Perseverative thinking is associated with features of spoken language.
Perseverative thinking is associated with features of spoken language.
复制标题
坚持不懈的思维与口语的特征有关。
DOI：
10.1016/j.brat.2023.104307
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发表时间：
2023
影响因子：
4.1
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;Havaldar,Shreya;Ruscio,AyeletMeron
持续性思维（PT），如沉思或担忧，是一种与情绪障碍的发作和维持有关的转诊断过程。PT的现有措施是有限的需求和预期效应，认知偏差，和反射性，导致呼吁不引人注目的，行为的措施。作为回应，我们开发了一种基于语言的PT行为测量。188名患有重度抑郁症、广泛性焦虑症或无精神病理学的参与者的混合样本完成了自我报告的PT测量。参与者还接受了采访，提供了自然语言样本。我们研究了与PT相关的语言特征，然后建立了一个基于语言的PT模型，并检查了其预测能力。PT与多种语言特征相关，最显著的是I-使用（例如，“我”，“我”;β= 0.25）和负面情绪语言（例如，“焦虑”、“困难”;β= 0.19）。在机器学习分析中，语言特征占自我报告PT方差的14%。基于年龄的PT预测抑郁和焦虑、精神病合并症和寻求治疗的存在和严重程度，其影响范围为0.15-0.41。PT有脸有效的语言相关，我们的语言为基础的措施保持承诺评估PT不引人注目。随着进一步的发展，这一措施可以用来被动检测PT部署“及时”的干预措施。
Perseverative thinking (PT), such as rumination or worry, is a transdiagnostic process implicated in the onset and maintenance of emotional disorders. Existing measures of PT are limited by demand and expectancy effects, cognitive biases, and reflexivity, leading to calls for unobtrusive, behavioral measures. In response, we developed a behavioral measure of PT based on language. A mixed sample of 188 participants with major depressive disorder, generalized anxiety disorder, or no psychopathology completed self-report PT measures. Participants were also interviewed, providing a natural language sample. We examined language features associated with PT, then built a language-based PT model and examined its predictive power. PT was associated with multiple language features, most notablyI-usage(e.g., “I”, “me”;β= 0.25) andnegative emotionlanguage (e.g., “anxiety”, “difficult”;β= 0.19). In machine learning analyses, language features accounted for 14% of the variance in self-reported PT. Language-based PT predicted the presence and severity of depression and anxiety, psychiatric comorbidity, and treatment seeking, with effects in ther= 0.15–0.41 range. PT has face-valid linguistic correlates and our language-based measure holds promise for assessing PT unobtrusively. With further development, this measure could be used to passively detect PT for deployment of “just-in-time” interventions.