An Ecological Momentary Assessment Study of Intolerance of Uncertainty: Linking Computational Measures with Clinical Factors

项目总结 焦虑症影响着大约30%的美国人口1，超过四分之一的成年人患有焦虑症 至少一种终生焦虑症的标准2。焦虑症是一个主要的公共卫生负担，因为他们 与生活质量下降、严重的功能障碍和巨大的经济负担有关 成本6，7。尽管有这些相当大的影响，治疗焦虑只是中等有效的8， 强调需要进一步查明和探讨可改善预防和治疗的风险因素 努力。不能容忍不确定性是导致焦虑的一个重要危险因素 混乱9.然而，不确定性弥漫在日常生活中，通常会让人感到不适。 不同的人在容忍不确定性的程度上存在很大差异。在经历不确定性时 无法忍受会引发功能失调的反应，如担忧、消极情绪和回避行为10、11 这被认为是导致焦虑症状的原因之一。重要的是，研究如何使用 导致焦虑的风险受到两个主要限制的限制。首先，在临床上对IU的定义 文献是不精确的，可能会混淆不确定容忍度的两个不同组成部分，由 计算行为决策领域。需要使用多模式评估进行进一步研究，以 根据NIMH RDoC倡议整合IU的这些不同的操作，该倡议强调 考虑多层次分析的重要性13。其次，几乎没有关于使用Iu的研究 纵向的、人内的设计，从而限制了我们对IU如何影响情感和 对现实生活不确定性的行为反应。拟议的研究将使用新的方法来解决这两个问题 这些概念上的局限性和关于信息技术如何促进焦虑发展的研究不断扩大。 时间到了。具体地说，这项研究将：a)比较IU的临床评估和源自以下方面的行为测量 计算模型，b)使用生态瞬时评估(EMA)来评估个体 临床和行为信息单位的差异可以预测日常的负面情绪和行为回避反应 C)调查日常情感和行为反应是否会导致下游焦虑症状。 这项拟议研究的结果将推进IU的测量，提供对如何 免疫缺陷增加了焦虑的风险，并最终促进了治疗的发展和完善 治疗焦虑症。这一建议对预防和治疗焦虑具有重要意义。通过这件事 建议的学习，申请人将获得额外的培训和密集的纵向学习的经验 设计、高级统计技术和多模式评估方法，包括计算 建模方法。通过奖学金获得的经验将为申请者奠定基础。 成为研究焦虑病理的跨诊断危险因素的独立研究员， 最终目标是确定可延展的目标，以开发更有效的焦虑症治疗方法。

PROJECT SUMMARY Anxiety disorders affect approximately 30% of the U.S. population1, with over 1 in 4 adults meeting diagnostic criteria for at least one lifetime anxiety disorder2. Anxiety disorders are a major public health burden3, as they are associated with a decreased quality of life4, substantial functional impairment5, and an enormous economic cost6,7. Despite these considerable ramifications, treatments for anxiety are only moderately effective8, highlighting the need to further identify and explore risk factors that may improve prevention and treatment efforts. Intolerance of uncertainty (IU) is one important risk factor implicated in the development of anxiety disorders9. Uncertainty permeates daily life and is generally found to be somewhat discomforting, though individuals differ greatly on the degree to which they tolerate uncertainty. Experiencing uncertainty as intolerable can elicit dysfunctional responses such as worry, negative mood, and avoidance behavior10,11, all of which are thought to contribute to the development of anxiety symptoms12. Importantly, research on how IU contributes to risk for anxiety is constrained by two main limitations. First, the definition of IU in the clinical literature is imprecise and may conflate two distinct components of uncertainty tolerance identified by the computational behavioral decision-making field. Further research using multimodal assessments is needed to integrate these distinct operationalizations of IU, in line with the NIMH RDoC Initiative which emphasizes the importance of considering multiple levels of analysis13. Second, there is virtually no research on IU using longitudinal, within-person designs, thus limiting our understanding of how IU influences affective and behavioral responses to real-life uncertainty. The proposed study will use novel methodologies to both address these conceptual limitations and expand research on how IU contributes to the development of anxiety over time. Specifically, this study will: a) compare clinical assessments of IU and behavioral measures derived from computational modeling, b) use ecological momentary assessment (EMA) to assess whether individual differences in clinical and behavioral IU predict daily negative affect and behavioral avoidance responses, and c) investigate whether daily affective and behavioral responses contribute to downstream anxiety symptoms. Results of this proposed study would advance the measurement of IU, providing a better understanding of how IU contributes to risk for anxiety and ultimately contributing to the development and refinement of treatments for anxiety disorders. This proposal has important implications for preventing and treating anxiety. Through this proposed study, the applicant will acquire additional training and experience in intensive longitudinal study design, advanced statistical techniques, and multimodal assessment methods, including computational modeling approaches. The experience gained through this fellowship will lay the groundwork for the applicant to become an independent researcher investigating the transdiagnostic risk factors of anxiety pathology, with the ultimate goal of identifying malleable targets for the development of more effective treatments for anxiety.