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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：Murphy， Susan首席研究员：Kumar， Santosh