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

应激障碍中的情绪调节：阐明认知过程和人境适应性在实验室和日常生活中的作用

• 批准号: 10415077
• 负责人: Kristin Gainey
• 金额: $ 44.93万
• 依托单位: UNIVERSITY OF WESTERN AUSTRALIA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-12 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10415077
• 关键词: Adult; Affect; Algorithms; Anxiety; Applications Grants; Arousal; Awareness; Behavioral; Cardiovascular system; Characteristics; Clinical; Complex; Dimensions; Disease; Distress; Ecological momentary assessment; Emotional; Emotions; Generalized Anxiety Disorder; Goals; Impairment; Individual; Individual Differences; Intervention; Interview; Laboratories; Life; Machine Learning; Major Depressive Disorder; Measurement; Measures; Mental Depression; Mission; Modeling; National Institute of Mental Health; Outcome; Participant; Patient Self-Report; Pattern; Perception; Personal Satisfaction; Persons; Physiological; Process; Psychological Stress; Psychophysiology; Public Health; Reporting; Research Domain Criteria; Risk; Role; Severities; Symptoms; Testing; Thinking; Time; Treatment Efficacy; Work; adaptive intervention; base; clinically relevant; cognitive process; comparative; emotion regulation; improved; improved outcome; indexing; innovation; multimodality; novel; personalized intervention; personalized medicine; predictive modeling; psychosocial; random forest; response; rumination; success; treatment response

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.

摘要：情绪调节（ER）是一个关键的转诊过程和治疗目标，特别是 与遇险障碍（抑郁症，普遍焦虑症）有关。遇险障碍 以增强的负面情绪为特征，是普遍的条件，考虑到公共卫生伯恩 并且对治疗的反应相对较低。干预措施的效率可能是 通过研究实验室（ER容量）的指示，通过调查有效ER的组件以及AS来改进 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 对自己情绪的看法（即情感意识）以及对环境环境的敏感性（即 上下文灵敏度），促进了适当的ER策略的选择和实施。为了 患有遇险障碍的人，持续的负面思维水平升高（PNT）干扰这些 过程，有助于ER定义并增加症状。 ER的上下文化和集成的模型 在遇险障碍中尚未进行测试，对有效的预测因素或结果知之甚少 ER趋势 - 如果ER在日常生活中不熟练雇用ER，那么ER的能力是无关紧要的。 此外，对ER趋势的检查可以表明个人如何考虑其特定特征 和能力，可以将特定的ER策略与他们遇到的特定情况相匹配。目标 该应用是为了评估无效ER的组成部分 - 无论是在实验室还是在日常生活中） 严重性和遇险障碍和功能的过程。与NIMH战略对象一致 RDOC框架，该项目结合了多模式评估，尺寸症状测量和 机器学习方法。拟议的研究将完成300名成年人，已超采样以升高 PNT（50％）。参与者将完成实验室评估，以衡量ER容量的预测指标，然后是10- 日生的瞬时评估研究研究了日常生活中ER趋势的预测指标。更好的捕获 负面情绪发生时，日常生活中的报道将通过检测到的算法触发物理触发 心理压力的潜在发作。有效的ER将在实验室中以多种方式进行操作，并且 在日常生活中，使用自我报告的情感，身体指标和感知的ER成功变化。有效的ER 然后，能力和趋势将被检查为遇险症状，功能和福祉的预测因素 每月评估12个月的轨迹。此外，探索目的是从 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与临床相关的框架，以使人如何 级别，情况级别和ER策略使用互动以预测最佳ER。总体而言，该项目有助于 通过更精确地隔离密钥来确定和完善个性化干预措施的目标和完善目标 特定个体的有效ER机制以及他们日常生活中遇到的环境。