Typical symptom change patterns and their predictors in patients with social anxiety disorder: A latent class analysis

2020 ◽  
Vol 71 ◽  
pp. 102200 ◽  
Author(s):  
Uwe Altmann ◽  
Romina Gawlytta ◽  
Jürgen Hoyer ◽  
Falk Leichsenring ◽  
Eric Leibing ◽  
...  
Keyword(s):  
Anxiety Disorder ◽  
Social Anxiety ◽  
Latent Class Analysis ◽  
Social Anxiety Disorder ◽  
Latent Class ◽  
Class Analysis ◽  
Symptom Change ◽  
Typical Symptom

Latent class analysis of the feared situations of social anxiety disorder: A population-based study

2016 ◽  
Vol 33 (12) ◽  
pp. 1178-1187 ◽  
Author(s):  
Hugo Peyre ◽  
Nicolas Hoertel ◽  
Fabrice Rivollier ◽  
Benjamin Landman ◽  
Kibby McMahon ◽  
...  
Keyword(s):  
Anxiety Disorder ◽  
Social Anxiety ◽  
Latent Class Analysis ◽  
Social Anxiety Disorder ◽  
Latent Class ◽  
Population Based ◽  
Class Analysis ◽  
Population Based Study

Processes in cognitive behavior therapy for social anxiety disorder: Predicting subsequent symptom change

2019 ◽  
Vol 67 ◽  
pp. 102118 ◽  
Author(s):  
Fredrik Santoft ◽  
Sigrid Salomonsson ◽  
Hugo Hesser ◽  
Elin Lindsäter ◽  
Brjánn Ljótsson ◽  
...  
Keyword(s):  
Anxiety Disorder ◽  
Social Anxiety ◽  
Social Anxiety Disorder ◽  
Behavior Therapy ◽  
Cognitive Behavior Therapy ◽  
Cognitive Behavior ◽  
Symptom Change

scholarly journals Cyberbullying and Social Anxiety: A Latent Class Analysis among Spanish Adolescents

2020 ◽  
Vol 17 (2) ◽  
pp. 406 ◽  
Author(s):  
María C. Martínez-Monteagudo ◽  
Beatriz Delgado ◽  
Cándido J. Inglés ◽  
Raquel Escortell
Keyword(s):  
Social Anxiety ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Negative Evaluation ◽  
Class Analysis ◽  
Social Avoidance ◽  
Social Maladjustment ◽  
Social Situations ◽  
Bully Victims ◽  
Student Profiles

Cyberbullying is a common social maladjustment that has negative repercussions on the wellbeing and development of adolescents, but numerous questions remain as to the relationship between cyberbullying and social anxiety in adolescence. This study analyzes cyberbullying profiles (screening of harassment among peers) and assesses whether these profiles vary with respect to the level of social anxiety (social anxiety scale for adolescents). The sample consisted of 1412 Spanish secondary education students aged 12 to 18 (M = 14.36, SD = 1.65). Latent class analysis and ANOVA were performed. Analyses revealed three profiles: high cyberbullying (high victimization, aggression, and aggression-victimization), low cyberbullying (moderate victimization, aggression, and aggression-victimization), and non-cyberbullying. The cyberbullying patterns varied significantly for all social anxiety subscales. Students with the high cyberbullying profile (bully–victims) presented high scores on social avoidance and distress in social situations in general with peers, whereas these students presented lower levels of fear of negative evaluation and distress and social avoidance in new situations as compared to the low cyberbullying (rarely victim/bully) and non-involved student profiles. Implications for psychologists and educational counselors and cyberbullying preventive interventions are discussed.

