Network analysis of misophonia symptoms using the Duke Misophonia Questionnaire.

Impairment Misophonia Network analysis Questionnaire Symptom

Journal

Journal of affective disorders
ISSN: 1573-2517
Titre abrégé: J Affect Disord
Pays: Netherlands
ID NLM: 7906073

Informations de publication

Date de publication:
24 Oct 2024
Historique:
received: 17 06 2024
revised: 12 10 2024
accepted: 20 10 2024
medline: 27 10 2024
pubmed: 27 10 2024
entrez: 26 10 2024
Statut: aheadofprint

Résumé

Misophonia is a complex disorder characterized by a strong aversion to specific sounds, leading to significant distress and impairment. While the Duke Misophonia Questionnaire (DMQ) is one of the most comprehensive and validated measures for assessing misophonia, the relative importance of specific subscales and items within the DMQ remains unclear. Network analysis enables an understanding of the interconnections among subscales, providing insights into which parts of the measure are most central to the others. This study employed network analysis to examine the interconnections among DMQ subscales and identify the most central components of misophonia symptomatology. Network analysis was conducted on DMQ data from 144 adults with varying levels of misophonia symptoms. Four network models were examined: overall misophonia, symptoms, beliefs, and impairment. Sex differences were also explored. The Impairment subscale emerged as the most central in the overall network for both males and females. Key items included cognitive reactions ("I need to get away from the sound," "I thought about physically hurting the person making the sound") as well as affective reactions (panic, anger) in the symptom sub-network, non-acceptance of misophonia beliefs ("I hate being like this") in the belief sub-network, and deterioration of self-esteem due to misophonia in the impairment sub-network. Females reported more severe cognitive and physiological symptoms than males. The DMQ Impairment subscale and specific items identified as most central in each network may represent key aspects of misophonia symptomatology. Prioritizing these components in assessment and intervention efforts may be beneficial when appropriate.

Sections du résumé

BACKGROUND BACKGROUND
Misophonia is a complex disorder characterized by a strong aversion to specific sounds, leading to significant distress and impairment. While the Duke Misophonia Questionnaire (DMQ) is one of the most comprehensive and validated measures for assessing misophonia, the relative importance of specific subscales and items within the DMQ remains unclear. Network analysis enables an understanding of the interconnections among subscales, providing insights into which parts of the measure are most central to the others. This study employed network analysis to examine the interconnections among DMQ subscales and identify the most central components of misophonia symptomatology.
METHODS METHODS
Network analysis was conducted on DMQ data from 144 adults with varying levels of misophonia symptoms. Four network models were examined: overall misophonia, symptoms, beliefs, and impairment. Sex differences were also explored.
RESULTS RESULTS
The Impairment subscale emerged as the most central in the overall network for both males and females. Key items included cognitive reactions ("I need to get away from the sound," "I thought about physically hurting the person making the sound") as well as affective reactions (panic, anger) in the symptom sub-network, non-acceptance of misophonia beliefs ("I hate being like this") in the belief sub-network, and deterioration of self-esteem due to misophonia in the impairment sub-network. Females reported more severe cognitive and physiological symptoms than males.
CONCLUSIONS CONCLUSIONS
The DMQ Impairment subscale and specific items identified as most central in each network may represent key aspects of misophonia symptomatology. Prioritizing these components in assessment and intervention efforts may be beneficial when appropriate.

Identifiants

pubmed: 39461373
pii: S0165-0327(24)01794-4
doi: 10.1016/j.jad.2024.10.105
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024. Published by Elsevier B.V.

Déclaration de conflit d'intérêts

Declaration of competing interest Dr. Rosenthal receives book royalties from the American Psychological Association and is a consultant for several digital health companies (Odin, RealizedCare). All of this work is unrelated to misophonia. In addition, Dr. Rosenthal provides clinical training workshops about misophonia to community clinicians for a fee. We have no other conflicts of interest to disclose.

Auteurs

Yanyan Shan (Y)

Department of Psychiatry & Behavioral Sciences, Duke University Medical Center, Durham, NC, United States of America; Department of Psychology & Neuroscience, Duke University, Durham, NC, United States of America. Electronic address: yanyan.shan@duke.edu.

Marta Siepsiak (M)

Department of Psychology in Warsaw, SWPS University, Warsaw, Poland.

Kibby McMahon (K)

Department of Psychiatry & Behavioral Sciences, Duke University Medical Center, Durham, NC, United States of America; Department of Psychology & Neuroscience, Duke University, Durham, NC, United States of America.

Rachel Guetta (R)

Department of Psychiatry & Behavioral Sciences, Duke University Medical Center, Durham, NC, United States of America; Department of Psychology & Neuroscience, Duke University, Durham, NC, United States of America.

Lisalynn Kelley (L)

Department of Psychiatry & Behavioral Sciences, Duke University Medical Center, Durham, NC, United States of America; Department of Psychology & Neuroscience, Duke University, Durham, NC, United States of America.

Tao Chen (T)

Brain and Mind Centre, University of Sydney, Sydney, Australia.

M Zachary Rosenthal (MZ)

Department of Psychiatry & Behavioral Sciences, Duke University Medical Center, Durham, NC, United States of America; Department of Psychology & Neuroscience, Duke University, Durham, NC, United States of America.

Classifications MeSH