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