagnostic and Statistical Manual of Mental Disorders, fifth edition) were included in a cross-sectional study. Supporting our expectations of no difference in the global capacity for affect integration between groups, the estimated difference was 0.00 (95% confidence interval [CI] [−0.53, 0.53]). On the other hand, the expected increased dysfunction in APD regarding Expression could not be confirmed. Furthermore, problems with specific affects distinguished the groups; integration of Interest was worse in APD (p = 0.01), whereas integration of Jealousy was worse in BPD (p = 0.04). In terms of prototypical modes of experiencing affects, APD was characterized by decreased access to the motivational properties of Interest (p < 0.01), while BPD was more driven by Interest (p < 0.01), Anger (p < 0.01), and Jealousy (p = 0.01). In conclusion, even though the two disorders are characterized by similar overall levels of emotional dysfunction, they differ systematically and predictably regarding specific affects and modes of experiencing. These findings carry implications for the understanding of emotional dysfunction in APD and BPD, suggesting specific areas of emotional dysfunction that could be targeted in tailored psychotherapeutic interventions

The Relationship between Affect Integration and Psychopathology in Patients with Personality Disorder: A Cross-Sectional Study

Background and Objectives: Emotional dysfunction is considered a key component in personality disorders; however, only few studies have examined the relationship between the two. In this study, emotional dysfunction was operationalized through the Affect Integration Inventory, and the aim was to examine the relationships between the level of affect integration and the levels of symptom distress, interpersonal problems, and personality functioning in patients diagnosed with personality disorder according to the Diagnostic and Statistical Manual of Mental Disorders, fifth edition. Materials and Methods: Within a hospital-based psychiatric outpatient setting, 87 patients with personality disorder referred for treatment were identified for assessment with the Affect Integration Inventory and other measures (e.g., the Symptom Checklist-90, Revised, the Inventory of Interpersonal Problems 64 circumplex version, and the Severity Indices of Personality Problems). Results: The analyses revealed that problems with affect integration were strongly and statistically significantly correlated with high levels of symptom distress, interpersonal problems, and maladaptive personality functioning. Additionally, low scores on the Affect Integration Inventory regarding discrete affects were associated with distinct and differentiated patterns of interpersonal problems. Conclusion: Taken together, emotional dysfunction, as measured by the Affect Integration Inventory, appeared to be a central component of the pathological self-organization associated with personality disorder. These findings have several implications for the understanding and psychotherapeutic treatment of personality pathology. Furthermore, they highlight the importance of considering the integration of discrete affects and their specific contributions in the conceptualization and treatment of emotional dysfunction in patients with personality disorders

Text Topics and Treatment Response in Internet-Delivered Cognitive Behavioral Therapy for Generalized Anxiety Disorder : Text Mining Study

Publisher Copyright: © 2022 Sanna Mylläri, Suoma Eeva Saarni, Ville Ritola.Background: Text mining methods such as topic modeling can offer valuable information on how and to whom internet-delivered cognitive behavioral therapies (iCBT) work. Although iCBT treatments provide convenient data for topic modeling, it has rarely been used in this context. Objective: Our aims were to apply topic modeling to written assignment texts from iCBT for generalized anxiety disorder and explore the resulting topics' associations with treatment response. As predetermining the number of topics presents a considerable challenge in topic modeling, we also aimed to explore a novel method for topic number selection. Methods: We defined 2 latent Dirichlet allocation (LDA) topic models using a novel data-driven and a more commonly used interpretability-based topic number selection approaches. We used multilevel models to associate the topics with continuous-valued treatment response, defined as the rate of per-session change in GAD-7 sum scores throughout the treatment. Results: Our analyses included 1686 patients. We observed 2 topics that were associated with better than average treatment response: "well-being of family, pets, and loved ones"from the data-driven LDA model (B=-0.10 SD/session/Δtopic; 95% CI -016 to -0.03) and "children, family issues"from the interpretability-based model (B=-0.18 SD/session/Δtopic; 95% CI -0.31 to -0.05). Two topics were associated with worse treatment response: "monitoring of thoughts and worries"from the data-driven model (B=0.06 SD/session/Δtopic; 95% CI 0.01 to 0.11) and "internet therapy"from the interpretability-based model (B=0.27 SD/session/Δtopic; 95% CI 0.07 to 0.46). Conclusions: The 2 LDA models were different in terms of their interpretability and broadness of topics but both contained topics that were associated with treatment response in an interpretable manner. Our work demonstrates that topic modeling is well suited for iCBT research and has potential to expose clinically relevant information in vast text data.Peer reviewe