A cross-cultural EMA study including digital, affective, and interpersonal markers to enhance the prediction of treatment response and dropout for patients with depression

这个项目的目的是基于治疗前密集的纵向数据(生态瞬时评估；EMA)，对抑郁症患者的治疗成功和停止进行前瞻性预测。这项研究将包括N=300名在三个临床地点(德克萨斯州特里尔大学、JUSTUS利比希大学和布宜诺斯艾利斯大学)之一接受认知行为疗法(CBT)的患者的样本。在开发阶段，将使用训练样本(n=150；50%)来开发预测模型，该模型将在验证阶段(n=150；50%)进行前瞻性测试。在治疗开始之前，在为期两周的诊断阶段，将通过智能手机使用电子日记每天六次测量积极和消极的情绪以及人际苦恼。与此同时，数字表型数据(即心率变异性、睡眠时间和体力活动)将使用健康跟踪器进行被动测量。为了评估治疗的成功，患者将完成患者健康问卷-9(PHQ-9)，而停止治疗将由治疗师的报告进行评估。基于这些信息，将开发治疗成功和停止治疗的预测模型(EMA丰富的预测模型；EPM)，并进行前瞻性评估。主要的研究问题是，使用电子日记和数字健康跟踪器进行复杂的测量是否可以改善基于一次性评估的预测模型(即横断面模型；CSM)的准确性。CSM基于治疗开始前的一次性变量收集。此外，还将调查关于EPM准确性的个别站点效应(UT、JLU和UBA)，以收集关于算法跨文化有效性的证据。这个项目将是第一个进行临床个体预测的项目，使用量化的具体方法来建立患者的情感、人际和数字表型动力学。此外，这项研究将是第一次对这些算法进行前瞻性测试，并调查它们的跨文化推广能力。这一项目的结果将对心理治疗的个性化具有重要的临床意义。此外，该项目的结果将促进跨文化有效性和结果的复制，使具有不同文化背景的研究小组能够进行长期合作。

The aim of this project is the prospective prediction of therapy success and discontinuation in patients with a depressive disorder based on pre-treatment intensive longitudinal data (Ecological Momentary Assessments; EMA). A sample of N = 300 patients receiving cognitive-behavioral therapy (CBT) at one of three clinical sites (University of Trier, UT; Justus Liebig University Gießen, JLU; and University of Buenos Aires, UBA) will be included in this study. In a development phase, a training sample (n = 150; 50%) will be used to develop a prediction model, which will be prospectively tested in a validation phase (n = 150; 50%). Before treatment start, positive and negative affect and interpersonal distress will be measured six times a day during a two-week diagnostic phase, using electronic diaries via smartphones. At the same time, data for digital phenotyping (i.e., heart rate variability, sleep duration and physical activity) will be passively measured using a fitness tracker. To assess therapy success, patients will complete the Patient Health Questionnaire-9 (PHQ-9), while discontinuation will be assessed by therapists’ reports. Based on this information, predictive models (EMA-enriched predictive models; EPM) for therapy success and discontinuation will be developed, and prospectively evaluated. The primary research question is whether complex measurements using electronic diaries and digital fitness trackers can improve predictive models based on one-time assessments (i.e., cross-sectional models; CSM) in terms of their accuracy. The CSM are based on the one-time collection of variables before the beginning of treatment. In addition, the individual site effects (UT, JLU and UBA) regarding the EPM’s accuracy will be investigated to gather evidence on the cross-cultural validity of the algorithms. This project will be the first to perform clinical individual predictions, using quantitative idiographic methods to establish patients’ affective, interpersonal, and digital phenotyping dynamics. Additionally, this study will be the first in prospectively testing the algorithms’ and investigate their cross-cultural generalizability. The results of this project will have important clinical implications for the personalization of psychotherapeutic treatments. In addition, the findings of the project will promote cross-cultural validity and replication of results enabling long-term cooperation between research groups with heterogeneous cultural backgrounds.