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

一项跨文化 EMA 研究，包括数字、情感和人际标记，以增强对抑郁症患者治疗反应和退出的预测

• 批准号: 504507043
• 负责人: Professor Dr. Wolfgang Lutz
• 金额: --
• 依托单位: Abteilung für Klinische Psychologie und Psychotherapie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/504507043?language=en
• 关键词: cross; cultural; EMA; study; including

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.

该项目的目的是基于治疗前密集纵向数据（生态瞬时评估; EMA）对抑郁症患者的治疗成功和停药进行前瞻性预测。本研究将纳入N = 300例在三个临床研究中心（University of Trier，UT; Justus Liebig University Gießen，JLU;和University of Buenos Aires，乌巴）之一接受认知行为治疗（CBT）的患者样本。在开发阶段，将使用训练样本（n = 150; 50%）开发预测模型，并在验证阶段（n = 150; 50%）对该模型进行前瞻性测试。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测量六次积极和消极的影响以及人际关系困扰。同时，数字表型分析的数据（即，心率变异性、睡眠持续时间和身体活动）将使用健身追踪器被动地测量。为了评估治疗成功，患者将完成患者健康问卷-9（PHQ-9），而中止治疗将通过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药的预测模型（EMA富集预测模型; EMA），并进行前瞻性评价。主要的研究问题是，使用电子日记和数字健身追踪器的复杂测量是否可以改善基于一次性评估的预测模型（即，横截面模型; CSM）的准确性。CSM基于治疗开始前一次性收集的变量。此外，个别网站的影响（UT，JLU和乌巴）有关的算法的准确性将进行调查，以收集证据的跨文化有效性的算法。该项目将是第一个进行临床个体预测的项目，使用定量的具体方法来建立患者的情感，人际关系和数字表型动力学。此外，这项研究将是第一个前瞻性地测试算法的，并调查其跨文化的普遍性。该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，该项目的研究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长期合作。