Novel Methods for Evaluation and Implementation of Behavioral Intervention Technologies for Depression

 描述(由申请人提供)：严重抑郁障碍(MDD)预计将成为全球和美国疾病负担的主要原因。仅在美国，仅在2012年，估计就有1600万18岁及以上的成年人(占所有成年人的7%)至少有一次严重的抑郁发作。虽然心理治疗在治疗抑郁症方面是有效的，但MDD的高患病率使标准的一对一强化心理治疗无法满足人群的需求。行为干预技术(BITS)使用手机等技术来支持行为改变，以改善心理健康，并已被证明具有类似于心理治疗和药物治疗的效果。随着手机用户数量的不断增加，BIT是提供心理治疗的一个可行和有前途的选择。另一方面，目前新干预措施的评价框架不足以评价和实施双边投资条约，因为双边投资条约的格局迅速演变，干预措施也很复杂。这项研究旨在开发和验证新的概念和评估框架，以应对在实用环境下在MDD患者中传播和实施BITS的这两个挑战。我们计划分四步实现这一研究目标。首先，我们将开发一种新的统计设计，称为开放式自适应随机化(OAR)程序，它将使我们能够连续评估进入和离开护理提供系统的BIT。OAR还旨在通过根据部署期间的临时证据按顺序将患者分配到远离劣质BIT的位置，从而提高对参与患者的护理质量。其次，我们将开发一种数据分析技术，称为正则化Q学习，它将使我们能够在高维环境中进行变量选择，并在学习模型中仅保留对健康结果的重要预测因素。虽然最初的Q-学习是一种起源于计算机科学文献的尖端技术，但该研究将扩展其处理能力 通过引入正则化回归，丰富了学习模型。第三，我们将通过计算机模拟校准方法，通过分析当前随机临床试验的数据创建初始知识库，确定与医疗保健提供者和应用程序管理平台的合作伙伴关系，为拟议方法的下一个实施阶段做准备。第四，我们将通过出版出版物、建立认知计算系统、跟踪被引用的来源以及更广泛的卫生研究社区对已发表成果的采用，来倡导建议的方法的普遍实施。我们的长期目标是增强我们在整个医疗系统中以个性化和循证的方式向抑郁症患者部署BITS等复杂干预措施的能力。

 DESCRIPTION (provided by applicant): Major depressive disorder (MDD) is projected to be a leading cause of burden of disease globally and in the United States. In the United States, in 2012 alone, an estimated 16 million adults aged 18 or older (7% of all adults) had at least one major depressive episode. While psychological treatments are effective at treating depression, the high prevalence of MDD makes it impossible to meet the needs in the population with standard one-to-one intensive psychological treatments. Behavioral intervention technologies (BITs) use technologies such as mobile phones to support behavior change to improve mental health, and have been shown to have similar effects to psychotherapy and pharmacotherapy. With the growing number of mobile phone users, BIT is a viable and promising option for delivering psychotherapy. On the other hand, the current evaluation framework of new interventions is not adequate for evaluating and implementing BITs, because of the rapidly evolving BIT landscape and the complexity of the interventions. This research aims to develop and validate novel concepts and evaluation framework to address these two challenges in the dissemination and implementation of BITs in MDD patients in pragmatic settings. We plan to achieve this research goal in four steps. First, we will develop a new statistical design, called open-ended adaptive randomization (OAR) procedure, which will enable us to continuously evaluate BITs that enter and leave a care delivery system. The OAR also aims to improve quality of care given to the participating patients, by sequentially allocating patients away from inferior BITs based on the interim evidence during deployment. Second, we will develop a data analytical technique, called regularized Q-learning, which will enable us to perform variable selection in high-dimensional settings and retain only the important predictors of health outcomes in the learning model. While the original Q-learning is a cutting-edge technique originating from the computer science literature, the research will extend its capability to handle high-dimensional data and enrich the learning model by incorporating regularized regression. Third, we will prepare for the next implementation phase of the proposed methods, by calibrating the methods with computer simulations, creating an initial knowledge base by analyzing data from current randomized clinical trials, identifying partnerships with healthcare providers and app curation plaftorms. Fourth, we will advocate for the general implementation of the proposed methods by producing publications, building cognitive computing systems, and tracking the source of citation and adoption of the published results by the broader health research community. Our long-term goal is to enhance our capability of deploying complex interventions such as BITs to depressed patients in a personalized and evidence-based manner throughout the healthcare system.