Identifying Subgroups of Patients With Eating Disorders Based on Emotion Dysregulation Profiles: A Factor Mixture Modeling Approach to Classification
2022 (English)In: Psychological Assessment, ISSN 1040-3590, E-ISSN 1939-134X, Vol. 34, no 4, p. 367-378Article in journal (Refereed) Published
Abstract [en]
This study aimed to investigate whether individuals with eating disorders (ED; N = 857) could be empirically classified into qualitatively distinct subgroups based on their emotion dysregulation profiles. A series of increasingly complex models (factor analysis; FA, latent class analysis; LCA, and factor mixture models; FMM) were evaluated to determine whether the structure of psychopathology was best characterized by emotional dysregulation subtypes, dimensions, or a combination of the two. The subscales of the difficulties in emotion regulation scale were used as indicators. Data were split into an exploratory and confirmatory dataset, and the best-fitting models in the exploratory set were compared and validated against clinically relevant variables in the confirmatory set. Results confirmed that individuals could be grouped into three latent classes that were clearly distinguishable on ED pathology and psychiatric comorbidity. Specifically, individuals belonging to the class with more severe emotion dysregulation had higher levels of ED pathology and were more likely to engage in vomiting and binge eating as well as substance abuse and self-harm. These results provide initial support for emotional dysregulation profiling as a viable transdiagnostic approach to classification in the field of EDs.
Place, publisher, year, edition, pages
American Psychological Association (APA), 2022. Vol. 34, no 4, p. 367-378
Keywords [en]
Eating Disorders, Emotion Regulation, Psychiatric Comorbidity, Empirical Classification, Factor Mixture Modeling
National Category
Psychology
Identifiers
URN: urn:nbn:se:oru:diva-96232DOI: 10.1037/pas0001103ISI: 000733192300001PubMedID: 34941352Scopus ID: 2-s2.0-85122391329OAI: oai:DiVA.org:oru-96232DiVA, id: diva2:1625421
2022-01-072022-01-072024-01-11Bibliographically approved