Digital Phenotyping of emotion (dys-)regulation as transdiagnostic process and proxy for clinical and neurobiological markers of treatment (non-)response

关注于情绪调节（ER）的治疗无反应（NR）的预测因子应该是生态有效的，并且容易以低成本获得。这样的预测器使治疗信息的单个病例预测成为可能。在实验室环境中收集NR的神经和心理生理预测因子是耗时且难以在大学环境之外实施的。临床实践需要将神经nr特征的预测价值和内化障碍的相关ER-（日）功能转移到临床环境中的代理，并且可以在不同的治疗期间（例如，治疗前、治疗中和治疗后）对治疗师和患者进行重复评估。嵌入在研究单位，目前的项目使用数字表型来获得这样的代理。我们使用个人电子设备（如智能手机和可穿戴设备）主动和被动收集的数据，基于ER指标作为总体核心结构，推导出NR风险个体的表型。智能手机特别适合这一目的。首先，智能手机允许通过生态瞬间评估（EMA，即对当前精神状态和活动的重复简短调查）积极和生态有效地评估情绪和认知。其次，智能手机传感器丰富的环境提供了通过内置应用程序和连接的智能设备（如可穿戴设备）被动收集的关于行为、生理和情绪的多模态客观数据。本项目（SP6）在进行CBT治疗之前，收集了n = 468名内化谱精神障碍患者（例如患有特定恐惧症、社交焦虑症、恐慌症、广场恐怖症、广泛性焦虑症、强迫症、创伤后应激障碍、单极抑郁症）的ER传感器和EMA数据。此外，我们在n = 350的子样本中收集了整个治疗过程中的传感器和EMA数据（T20和后/12个月时的2次额外测量爆发）。基于传感器的移动设备评估侧重于以下标记：(A)调节（例如，身体活动，智能手机使用），(B)影响个人调节情绪的能力（例如，睡眠，心率变异性），或(C)作为影响ER的调节努力或环境特征的代理（例如，GPS跟踪的活动模式，身体活动和智能手机使用的空间分辨率）。ER指标的计算基于(A)自我报告的情绪体验的动态和/或(B)自我报告的ER策略应用的模式。结合剩余SP的数据模式，该SP提供了一个独特的机会，可以建立对ER的全面跨层面理解及其在自然环境下NR预测的价值。

Predictors of treatment non-response (NR) that focus on emotion regulation (ER) should be ecologically valid, readily available at low-cost. Such predictors enable treatment-informing single case-predictions. Collecting neural and psycho-physiological predictors of NR in a laboratory context is time-consuming and difficult to implement outside of the university context. Clinical practice needs proxies that transfer the predictive value of neural NR-signatures and related ER-(dys-)functions of internalizing disorders into clinical settings and can be assessed repeatedly with minimal effort for therapists and patients during different treatment periods (e.g., before, during, and after therapy). Embedded within the Research Unit, the present project uses digital phenotyping to derive such proxies. We use data actively and passively collected by personal electronic devices such as smartphones and wearables, to derive phenotypes of individuals at risk of NR based on indicators of ER as the overarching core construct. Smartphones are particularly well-suited for this purpose. First, smartphones allow assessing mood and cognition actively and ecologically valid by means of Ecological Momentary Assessment (EMA, i.e., repeated short surveys of the current state of mind and activities). Second, the sensor-rich environment of smartphones provides multimodal objective data about behavior, physiology, and mood collected passively through built-in applications and connected smart devices (e.g., wearables). The present project (SP6) collects, prior to a CBT treatment, sensor- and EMA data focusing on ER in n = 468 patients with mental disorders from the internalizing spectrum (e.g., patients suffering from specific phobia, social anxiety disorder, panic disorder, agoraphobia, generalized anxiety disorder, obsessive-compulsive disorder, post-traumatic stress disorder, unipolar depressive disorders). Furthermore, we collect sensor and EMA data (2 additional measurement bursts at T20, and post/12-months) throughout the entire treatment in a subsample of n = 350. The sensor-based assessment on mobile devices focusses on markers that are either (A) regulatory (e.g., physical activity, smartphone usage), (B) affect the individual's ability to regulate emotions (e.g., sleep, heart rate variability), or (C) serve as proxies for regulatory efforts or environmental characteristics that influence ER (e.g., GPS tracked spatial resolution of activity patterns, physical activity, and smartphone use). ER indicators are calculated based on (A) the dynamics of self-reported emotional experiences and/or (B) patterns in the self-reported application of ER strategies. Combined with the data modalities of the remaining SPs, this SP provides a unique opportunity to establish a comprehensive cross-level understanding of ER and its value for NR prediction in naturalistic settings.