Characterizing severe and enduring anorexia nervosa: An empirical approach

About this resource

Targeted approaches for the treatment of severe and enduring anorexia nervosa (SE-AN) have been recommended, but there is no consensus definition of SE-AN to inform research and clinical practice. This study aimed to take initial steps toward developing an empirically based definition of SE-AN by characterizing associations among putative indicators of severity and chronicity in eating disorders. Patients with AN (N = 355) completed interviews and questionnaires at treatment admission and discharge; height and weight were assessed to calculate body mass index (BMI). Structural equation mixture modeling was used to test whether associations among potential indicators of SE-AN (illness duration, treatment history, BMI, binge eating, purging, quality-of-life) formed distinct subgroups, a single group with one or more dimensions, or a combination of subgroups and dimensions. A three-factor (dimensional), two-profile (categorical) mixture model provided the best fit to the data. Factor 1 included eating disorder behaviors; Factor 2 comprised quality-of-life domains; Factor 3 was characterized by illness duration, number of hospitalizations, and admission BMI. Profiles differed on eating disorder behaviors and quality-of-life, but not on indicators of chronicity or BMI. Factor scores, but not profile membership, predicted outcome at discharge from treatment. Data suggest that patients with AN can be classified on the basis of eating disorder behaviors and quality-of-life, but there was no evidence for a chronic subgroup of AN. Rather, indices of chronicity varied dimensionally within each class. Given that current definitions of SE-AN rely on illness duration, these findings have implications for research and clinical practice.

AuthorWildes, Jennifer E.; Forbush, Kelsie T.; Hagan, Kelsey E.; Marcus, Marsha D.; Attia, Evelyn; Gianini, Loren M. & Wu, Wei
JournalInternational Journal of Eating Disorders
Volume50(4):389-397
Year2017

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