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）是一个关键的转诊断过程和治疗靶点，特别是 与抑郁症（抑郁症、广泛性焦虑症）相关。抑郁症， 以负面情绪加剧为特征的疾病是一种普遍存在的疾病，给公共卫生造成了相当大的负担。 对治疗的反应相对较低。干预抑郁障碍的有效性可能是 通过调查有效ER的组成部分，无论是在实验室（ER能力）的指示， 在日常生活中自然测量（ER趋势）。有效的ER基于关键组件，例如准确的 对一个人的情感的感知（即，情绪意识）以及对环境背景的敏感性（即， 上下文敏感性），这有助于选择和实施适当的ER策略。为 对于患有抑郁症的人，持续性消极思维（PNT）水平的升高会干扰这些 过程，导致ER缺陷和症状增加。这种情境化和整合的ER模型 抑郁症的治疗尚未得到验证，关于有效治疗的预测因素或结果知之甚少。 ER倾向-一个关键的差距，因为ER能力是无关紧要的，如果ER不熟练地运用在日常生活中。 此外，ER倾向的检查可以显示个体如何考虑他们的特定特征， 和能力，可以最佳地匹配特定的ER策略，他们遇到的具体情况。的目标 该应用程序是评估无效ER的组成部分-无论是在实验室还是在日常生活中-影响 严重程度和过程的痛苦障碍和功能。符合NIMH战略目标和 RDoC框架，该项目结合了多模态评估，维度症状测量， 机器学习方法。这项拟议的研究将由300名成年人组成， PNT（50%）。参与者将完成实验室评估，以测量ER能力的预测因子，然后进行10- 一天的生态瞬时评估研究，检查日常生活中ER趋势的预测因素。更好地捕捉 负面情绪，因为他们发生，报告在日常生活中将生理触发的算法，检测 潜在的心理压力有效的ER将在实验室中以多种方式运作， 在日常生活中，使用自我报告的影响，生理指标和感知ER成功的变化。有效ER 能力和倾向，然后将被检查作为预测的痛苦症状，功能，和福祉 每月评估轨迹，持续12个月。此外，一个探索性的目标是创建预测模型， 一组多方面的理论动机变量，影响ER趋势和随后的临床 结果。为此，机器学习将用于建立一个临床相关的框架，以了解人如何... 情境水平、情境水平和ER策略使用交互作用来预测最佳ER。总的来说，该项目有助于 通过更精确地隔离关键因素，确定和完善个性化干预目标的长期目标 有效的ER机制，为特定的个人和他们在日常生活中遇到的情况。

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.