Major depression and generalized anxiety disorder, conceptualized jointly as distress disorders, are prevalent conditions with considerable public health burden and comparatively lower response to treatment. Distress disorders share numerous features that serve to maintain and worsen these conditions, including heightened negative emotions, deficits in emotion regulation (ER), and elevated perseverative negative thinking (PNT). Importantly, effective ER is associated with better psychological functioning and better physiological recovery from stress, and it is a target in distress disorders interventions. Effective ER depends upon intact emotional awareness and contextual sensitivity, which facilitate flexibly enacting ER strategies to match one's characteristics and the current situation. A critical barrier to improving interventions for distress disorders is a lack of knowledge regarding the specific predictors of effective ER, especially when assessing performance under optimal conditions measured in the lab (ER capacity) and naturalistic, spontaneous ER use in daily life (ER tendency). The goal of this application is to evaluate how cognitive and situational factors contribute to effective ER capacity and ER tendency, and to examine how ER capacity and tendency work synergistically to predict long-term distress symptoms and functioning. An integrative model of ER specifically suited to the distress disorders that incorporates PNT will be tested in the laboratory and in daily life, with findings linked to prospective assessments of distress symptoms and functioning to isolate elements of effective ER most relevant to clinical outcomes. The proposed study will be comprised of 300 adults, oversampling for elevated PNT, with a multi-method design. Participants will complete a lab assessment that incorporates behavioral, cardiovascular physiological, and clinical interview measures, followed by a 10-day ecological momentary assessment study examining predictors of ER in daily life. To better capture negative emotions as they occur, reports in daily life will be physiologically-triggered with algorithms that detect psychological stress. Effective ER will be operationalized in multiple ways in the lab and in daily life, using self-reported changes in affect, physiological indices, and perceived ER success. The associations of effective ER and its predictors will then be examined in relation to the trajectory of distress symptoms and functioning at 6-month and 12-month follow-up assessments. Additionally, an exploratory aim is to create predictive models from a multifaceted battery of theoretically motivated variables that may impact real-life ER and subsequent clinical outcomes, by using machine learning to build a clinically-relevant framework for how person-level, situation-level, and ER strategy use interact to predict optimal ER. This project is consistent with NIMH strategic objectives and the RDoC framework due to the dimensional approach to psychopathology and multiple units of analysis. Overall, this project contributes to the long-term goal of identifying and refining targets for personalized interventions, by more precisely isolating key mechanisms of effective ER for specific individuals and the contexts they encounter in their daily lives.
The proposed study seeks to examine predictors of dysfunctional emotion regulation that contribute to depression and anxiety, as well as to provide an initial framework for how to optimize emotion regulation for a given person in a given situation. This study uses multiple measurements (behavioral, psychophysiological, clinical interview) in the laboratory and in naturalistic settings in daily life, and it focuses on transdiagnostic features that underlie numerous psychological symptoms. Consistent with NIMH's mission, findings will be relevant to public health because they can help refine intervention targets and inform personalized treatment for distress disorders, which are common and impairing conditions.
