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Linguistic analysis of autobiographical narratives in unipolar and bipolar mood disorders in light of multiple code theory.

Authors :
Mariani, R.
Di Trani, M.
Negri, A.
Tambelli, R.
Di Trani, M
Source :
Journal of Affective Disorders. Aug2020, Vol. 273, p24-31. 8p.
Publication Year :
2020

Abstract

<bold>Background: </bold>Discriminating bipolar disorder (BD) from unipolar disorder (UD) is crucial in diagnosing mood disorders. Neurophysiological studies have identified different correlates of emotional regulation in BD and UD. According to the Multiple Code Theory, bodily modifications relate to linguistic styles, as highlighted by studies on the language of depression. Our purpose is to verify the existence in the Italian language of linguistic features of depression differentiating BD from UD to provide tools for clinicians to use beyond self-report measures.<bold>Methods: </bold>The sample included 20 BD, 20 UD (all diagnosed using DSM-5), and 20 Control Group (CG) participants. Participants completed the Profile of Mood States (POMS) and an audio-recorded Relationship Anecdotes Paradigm Interview, transcribed and analyzed by the Discourse Attributes Analysis Program for Referential Process Linguistic Measures.<bold>Results: </bold>One-way ANOVAs confirmed that specific linguistic features characterized BD, UD and CG. The use of Sensory-Somatic words was significantly different in the groups: higher in BD, intermediate in UD, and lower in CG. Individuals with BD produced higher scores on the Referential Activity Intensity Index and the use of singular pronoun "I". Negative Affect, as well as several POMS subscales, distinguished UD and BD from CG.<bold>Limitations: </bold>Narrow sample size, use of a single self-report instrument and treatment effects on measures in the clinical groups are limitations of the study.<bold>Conclusion: </bold>Individuals with UD and BD appear to use sensory-somatic language in predictably different patterns from each other and from the non-clinical population. Observation and assessment of linguistic features could improve diagnostic accuracy. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01650327
Volume :
273
Database :
Academic Search Index
Journal :
Journal of Affective Disorders
Publication Type :
Academic Journal
Accession number :
143779822
Full Text :
https://doi.org/10.1016/j.jad.2020.03.170