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

抑郁症行为干预技术评估和实施的新方法

• 批准号: 9083697
• 负责人: Ken Cheung
• 金额: $ 41.2万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-05-20 至 2020-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9083697
• 关键词: Accounting; Address; Adherence; Adopted; Adoption; Adult; Advocate; Algorithms; Anxiety; Behavior Therapy; Car Phone; Caring; Cellular Phone; Clinical Trials; Cognitive; Communities; Complex; Computational algorithm; Computer Simulation; Computer Systems; Computer software; Computers; Data; Data Analyses; Development; Effectiveness; Elements; Emerging Technologies; Engineering; Evaluation; Evidence based intervention; Evidence based practice; Goals; Health; Health Care Research; Health Personnel; Health Services Research; Healthcare Systems; High Prevalence; Inferior; Institute of Medicine (U.S.); Intelligence; Intervention; Learning; Left; Literature; Major Depressive Disorder; Marketing; Mental Depression; Mental Health; Mental Health Services; Methods; Modeling; Morbidity - disease rate; National Institute of Mental Health; Outcome; Patient-Focused Outcomes; Patients; Pharmaceutical Preparations; Pharmacotherapy; Phase; Population; Primary Health Care; Procedures; Process; Productivity; Provider; Psychological reinforcement; Psychotherapy; Public Health; Publications; Publishing; Quality of Care; Quality of life; Randomized; Randomized Clinical Trials; Research; Research Priority; Science; Source; Statistical Methods; Strategic Planning; System; Tablets; Techniques; Technology; Testing; TimeLine; Translations; United States; Work; aged; analytical tool; base; behavior change; burden of illness; care delivery; caregiving; computer science; cost; depressed patient; design; dissemination research; effective therapy; evidence base; functional status; implementation research; improved; innovation; knowledge base; meetings; models and simulation; mortality; novel; preference; psychologic; public health relevance; randomized trial; sensor; single episode major depressive disorder; statistics; technology development

 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.

 描述（由适用提供）：主要的抑郁症（MDD）预计是全球伯恩疾病的主要原因。在美国，仅在2012年，估计有1600万年龄在18岁或以上（占所有成年人的7％）的成年人至少有一次主要的抑郁发作。尽管心理治疗可有效治疗抑郁症，但MDD的高流行率使得无法通过标准的一对一密集的心理治疗来满足人群的需求。行为干预技术（BITS）使用诸如手机之类的技术来支持行为改变以改善心理健康，并已被证明对心理治疗和药物治疗具有相似的影响。随着手机用户数量的越来越多，BIT是提供心理治疗的可行且有希望的选择。另一方面，由于迅速发展的位景观和干预措施的复杂性，新干预措施的当前评估框架不足以评估和实施位。这项研究旨在开发和验证新颖的概念和评估框架，以应对在务实环境中MDD患者传播和实施这两个挑战。我们计划通过四个步骤来实现这一研究目标。首先，我们将开发一种称为开放式自适应随机化（OAR）程序的新统计设计，重要的是要使我们继续评估进入和留下护理输送系统的位。 OAR还旨在通过根据部署期间的临时证据将患者依次分配给参与患者的护理质量。其次，我们将开发一种称为正规Q学习的数据分析技术，这将使我们能够在高维设置中执行可变选择，并仅保留学习模型中健康结果的重要预测指标。虽然原始的Q学习是源自计算机科学文献的尖端技术，但该研究将扩展其处理能力 高维数据并通过转换正规回归来丰富学习模型。第三，我们将通过使用计算机模拟来校准该方法的下一个实施阶段，从而通过分析当前随机临床试验的数据来创建初始知识基础，从而确定与医疗保健提供者的合作伙伴关系和应用程序策划型的合作伙伴。第四，我们将主张通过生产出版物，构建认知计算系统以及跟踪广泛健康研究社区发布和采用的引文来源和采用引用和采用的来源，来倡导提出的方法的一般实施。我们的长期目标是增强我们在整个医疗保健系统中以个性化和基于证据的方式来部署复杂干预措施（例如位）的能力。