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A network investigation of core symptoms and pathways across duration of illness using a comprehensive cognitive–behavioral model of eating-disorder symptoms

Published online by Cambridge University Press:  07 January 2020

Caroline Christian Open the ORCID record for Caroline Christian [Opens in a new window] ,
Brenna M. Williams ,
Rowan A. Hunt ,
Valerie Z. Wong ,
Sarah E. Ernst ,
Samantha P. Spoor ,
Irina A. Vanzhula ,
Jenna P. Tregarthen ,
Kelsie T. Forbush  and
Cheri A. Levinson
Caroline Christian
Affiliation:
Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, United States of America
Brenna M. Williams
Affiliation:
Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, United States of America
Rowan A. Hunt
Affiliation:
Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, United States of America
Valerie Z. Wong
Affiliation:
Department of Psychology, Yale University New Haven, CT, United States of America
Sarah E. Ernst
Affiliation:
Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, United States of America
Samantha P. Spoor
Affiliation:
Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, United States of America
Irina A. Vanzhula
Affiliation:
Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, United States of America
Jenna P. Tregarthen
Affiliation:
Recovery Record, Inc. San Francisco, CA, United States of America
Kelsie T. Forbush
Affiliation:
Department of Psychology, University of Kansas, Lawrence, KS, United States of America
Cheri A. Levinson*
Affiliation:
Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, United States of America
*
Author for correspondence: Cheri A. Levinson, E-mail: cheri.levinson@louisville.edu
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Abstract

Background

In the past decade, network analysis (NA) has been applied to psychopathology to quantify complex symptom relationships. This statistical technique has demonstrated much promise, as it provides researchers the ability to identify relationships across many symptoms in one model and can identify central symptoms that may predict important clinical outcomes. However, network models are highly influenced by node selection, which could limit the generalizability of findings. The current study (N = 6850) tests a comprehensive, cognitive–behavioral model of eating-disorder symptoms using items from two, widely used measures (Eating Disorder Examination Questionnaire and Eating Pathology Symptoms Inventory).

Methods

We used NA to identify central symptoms and compared networks across the duration of illness (DOI), as chronicity is one of the only known predictors of poor outcome in eating disorders (EDs).

Results

Our results suggest that eating when not hungry and feeling fat were the most central symptoms across groups. There were no significant differences in network structure across DOI, meaning the connections between symptoms remained relatively consistent. However, differences emerged in central symptoms, such that cognitive symptoms related to overvaluation of weight/shape were central in individuals with shorter DOI, and behavioral central symptoms emerged more in medium and long DOI.

Conclusions

Our results have important implications for the treatment of individuals with enduring EDs, as they may have a different core, maintaining symptoms. Additionally, our findings highlight the importance of using comprehensive, theoretically- or empirically-derived models for NA.

Keywords

Duration of illnesseating disorder symptomsnetwork analysis

Information

Type
Original Article
Information
Psychological Medicine , Volume 51 , Issue 5 , April 2021 , pp. 815 - 824
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Copyright
Copyright © The Authors 2020. Published by Cambridge University Press

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