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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.

项目总结/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）对内脏的静息（紧张）贡献的有害变化整个身体的功能，一种受损的变稳态形式，已经被观察到，失调和紊乱的情绪，可能是一个共同的，核心脆弱性心血管疾病和代谢综合征一个关键的，但研究不足的心理脆弱性，妥协变稳态是低情绪粒度，或无法精确和详细地体验情感（例如，无法区分愤怒与沮丧，甚至愤怒与悲伤）。改善低粒度的关键障碍，因此降低心血管疾病和代谢综合征的易感性，一直缺乏一个理论框架，情绪粒度到生理调节，以及有效测量和改善的工具粒度情感科学的理论进展表明，使用更精确的情感概念是必要的。与能够更好地响应环境扰动的外围生理系统相关联（例如，压力源）。如果低粒度是由贫乏的情感概念造成的，那么大脑就不太能够预测并对由身体内部环境调节引起的内脏感觉变化进行分类。增加静息时的副交感神经张力通过促进恢复和能量保存而允许更有效的调节，大量的证据表明，降低心血管疾病的风险与增加高血压有关。频率心率变异性（HF HRV）。拟议的研究将使用经验抽样，动态监测数据，以映射日常生活中情绪粒度的变化，并检查其外周生理学的后果，重点是静息HF HRV。在目标1中，机器学习将是用于评估情绪粒度较低的个体是否具有较低的有效变稳态，如以下所反映的：既降低了静息副交感神经活动，也减少了ANS活动的不同模式。目标2：图论将用于开发用于测量情感粒度的时间和上下文动态的度量，它提供了描述主观体验和生理模式所必需的有意义的方差，活动探索性目标3将评估实验室内情绪概念培训是否可用于改善情感概念的粒度，最终目标是开发旨在提高生理特异性和静息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绪的具体变化，这项研究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。的这项工作的成果将允许开发心理干预措施，可以降低风险，通过增加有效的自动调节和鼓励适应性应对机制来治疗身体疾病。