Empirically determined severity levels for binge-eating disorder outperform existing severity classification schemes

Lauren N. Forrest, Ross C. Jacobucci, Carlos M. Grilo

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

BackgroundEating-disorder severity indicators should theoretically index symptom intensity, impairment, and level of needed treatment. Two severity indicators for binge-eating disorder (BED) have been proposed (categories of binge-eating frequency and shape/weight overvaluation) but have mixed empirical support including modest clinical utility. This project uses structural equation model (SEM) trees- A form of exploratory data mining-to empirically determine the precise levels of binge-eating frequency and/or shape/weight overvaluation that most significantly differentiate BED severities.MethodsParticipants were 788 adults with BED enrolled in BED treatment studies. Participants completed interviews and self-report measures assessing eating-disorder and comorbid symptoms. SEM Tree analyses were performed by specifying an outcome model of BED severity and then recursively partitioning the outcome model into subgroups. Subgroups were split based on empirically determined values of binge-eating frequency and/or shape/weight overvaluation. SEM Forests also quantified which variable contributed more improvement in model fit.ResultsSEM Tree analyses yielded five subgroups, presented in ascending order of severity: Overvaluation <1.25, overvaluation = 1.25-2.74, overvaluation = 2.75-4.24, overvaluation â

Original languageEnglish (US)
JournalPsychological medicine
DOIs
StateAccepted/In press - 2020

All Science Journal Classification (ASJC) codes

  • Applied Psychology
  • Psychiatry and Mental health

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