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.
