Objective: To assess and compare lifetime rates of occurrence of eating disorders (ED) with four Axis II personality disorders (PD) and with major depressive disorder (MDD) without PD. The eating disorders met criteria outlined in the 4th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV).
Method: Six hundred sixty-eight patients recruited for the Collaborative Longitudinal Personality Disorders Study (CLPS) were reliably assessed with the Structured Clinical Interview for DSM-IV Axis I Disorders and the Diagnostic Interview for DSM-IV Personality Disorders. The distribution of ED diagnoses was compared among four PD study groups (schizotypal, borderline, avoidant, obsessive-compulsive) and a fifth study group with MDD without any PD.
Results: The distribution of lifetime diagnoses of anorexia nervosa (N = 40), bulimia nervosa (N = 56), and eating disorder not otherwise specified (N = 118) did not differ significantly across the five study groups, between the MDD group versus all PD groups, and among the four PD study groups.
Conclusions: ED diagnoses did not differentially co-occur significantly across common Axis I and II disorders. The pattern of ED lifetime co-occurrence rates demonstrates the powerful influence of base rates and highlights that declarations of comorbidity demand significant variations from base-rate patterns.
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http://dx.doi.org/10.1002/eat.10123 | DOI Listing |
Int J Eat Disord
January 2025
Department of Psychiatry, University of California San Diego Health, San Diego, California, USA.
Objective: Outcomes for low-weight restrictive eating disorders, including anorexia nervosa, restricting type (AN-R) and avoidant/restrictive food intake disorder (ARFID), are sub-optimal. Reducing dietary restriction is a key treatment target. Understanding heterogeneity in patterns of change in dietary restriction may aid in improving outcomes.
View Article and Find Full Text PDFMindfulness (N Y)
May 2024
Department of Pediatrics, UCI School of Medicine, University of California Irvine, 3800 W. Chapman Ave, Suite 2200, Orange, CA 92868, USA.
Objectives: Cardiometabolic health during pregnancy has potential to influence long-term chronic disease risk for both mother and offspring. Mindfulness practices have been associated with improved cardiometabolic health in non-pregnant populations. The objective was to evaluate diverse studies that explored relationships between prenatal mindfulness and maternal cardiometabolic health.
View Article and Find Full Text PDFNurs Child Young People
January 2025
Imperial College London, London, England.
Background: Nasogastric tube (NGT) feeding under physical restraint is a clinical intervention that may be required when a child or young person is medically unstable secondary to restrictive eating.
Aim: To explore the experiences of parents when their child receives NGT feeding under physical restraint and understand the effects of this on them.
Method: This is a secondary analysis of data from two previous studies on NGT feeding under physical restraint - one in mental health wards and one in children's wards - in which semi-structured interviews had been conducted with patients, staff and parents.
Int J Eat Disord
January 2025
Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Objective: Self-report measures of ARFID symptoms (e.g., Nine-Item Avoidant/Restrictive Food Intake Disorder Screen [NIAS]) are used to assess symptom differences between groups.
View Article and Find Full Text PDFInt J Eat Disord
January 2025
SEED Lifespan Strategic Research Centre, School of Psychology, Faculty of Health, Deakin University, Geelong, Victoria, Australia.
Objective: Artificial intelligence (AI) could revolutionize the delivery of mental health care, helping to streamline clinician workflows and assist with diagnostic and treatment decisions. Yet, before AI can be integrated into practice, it is necessary to understand perspectives of these tools to inform facilitators and barriers to their uptake. We gathered data on clinician and community participant perspectives of incorporating AI in the clinical management of eating disorders.
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