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

改善情绪粒度测量以研究心血管疾病和代谢综合征的情感机制

基本信息

批准号：
9769513
负责人：
Katherine Anne Hoemann
金额：
$ 3.86万
依托单位：
NORTHEASTERN UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2020-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9769513
关键词：
Address Affective Alcohol consumption Alexithymias Ambulatory Monitoring Anger Autonomic nervous system Behavioral Binge Eating Brain Cardiovascular Diseases Cessation of life Common Core Data Depressed mood Development Emotional Emotional disorder Emotions Engineering Fellowship Frequencies Frustration Future Goals Hypertension Hypertriglyceridemia Immune system Individual Individual Differences Intervention Knowledge Life Link Machine Learning Maps Measurement Measures Metabolic Metabolic syndrome Methodology Methods Modeling Mood Disorders Neurosecretory Systems Obesity Outcome Pattern Peripheral Physiological Physiology Population Predisposition Recovery Regulation Reporting Research Rest Risk Risk Factors Sampling Science Specificity System Testing Time Training Variant Visceral Work alcohol risk allostasis cardiovascular risk factor career computer science contextual factors coping coping mechanism disability emotion dysregulation emotion regulation emotional experience experience graph theory heart rate variability improved indexing innovation neuromechanism novel physical conditioning protective factors psychologic stressor theories tool

项目摘要

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.

项目摘要/摘要心血管疾病（CVD）和代谢综合征是全球残疾和死亡的主要原因。自主神经系统（ANS）对内脏的静止（补品）贡献的有害转移在整个身体中的功能，一种被妥协的同性恋形式，都被观察到了两者的情感失调和情绪失调，可能是CVD和代谢的常见核心脆弱性综合征。至关重要但被理解的心理脆弱性妥协的同性恋是情绪低落的粒度，或无法精确和细节体验情感（例如，无法区分愤怒与挫败感，甚至愤怒与悲伤）。改善低粒度的关键障碍，因此降低对CVD和代谢综合征的敏感性，缺乏链接的理论框架对身体调节的情绪颗粒状，以及有效衡量和改进的工具粒度。情感科学的理论进步积极，即使用更精确的情感概念是与外围物理系统相关联，能够更好地响应环境扰动（例如，压力源）。如果低粒度是由贫困的情感概念引起的，那么大脑就无法预测并分类由人体内部环境的调节引起的内脏感觉变化。增加静止的副交感神经允许通过促进恢复和能源保护，更有效地调节大量证据表明，CVD的风险档案降低与高增加有关频率心率变异性（HF HRV）。拟议的研究将使用经验抽样和卧床监测数据以绘制日常生活中情感粒度的变异性并检查其外围生理的后果，重点是静止HF HRV。在AIM 1中，机器学习将是用来评估较低的情绪粒度较低的人的效率较低，如较低的静息副交感神经和ANS活性的不同模式较少。在AIM 2中，图理论将用于制定指标来衡量情感粒度的暂时和上下文动态，它提供了描述主观经验和生理模式所必需的有意义的差异活动。探索性目标3将评估是否可以使用LAB内情感概念培训来改进情感概念的粒度，其最终目标是开发旨在增加的长期培训生理特异性和静止的HF HRV。通过整合工程和计算机科学的建模为了更好地捕捉情感的二志变化，拟议的研究为您提供了一种创新的方法了解心理脆弱性如何增加心血管疾病的风险。这这项工作的结果将允许发展心理干预措施，以降低风险通过提高效率的同性恋和鼓励适应性应对机制，通过身体疾病。