Depression and anxiety have distinct and overlapping language patterns: Results from a clinical interview.

IF 3.1 Q2 PSYCHIATRY
Journal of psychopathology and clinical science Pub Date : 2023-11-01 Epub Date: 2023-07-20 DOI:10.1037/abn0000850
Elizabeth C Stade, Lyle Ungar, Johannes C Eichstaedt, Garrick Sherman, Ayelet Meron Ruscio
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引用次数: 0

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).

抑郁症和焦虑症有不同且重叠的语言模式:来自临床访谈的结果。
抑郁症与第一人称单数代词(I用法;例如“I”、“my”)和负面情绪词的使用增加有关。然而,过去的研究依赖于非临床样本和非特异性抑郁测量,这就提出了一个问题,即这些特征是否是抑郁症特有的,而不是经常同时发生的情况,尤其是焦虑。使用关于最近生活变化或困难的结构化问题,我们采访了具有不同程度抑郁和焦虑的个体样本(N=486),包括重度抑郁发作的个体(N=228)和/或被诊断为广泛性焦虑症的个体(N=273)。访谈被转录以提供自然语言样本。分析与金标准相关的孤立语言特征,临床医生对抑郁和焦虑的评分。事实上,抑郁症和焦虑症之间有许多与抑郁症相关的语言特征。对抑郁具有相对特异性的语言标记物包括I使用、悲伤和积极情绪下降,而否定(如“否”、“否”)、消极情绪和几种情绪语言标记物(如焦虑、压力、抑郁)对焦虑具有相对特异性。其中一些结果是使用自我报告测量来复制的,该测量旨在理清抑郁和焦虑的成分。接下来,我们建立了机器学习模型,只使用访谈语言来检测常见和特定的抑郁和焦虑的严重程度。在这次简短的采访中,个人的言语特征预测了他们的抑郁和焦虑的严重程度,超过了其他临床和人口统计学变量。抑郁和焦虑在口语中有部分不同的表达模式。通过语言监测抑郁和焦虑的严重程度可以增强传统的评估模式,并有助于早期发现。(PsycInfo数据库记录(c)2023 APA,保留所有权利)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
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