Body dysmorphic disorder

AccessScience ◽  
2015 ◽  
Keyword(s):  
Body Dysmorphic Disorder

Try Group CBT for Body Dysmorphic Disorder

2010 ◽  
Vol 38 (10) ◽  
pp. 19-20
Author(s):  
CAROLINE HELWICK
Keyword(s):  
Body Dysmorphic Disorder ◽  
Group Cbt

Body Dysmorphic Disorder in the Bluest Eye

2012 ◽  
Vol 11 (Supplement) ◽  
Author(s):  
Liana N. Beian
Keyword(s):  
Body Dysmorphic Disorder ◽  
Bluest Eye

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

2019 ◽  
Author(s):  
Oskar Flygare ◽  
Jesper Enander ◽  
Erik Andersson ◽  
Brjánn Ljótsson ◽  
Volen Z Ivanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Clinical Utility ◽  
Body Dysmorphic Disorder ◽  
Prediction Models ◽  
Behavioral Therapy ◽  
Learning Approach ◽  
Learning Approaches ◽  
Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

scholarly journals Psychiatric Assessment and Management of Clients Undergoing Cosmetic Surgery: Overview and Need for an Integrated Approach

2021 ◽  
Author(s):  
Sharmi Bascarane ◽  
Pooja P. Kuppili ◽  
Vikas Menon
Keyword(s):  
Mental Health ◽  
Cosmetic Surgery ◽  
English Language ◽  
Body Dysmorphic Disorder ◽  
Psychiatric Morbidity ◽  
Integrated Approach ◽  
Assessment Tools ◽  
Health Issues ◽  
Psychiatric Assessment ◽  
Postsurgical Outcomes

Abstract Background Psychiatric disorders are more common among people undergoing cosmetic procedures than the general population and evaluating mental health can be cumbersome for plastic surgeons. We aim to summarize the available literature in this regard and propose an integrated approach to psychiatric assessment and management of mental health issues among this group. Methods Electronic search of MEDLINE, Google Scholar, and PsycINFO databases was done to identify relevant peer-reviewed English language articles from inception till April 2020. Generated abstracts were screened for their eligibility. Included articles were grouped according to their thematic focus under the following headings; prevalence of psychiatric morbidity among clients posted for cosmetic surgery, assessment tools, and management of psychiatric morbidity in relation to undergoing cosmetic surgery. Results A total of 120 articles were reviewed. The prevalence of psychiatric disorder in patients undergoing cosmetic surgery was 4 to 57% for body dysmorphic disorder (BDD); the corresponding figures for depression, anxiety, and personality disorder were 4.8 to 25.8, 10.8 to 22, and 0 to 53%, respectively. A range of tools have been used to assess these disorders and specific measures are also available to assess clinical outcomes following surgery. Screening for these disorders is essential to prevent unnecessary surgical procedures, as well as to ensure timely management of the psychiatric comorbidity. Conclusion Psychiatric morbidity is a common concomitant in cosmetic surgery. A structured and integrated approach to evaluation and management of psychiatric morbidity will help to optimize postsurgical outcomes.

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