An examination of generalized anxiety disorder and dysthymic disorder by latent class analysis

2013 ◽  
Vol 44 (8) ◽  
pp. 1701-1712 ◽  
Author(s):  
D. Rhebergen ◽  
I. M. van der Steenstraten ◽  
M. Sunderland ◽  
R. de Graaf ◽  
M. ten Have ◽  
...  
Keyword(s):  
Mental Health ◽  
Anxiety Disorder ◽  
Latent Class Analysis ◽  
Generalized Anxiety Disorder ◽  
Clinical Characteristics ◽  
Latent Class ◽  
Depressive Symptomatology ◽  
Dysthymic Disorder ◽  
Generalized Anxiety ◽  
Class Analysis

BackgroundThe nosological status of generalized anxiety disorder (GAD) versus dysthymic disorder (DD) has been questioned. The aim of this study was to examine qualitative differences within (co-morbid) GAD and DD symptomatology.MethodLatent class analysis was applied to anxious and depressive symptomatology of respondents from three population-based studies (2007 Australian National Survey of Mental Health and Wellbeing; National Comorbidity Survey Replication; and Netherlands Mental Health Survey and Incidence Study-2; together known as the Triple study) and respondents from a multi-site naturalistic cohort [Netherlands Study of Depression and Anxiety (NESDA)]. Sociodemographics and clinical characteristics of each class were examined.ResultsA three-class (Triple study) and two-class (NESDA) model best fitted the data, reflecting mainly different levels of severity of symptoms. In the Triple study, no division into a predominantly GAD or DD co-morbidity subtype emerged. Likewise, in spite of the presence of pure GAD and DD cases in the NESDA sample, latent class analysis did not identify specific anxiety or depressive profiles in the NESDA study. Next, sociodemographics and clinical characteristics of each class were examined. Classes only differed in levels of severity.ConclusionsThe absence of qualitative differences in anxious or depressive symptomatology in empirically derived classes questions the differentiation between GAD and DD.

scholarly journals Does Centrality in a Cross-Sectional Network Suggest Intervention Targets for Social Anxiety Disorder?

2018 ◽  
Author(s):  
Thomas Rodebaugh ◽  
Natasha April Tonge ◽  
Marilyn Piccirillo ◽  
Eiko I Fried ◽  
Arielle Horenstein ◽  
...  
Keyword(s):  
Anxiety Disorder ◽  
Social Anxiety ◽  
Social Anxiety Disorder ◽  
Partial Correlation ◽  
State Of The Art ◽  
Symptom Change ◽  
Cross Sectional ◽  
Network Analyses ◽  
Treatment Data ◽  
Item Properties

Objective: Network analysis allows us to identify the most interconnected (i.e., central) symptoms, and multiple authors have suggested that these symptoms might be important treatment targets. This is because change in central symptoms (relative to others) should have greater impact on change in all other symptoms. It has been argued that networks derived from cross-sectional data may help identify such important symptoms. We tested this hypothesis in social anxiety disorder. Method: We first estimated a state-of-the-art regularized partial correlation network based on participants with social anxiety disorder (N = 910) to determine which symptoms were more central. Next, we tested whether change in these central symptoms were indeed more related to overall symptom change in a separate dataset of participants with social anxiety disorder who underwent a variety of treatments (N = 244). We also tested whether relatively superficial item properties (infrequency of endorsement and variance of items) might account for any effects shown for central symptoms. Results: Centrality indices successfully predicted how strongly changes in items correlated with change in the remainder of the items. Findings were limited to the measure used in the network and did not generalize to three other measures related to social anxiety severity. In contrast, infrequency of endorsement showed associations across all measures. Conclusions: The transfer of recently published results from cross-sectional network analyses to treatment data is unlikely to be straightforward.

Interpersonal Psychotherapy for Social Anxiety Disorder

2012 ◽  
pp. 169-184 ◽  
Author(s):  
Joshua D Lipsitz
Keyword(s):  
Differential Diagnosis ◽  
Anxiety Disorder ◽  
Social Anxiety ◽  
Medical History ◽  
Empirical Evidence ◽  
Social Anxiety Disorder ◽  
Interpersonal Psychotherapy ◽  
Symptom Change ◽  
Case Discussion ◽  
Change State

Chapter 10 discusses interpersonal psychotherapy for social anxiety disorder, and covers interpersonal problem areas, stages of IPT, assessing symptom change, state of the empirical evidence, and a case example showing personal, psychiatric, family, and medical history, differential diagnosis and indication for IPT, IPT formulation, and case discussion.

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