Improving measurement of emotional granularity to investigate affective mechanisms of cardiovascular disease and metabolic syndrome

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PROJECT SUMMARY/ABSTRACT Cardiovascular disease (CVD) and metabolic syndrome are a leading cause of disability and death worldwide. Detrimental shifts in the resting (tonic) contributions of the autonomic nervous system (ANS) to visceral functions throughout the body, a form of compromised allostasis, have been observed with both emotional dysregulation and disordered mood and may be a common, core vulnerability for CVD and metabolic syndrome. A crucial but understudied psychological vulnerability to compromised allostasis is low emotional granularity, or the inability to experience emotion with precision and detail (e.g., the inability to distinguish anger vs. frustration, or even anger vs. sadness). A critical barrier to ameliorating low granularity, and therefore reducing susceptibility to CVD and metabolic syndrome, has been the lack of a theoretical framework linking emotional granularity to physiological regulation, as well as tools for effectively measuring and improving granularity. Theoretical advances in affective science posit that the use of more precise emotion concepts is associated with a peripheral physiological system better able to respond to environmental perturbations (e.g., stressors). If low granularity results from impoverished emotion concepts, then the brain is less able to predict and categorize viscerosensory changes that arise from regulation of the body’s internal milieu. Increased parasympathetic tone at rest permits more efficient regulation by promoting recovery and energy conservation, and substantial evidence suggests that lowered risk profiles for CVD are associated with increased high frequency heart rate variability (HF HRV). The proposed research will use experience sampling and ambulatory monitoring data to map variability in emotional granularity in everyday life and examine its consequences for peripheral physiology, with a focus on resting HF HRV. In Aim 1, machine learning will be used to assess whether individuals lower in emotional granularity have less efficient allostasis, as reflected by both lower resting parasympathetic activity and fewer distinct patterns of ANS activity. In Aim 2, graph theory will be used to develop metrics for measuring temporal and contextual dynamics of emotional granularity, which provide meaningful variance necessary to describe patterns of subjective experience and physiological activity. Exploratory Aim 3 will assess whether in-lab emotion concept training can be used to improve granularity of emotion concepts, with the ultimate goal of developing longer-term training aimed at increasing physiological specificity and resting HF HRV. By integrating modeling from engineering and computer science to better capture idiographic variation in emotion, the proposed research offers an innovative approach for understanding how a psychological vulnerability could increase the risk for cardiovascular illness. The outcomes of this work will allow for the development of psychological interventions that can decrease risk for physical illness by increasing efficient allostasis and encouraging adaptive coping mechanisms.