Dynamical System /Related Engineering Approach /Improving Behavioral Intervention

描述（由申请人提供）：本研究将发展方法，以加强行为干预发展和评估领域的研究。行为干预旨在通过使用依赖于减少不健康行为和促进健康行为的策略来预防和治疗疾病。这些干预措施在药物滥用、艾滋病毒/艾滋病、癌症、心理健康、糖尿病、肥胖、心血管健康和老龄化等对公共卫生具有重要意义的广泛领域发挥着日益突出的作用。标准治疗/对照随机临床试验（RCT）为确定行为干预是否有效提供了原则性的方法学框架。拟议的研究将制定相应的原则方法框架，以优化建筑干预措施，使其在最高功效（理想条件下的影响），有效性（现实条件下的影响）和效率（与资源使用有关的影响）下运行。跨学科的研究团队包括一名行为科学家和一名作为PI的工程师，统计学家和一个由来自不同公共卫生领域的八名行为干预科学家组成的杰出小组。所提出的优化行为干预的框架是基于工程中广泛使用的方法。本研究将调整这些方法用于行为干预。这些方法包括将行为干预表达为详细的动态模型。动态模型非常适合行为干预，行为干预通常是复杂的多变量、多层次时变过程。在行为干预的动态模型被表达后，它就可以作为既定工程程序的一部分来优化干预。这个项目有三个具体目标。首先是与行为干预科学家小组的每个成员合作，将干预表达为详细的动态系统模型，然后将工程优化方法（如内模控制和模型预测控制）应用于每个模型。第二个具体目标是开发、记录和传播一个计算机程序，行为科学家可以用它来模拟作为动态系统的行为干预，并对其应用优化技术。第三个具体目标是为进一步适应工程优化方法在行为科学中的应用奠定基础。这部分项目将侧重于系统识别和多级优化。拟议的研究的好处将扩展到使用行为干预来预防和治疗疾病的任何公共卫生领域，因为它将导致行为干预在降低发病率和死亡率方面更加有效、有效和高效。提出的工作将直接导致改善预防和治疗疾病的行为干预。任何采用行为干预措施的公共卫生领域都将受益于由此产生的干预效果、效力和效率的提高以及相应的发病率和死亡率的降低。

DESCRIPTION (provided by applicant): This study will develop methods to enhance the conduct of research in the area of behavioral intervention development and evaluation. Behavioral interventions aim to prevent and treat disease by using a strategy that relies on reducing unhealthful behaviors and promoting healthful behaviors. These interventions play an increasingly prominent role in a wide variety of areas of public health importance, including drug abuse, HIV/AIDS, cancer, mental health, diabetes, obesity, cardiovascular health, and aging. The standard treatment/control randomized clinical trial (RCT) provides a principled methodological framework for establishing whether behavioral interventions work. The proposed research will develop a corresponding principled methodological framework for building interventions that have been optimized so that they are operating at peak efficacy (impact under ideal conditions), effectiveness (impact in real-world conditions) and efficiency (impact in relation to use of resources). The interdisciplinary research team includes a behavioral scientist and an engineer as PI's, statisticians, and a distinguished panel of eight behavioral intervention scientists from different public health areas. The proposed framework for optimizing behavioral interventions is based on methods widely used in engineering. This research will adapt these methods for use in behavioral interventions. The methods involve expressing behavioral interventions as detailed dynamical models. Dynamical models are well suited to behavioral interventions, which are typically complex multivariate multi-level time-varying processes. After a dynamical model of a behavioral intervention has been expressed, it can then used as part of established engineering procedures to optimize the intervention. This project has three Specific Aims. The first is to work with each member of the panel of behavioral intervention scientists to express an intervention as a detailed dynamical system model, and then to apply engineering optimization methods, such as Internal Model Control and Model Predictive Control, to each one. The second Specific Aim is to develop, document, and disseminate a computer program that behavioral scientists can use to model behavioral interventions as dynamical systems and apply optimization techniques to them. The third Specific Aim is to lay the groundwork for further adaptation of engineering optimization approaches for use in behavioral science. This part of the project will focus on system identification and multi-level optimization. Benefits of the proposed research will extend to any area of public health that employs behavioral interventions for prevention and treatment of disease, because it will result in behavioral interventions that are more efficacious, effective, and efficient at reducing morbidity and mortality. The proposed work will lead directly to improved behavioral interventions for prevention and treatment of disease. Any area of public health that employs behavioral interventions will benefit from the resulting increase in intervention efficacy, effectiveness and efficiency and corresponding reduction in morbidity and mortality.