Depression and anxiety have distinct and overlapping language patterns: Results from a clinical interview.
Depression and anxiety have distinct and overlapping language patterns: Results from a clinical interview.
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抑郁和焦虑有不同且重叠的语言模式：临床访谈的结果。
DOI：
10.1037/abn0000850
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Ruscio,AyeletMeron
中科院分区：
文献类型：
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作者：
Stade,ElizabethC;Ungar,Lyle;Eichstaedt,JohannesC;Sherman,Garrick;Ruscio,AyeletMeron
抑郁症与第一人称单数代词的使用（我的用法;例如，“我"，“我的”）和负面情绪词的使用有关。然而，过去的研究依赖于非临床样本和非特异性抑郁指标，这就提出了一个问题，即这些特征是否是抑郁症相对于常见的并发症（尤其是焦虑症）所独有的。使用结构化的问题，最近的生活变化或困难，我们采访了一个样本的个人与不同程度的抑郁和焦虑（N= 486），包括个人在一个严重的抑郁发作（n= 228）和/或诊断为广泛性焦虑症（n= 273）。访谈被转录以提供自然语言样本。分析孤立的语言功能与金标准，临床医生评定的抑郁和焦虑的措施。事实上，许多与抑郁症相关的语言特征在抑郁症和焦虑症之间是共有的。抑郁症的相对特异性语言标记包括我的使用，悲伤，减少积极情绪，而否定（如，“不”，“不”），消极情绪，和几个情绪语言标记（如，焦虑，压力，抑郁）是相对具体的焦虑。这些结果中的几个被复制使用自我报告的措施，旨在解开抑郁和焦虑的组成部分。接下来，我们建立了机器学习模型，仅使用面试语言来检测常见和特定抑郁和焦虑的严重程度。在这个简短的访谈中，个体的言语特征预测了他们的抑郁和焦虑的严重程度，超出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方式，并有助于早期发现。（PsycInfo数据库记录（c）2023阿帕，保留所有权利）
Depression has been associated with heightened first-person singular pronoun use (I-usage; eg,“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 (eg,“not,”“no”), negative emotion, and several emotional language markers (eg, 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)