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

关注情绪调节(ER)的治疗无反应(NR)的预测指标应该是生态有效的，而且容易获得，成本很低。这样的预报器使治疗-通知单一病例-预测成为可能。在实验室中收集NR的神经和心理生理预测因子是耗时的，而且在大学以外的环境下很难实现。临床实践需要将神经NR信号和内在化障碍的相关ER-(dys-)功能的预测价值转移到临床环境中的替代指标，并且可以在不同的治疗期间(例如，治疗前、治疗期间和治疗后)对治疗师和患者进行最小限度的重复评估。本项目嵌入研究股，使用数字表型来派生这些代用指标。我们使用智能手机和可穿戴设备等个人电子设备主动和被动收集的数据，以ER指标为主导核心结构，得出NR风险个体的表型。智能手机尤其适合这一目的。首先，智能手机允许通过生态瞬时评估(EMA，即对当前心理状态和活动的重复短期调查)来主动和生态有效地评估情绪和认知。其次，智能手机的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(例如可穿戴设备)被动收集的有关行为、生理和情绪的多模式客观数据。本项目(SP6)在CBT治疗之前收集了传感器和EMA的数据，重点是来自内化谱的468名精神障碍患者的ER(例如，患有特定恐惧症、社交焦虑症、恐慌症、广场恐惧症、广泛性焦虑症、强迫症、创伤后应激障碍、单相抑郁障碍的患者)。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12个月后的另外两次测量突发)。基于传感器的移动设备评估侧重于(A)调节(例如，体力活动、智能手机使用)、(B)影响个体调节情绪的能力(例如，睡眠、心率变异性)、或(C)作为调节努力的替代或影响ER的环境特征(例如，GPS跟踪的活动模式、体力活动和智能手机使用的空间分辨率)的标记。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。结合其余SP的数据模式，本SP提供了一个独特的机会，以建立一个全面的跨层理解ER及其在自然环境下的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.