Emotion Regulation in Distress Disorders: Elucidating the Role of Cognitive Processes and Person-Situation Fit in the Laboratory and Daily Life

摘要：情绪调节(ER)是一种关键的跨诊断过程和治疗靶点，尤其是 与苦恼障碍(抑郁症、广泛性焦虑症)的相关性。苦恼障碍，这是 以负面情绪高涨为特征的是具有相当大公共卫生负担的流行状况 对治疗的反应相对较低。对窘迫障碍进行干预的效果可能是 通过调查有效ER的组件(ER容量)和AS进行改进 在日常生活中自然测量(ER倾向)。有效的ER基于诸如精确度等关键组件 对一个人的情绪的感知(即，情绪意识)以及对环境背景的敏感性(即， 背景敏感性)，这有助于选择和实施适当的应急战略。为 患有苦恼障碍的个体，持续性负面思维(PNT)水平的升高会干扰这些 过程，导致内质网缺陷和症状增加。这种情景化的综合ER模式 在困境障碍中还没有经过测试，对有效的预测因素或结果知之甚少 ER倾向-这是一个严重的差距，因为如果在日常生活中不熟练地使用ER，ER能力是无关紧要的。 此外，内质网倾向的检查可以显示个体如何考虑到他们的特殊特征 和能力，可以将特定的ER策略与他们遇到的特定情况进行最佳匹配。的目标是 该应用程序用于评估在实验室和日常生活中影响ER的无效成分 困难症和功能障碍的严重程度和病程。与NIMH战略目标和 RDoC框架，该项目结合了多模式评估、维度症状测量和 机器学习方法。这项拟议的研究将包括300名成年人，在升高的情况下进行过度抽样 PNT(50%)。参与者将完成实验室评估，以衡量ER容量的预测因素，然后是10- 日生态瞬时评估研究检验日常生活中雌激素受体倾向的预测因素。为了更好地捕捉 当负面情绪发生时，日常生活中的报告将通过检测到 潜在的心理压力发作。有效的ER将在实验室以多种方式运行，并 在日常生活中，使用自我报告的情感变化、生理指标和感知的ER成功。有效ER 能力和倾向将被检验为痛苦症状、功能和幸福感的预测因子。 连续12个月每月评估轨迹。此外，探索性目标是从以下方面创建预测模型 一组多方面的理论驱动变量影响ER倾向和随后的临床 结果。为此，将使用机器学习来构建一个与临床相关的框架，以了解人- 层级、情境级和ER策略使用交互作用来预测最佳ER。总体而言，该项目有助于 通过更准确地隔离关键因素，确定和细化个性化干预的目标的长期目标 针对特定个体及其在日常生活中遇到的环境的有效ER机制。

ABSTRACT: Emotion regulation (ER) is a key transdiagnostic process and treatment target, with particular relevance to distress disorders (depression, generalized anxiety disorder). Distress disorders, which are characterized by heightened negative emotions, are prevalent conditions with considerable public health burden and comparatively lower response to treatment. The efficacy of interventions for distress disorders may be improved by investigating components of effective ER, both as instructed in the lab (ER capacity) and as measured naturalistically in daily life (ER tendency). Effective ER is based on key components such as accurate perceptions of one's emotions (i.e., emotional awareness) as well sensitivity to the environmental context (i.e., contextual sensitivity), which facilitate the selection and implementation of the appropriate ER strategy. For individuals with distress disorders, elevated levels of perseverative negative thinking (PNT) interferes with these processes, contributing to ER deficits and increased symptoms. This contextualized and integrative model of ER in distress disorders has not been tested, and very little is known about the predictors or outcomes of effective ER tendency—a critical gap given that ER capacity is irrelevant if ER is not employed skillfully in daily life. Furthermore, examinations of ER tendency can show how individuals, considering their particular characteristics and abilities, can optimally match particular ER strategies to the specific situations they encounter. The goal of this application is to evaluate components of ineffective ER— both in the lab and in daily life— that influence the severity and course of distress disorders and functioning. Consistent with NIMH strategic objectives and the RDoC framework, this project incorporates multimodal assessment, dimensional symptom measurement, and machine learning approaches. The proposed study will be comprised of 300 adults, oversampled for elevated PNT (50%). Participants will complete a lab assessment to measure predictors of ER capacity, followed by a 10- day ecological momentary assessment study examining predictors of ER tendency in daily life. To better capture negative emotions as they occur, reports in daily life will be physiologically-triggered with algorithms that detect potential episodes of 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. Effective ER capacity and tendency will then be examined as predictors of distress symptom, functioning, and well-being trajectories assessed monthly for 12 months. Additionally, an exploratory aim is to create predictive models from a multifaceted battery of theoretically motivated variables that impact ER tendency and subsequent clinical outcomes. To this end, machine learning will be used to build a clinically-relevant framework for how person- level, situation-level, and ER strategy use interact to predict optimal ER. 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.