mDOT TR&D2 (Optimization): Dynamic Optimization of Continuously Adapting mHealth Interventions via Prudent, Statistically Efficient, and Coherent Reinforcement Learning

mDOT TR

基本信息

批准号：
10541807
负责人：
SUSAN A MURPHY
金额：
$ 19.46万
依托单位：
UNIVERSITY OF MEMPHIS
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-15 至 2025-11-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10541807
关键词：
Acceleration Accounting Address Behavioral Biological Markers Cellular Phone Chronic Disease Clinical Cloud Computing Collaborations Communities Companions Data Detection Development Distal Distant Ensure Event Feedback Future Health Health Benefit Healthcare Individual Infrastructure Intervention Lead Learning Location Maps Medical Medicine Methodology Methods Modeling Outcome Patients Persons Population Principal Investigator Psychological reinforcement Research Research Personnel Resources Risk Risk Estimate Services Software Framework Software Tools Stress Structure System Technology Testing Time Training Translations Treatment Efficacy Universities Variant Work biopsychosocial cloud based cohort data sharing flexibility habituation healthy lifestyle improved interest intervention delivery intervention effect learning algorithm learning strategy mHealth member multiple chronic conditions next generation online intervention open source personalized decision personalized intervention prevent public health relevance sensor sensor technology social software systems synergism technological innovation technology research and development tool treatment effect wearable device

项目摘要

Project Lead: Murphy, Susan Principal Investigator: Kumar, Santosh TR&D2: Dynamic Optimization of Continuously Adapting mHealth Interventions via Prudent, Statistically Efficient, and Coherent Reinforcement Learning Lead: Dr. Susan Murphy, Harvard University; 10% effort (1.2CM) Abstract: The mHealth Center for Discovery, Optimization & Translation of Temporally-Precise Interventions (the mDOT Center) will enable a new paradigm of temporally-precise medicine to maintain health and manage the growing burden of chronic diseases. The mDOT Center will develop and disseminate the methods, tools, and infrastructure necessary for researchers to pursue the discovery, optimization and translation of temporally- precise mHealth interventions. Such interventions, when dynamically personalized to the moment-to-moment biopsychosocial-environmental context of each individual, will precipitate a much-needed transformation in healthcare by enabling patients to initiate and sustain the healthy lifestyle choices necessary for directly managing, treating, and in some cases even preventing the development of medical conditions. Organized around three Technology Research & Development (TR&D) projects, mDOT represents a unique national resource that will develop multiple methodological and technological innovations and support their translation into research and practice by the mHealth community in the form of easily deployable wearables, apps for wearables and smartphones, and a companion mHealth cloud system, all open-source. Technology Research and Development project 2 (TR&D2) will address three key limitations of current online reinforcement learning (RL) when applied to personalize mobile interventions to individuals. Two of these limitations are related to the need to increase efficacy and reduce negative delayed intervention burden effects leading to disengagement. The third looks to future needs involving the personalization of multiple intervention components each operating at a different time scale. In particular, we will accommodate the ever-present mobile health challenge of user disengagement by developing a continuum of approaches between RL algorithms that ignore delayed intervention effects and RL algorithms that attempt to capture noisy delayed intervention effects over a more distant future. Second, we will increase the rate at which personalization occurs via optimally leveraging data across time and across users to more quickly personalize the interventions to each user. Third, we will develop the first RL approaches to coherently personalize multiple intervention components holistically. In addition, to enhance impact and dissemination, the methods will be developed in close collaboration with three collaborative projects with an emphasis on model interpretability. We will provide the two service projects and the broader research community with open-source software tools and systems consisting of smartphone and cloud computing components for online personalization. TR&D2 will synergistically work in partnership with the other TR&D projects, the Training and Dissemination Core, and the Administration Core to maximize the societal impact of TR&D2 technologies. 0

项目负责人：墨菲，苏珊首席研究员：库马尔，桑托什 TR＆D2：通过审慎的持续调整MHealth干预措施的动态优化，统计学上有效且连贯的增强学习负责人：哈佛大学苏珊·墨菲（Susan Murphy）博士； 10％的努力（1.2厘米）摘要：MHealth发现，优化和翻译的暂时性干预中心（MDOT中心）将使暂时药物的新范式保持健康和管理慢性疾病的负担越来越大。 MDOT中心将开发和传播方法，工具，研究人员追求时间的发现，优化和翻译所需的基础设施精确的MHealth干预措施。这样的干预措施，当动态性地对瞬间每个个体的生物心理社会环境环境将促成急需的转变通过使患者能够直接启动和维持健康的生活方式选择来进行医疗保健管理，治疗，在某些情况下甚至阻止医疗状况的发展。有组织 MDOT大约三个技术研发（TR＆D）项目，代表了一个独特的国家将开发多种方法论和技术创新并支持其翻译的资源以易于部署的可穿戴设备的形式进入MHealth社区的研究和练习可穿戴设备和智能手机，以及配套MHealth云系统，都是开源的。技术研发项目2（TR＆D2）将解决当前在线的三个关键局限强化学习（RL）应用于个性化移动干预措施。其中两个限制与增加疗效并减少负面延迟干预负担有关导致脱离接触。第三种涉及多个干预个性化的未来需求每个组件都以不同的时间尺度运行。特别是，我们将容纳永远存在的手机通过在RL算法之间开发一种方法，对用户脱离的健康挑战忽略试图捕获嘈杂延迟干预效果的延迟干预效果和RL算法在更遥远的未来。其次，我们将提高通过最佳方式进行个性化发生的速率利用跨时间和跨用户的数据，以更快地个性化每个用户的干预措施。第三，我们将开发第一种RL方法，以整体地将多个干预组件个性化。此外，为了增强影响和传播，该方法将与三个密切合作开发协作项目着重于模型可解释性。我们将提供两个服务项目，具有开源软件工具和系统的更广泛的研究社区，包括智能手机和用于在线个性化的云计算组件。 TR＆D2将与其他TR＆D项目，培训和传播核心以及管理核心，以最大化社会 TR＆D2技术的影响。 0