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Depression and anxiety have distinct and overlapping language patterns: Results from a clinical interview.
Stade, Elizabeth C; Ungar, Lyle; Eichstaedt, Johannes C; Sherman, Garrick; Ruscio, Ayelet Meron.
Afiliação
  • Stade EC; Department of Psychology, University of Pennsylvania.
  • Ungar L; Department of Computer and Information Science, University of Pennsylvania.
  • Eichstaedt JC; Department of Psychology, Stanford University.
  • Sherman G; Intramural Research Program, National Institute on Drug Abuse.
  • Ruscio AM; Department of Psychology, University of Pennsylvania.
J Psychopathol Clin Sci ; 132(8): 972-983, 2023 Nov.
Article em En | MEDLINE | ID: mdl-37471025
ABSTRACT
Depression has been associated with heightened first-person singular pronoun use (I-usage; e.g., "I," "my") and negative emotion words. However, past research has relied on nonclinical samples and nonspecific depression measures, raising the question of whether these features are unique to depression vis-à-vis frequently co-occurring conditions, especially anxiety. Using structured questions about recent life changes or difficulties, we interviewed a sample of individuals with varying levels of depression and anxiety (N = 486), including individuals in a major depressive episode (n = 228) and/or diagnosed with generalized anxiety disorder (n = 273). Interviews were transcribed to provide a natural language sample. Analyses isolated language features associated with gold standard, clinician-rated measures of depression and anxiety. Many language features associated with depression were in fact shared between depression and anxiety. Language markers with relative specificity to depression included I-usage, sadness, and decreased positive emotion, while negations (e.g., "not," "no"), negative emotion, and several emotional language markers (e.g., anxiety, stress, depression) were relatively specific to anxiety. Several of these results were replicated using a self-report measure designed to disentangle components of depression and anxiety. We next built machine learning models to detect severity of common and specific depression and anxiety using only interview language. Individuals' speech characteristics during this brief interview predicted their depression and anxiety severity, beyond other clinical and demographic variables. Depression and anxiety have partially distinct patterns of expression in spoken language. Monitoring of depression and anxiety severity via language can augment traditional assessment modalities and aid in early detection. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Depressão / Transtorno Depressivo Maior Tipo de estudo: Prognostic_studies / Qualitative_research / Screening_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Depressão / Transtorno Depressivo Maior Tipo de estudo: Prognostic_studies / Qualitative_research / Screening_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article