Rook Laurens, Mazza Maria Chiara, Lefter Iulia, Brazier Frances
Faculty of Technology, Policy and Management, Delft University of Technology, Delft, Netherlands.
Front Digit Health. 2022 Apr 15;4:779039. doi: 10.3389/fdgth.2022.779039. eCollection 2022.
Generalized anxiety disorder (GAD) refers to extreme, uncontrollable, and persistent worry and anxiety. The disorder is known to affect the social functioning and well-being of millions of people, but despite its prevalence and burden to society, it has proven difficult to identify unique behavioral markers. Interestingly, the worrying behavior observed in GAD is argued to stem from a verbal linguistic process. Therefore, the aim of the present study was to investigate if GAD can be predicted from the language people use to put their anxious worries into words. Given the importance of avoidance sensitivity (a higher likelihood to respond anxiously to novel or unexpected triggers) in GAD, this study also explored if prediction accuracy increases when individual differences in behavioral avoidance and approach sensitivity are taken into account.
An expressive writing exercise was used to explore whether GAD can be predicted from linguistic characteristics of written narratives. Specifically, 144 undergraduate student participants were asked to recall an anxious experience during their university life, and describe this experience in written form. Clinically validated behavioral measures for GAD and self-reported sensitivity in behavioral avoidance/inhibition (BIS) and behavioral approach (BAS), were collected. A set of classification experiments was performed to evaluate GAD predictability based on linguistic features, BIS/BAS scores, and a concatenation of the two.
The classification results show that GAD can, indeed, be successfully predicted from anxiety-focused written narratives. Prediction accuracy increased when differences in BIS and BAS were included, which suggests that, under those conditions, negatively valenced emotion words and words relating to social processes could be sufficient for recognition of GAD.
Undergraduate students with a high GAD score can be identified based on their written recollection of an anxious experience during university life. This insight is an important first step toward development of text-based digital health applications and technologies aimed at remote screening for GAD. Future work should investigate the extent to which these results uniquely apply to university campus populations or generalize to other demographics.
广泛性焦虑障碍（GAD）指的是极端、无法控制且持续的担忧和焦虑。已知该障碍会影响数百万人的社交功能和幸福感，但其尽管在社会中普遍存在且负担沉重，却难以识别独特的行为标志物。有趣的是，GAD中观察到的担忧行为被认为源于言语语言过程。因此，本研究的目的是调查是否可以从人们用于表达焦虑担忧的语言中预测GAD。鉴于回避敏感性（对新奇或意外触发因素做出焦虑反应的可能性更高）在GAD中的重要性，本研究还探讨了在考虑行为回避和趋近敏感性的个体差异时，预测准确性是否会提高。
采用表达性写作练习来探索是否可以从书面叙述的语言特征中预测GAD。具体而言，144名本科学生参与者被要求回忆他们大学生活中的一次焦虑经历，并以书面形式描述该经历。收集了经临床验证的GAD行为测量指标以及自我报告的行为回避/抑制（BIS）和行为趋近（BAS）敏感性指标。进行了一组分类实验，以评估基于语言特征、BIS/BAS分数以及两者结合的GAD预测能力。
分类结果表明，确实可以从以焦虑为重点的书面叙述中成功预测GAD。当纳入BIS和BAS的差异时，预测准确性提高，这表明在这些条件下，负性情绪词和与社会过程相关的词可能足以识别GAD。
根据大学生活中焦虑经历的书面回忆，可以识别出GAD得分高的本科生。这一见解是朝着开发基于文本的数字健康应用和技术以进行GAD远程筛查迈出的重要第一步。未来的工作应调查这些结果在多大程度上仅适用于大学校园人群或推广到其他人群。
