mDOT TR&D1 (Discovery) - Enabling the Discovery of Temporally-Precise Intervention Targets and Timing Triggers from mHealth Biomarkers via Uncertainty-Aware Modeling of Personalized Risk Dynamics

mDOT TR

• 批准号: 10541804
• 负责人: James M. Rehg
• 金额: $ 16.52万
• 依托单位: UNIVERSITY OF MEMPHIS
• 依托单位国家: 美国
• 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/10541804
• 关键词: Address; Adverse event; Architecture; Area; Awareness; Behavior; Biological Markers; Budgets; Cellular Phone; Chronic Disease; Collection; Communities; Companions; Complement; Complex; Computer software; Data; Data Analyses; Data Sources; Decision Making; Development; Devices; Disease Progression; Dropout; Effectiveness; Ensure; Event; Exercise; Fatigue; Feedback; Generations; Graph; Health; Health behavior change; Healthcare; Individual; Infrastructure; Intervention; Investigation; Knowledge; Lead; Learning; Length; Life; Maintenance; Measures; Medical; Medicine; Metadata; Methods; Modality; Modeling; Moods; Nature; Neural Network Simulation; Non-linear Models; Outcome; Participant; Patients; Pattern; Physical activity; Population; Privacy; Process; Psychological reinforcement; Research; Research Personnel; Resources; Risk; Risk Assessment; Risk Factors; Running; Series; Services; Sleep; Smoking; Stress; System; Technology; Time; Training; Translations; Treatment Efficacy; Uncertainty; Work; adaptive intervention; biopsychosocial; cigarette smoking; cloud based; craving; data modeling; design; field study; forgetting; healthy lifestyle; improved; interest; learning strategy; mHealth; machine learning method; mobile sensor; multimodality; neural network; novel; open source; patient engagement; personalized learning; prevent; public health relevance; recurrent neural network; sedentary lifestyle; sensor; success; synergism; technological innovation; technology research and development; therapy design; tool; usability; wearable device; wireless

Project Lead: Rehg, Jim Primary Investigator: Kumar, Santosh TR&D1: Enabling the Discovery of Temporally-Precise Intervention Targets and Timing Triggers from mHealth Biomarkers via Uncertainty-Aware Modeling of Personalized Risk Dynamics Lead: Dr. Jim Rehg, Georgia Tech; 10% effort (0.9 CM) 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 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. TR&D1 will develop, evaluate and disseminate methods to analyze population-scale multi-modal time series of mHealth biomarkers to enable research on identifying the momentary risk factors and risk dynamics that drive adverse health outcomes, while accounting for the uncertainty and missingness inherent in these data sources. TR&D1 will do this under three aims. Aim 1 will address missing sensor data in mHealth field studies and develop state-of-the art imputation models using novel deep probabilistic neural networks that leverage the hierarchical nature of biomarker computation graphs. Aim 2 will address compressing a collection of biomarkers that serve as risk factors for a particular adverse health event into a single risk score, to support the online adaptation of decision rules in TR&D2, using longitudinal data that include multiple instances of adverse events and their contexts. In addition to risk scoring, we will also develop models for receptivity to intervention and participant engagement, which complement the assessment of risk in guiding intervention design. Aim 3 will begin to tackle the critical issue of providing model-based tools for identifying which potential risk factors actually impact risk in different contexts for different individuals, in order to support the intervention design process. TR&D1 will work with its collaborative projects to ensure that it focuses on the most pressing problems facing the mobile health research community. TR&D1 will disseminate its technologies to service projects and the community as software packages and cloud-based data analysis tools, to ensure the usability of these technologies by investigators who are external to the mDOT investigating team. TR&D1 will synergistically work in partnership with the other TR&D projects, the Training and Dissemination Core, and the Administration Core to maximize both the research and societal impact of TR&D1 technologies. 1

项目负责人：Rehg，Jim主要调查员：Kumar，Santosh Tr&d1：能够发现时间上精确的干预目标和时间触发因素 通过个性化风险动力学的不确定性感知建模的mHealth生物标记物 领队：佐治亚理工学院Jim Rehg博士；10%努力(0.9 CM) 摘要：移动健康中心发现、优化和翻译时间精确干预措施 (MDOT中心)将实现一种时间精准医学的新范式，以维持健康和管理 慢性病带来的负担越来越大。MDOT中心将开发和传播方法、工具、 和基础设施，为研究人员进行发现、优化和翻译临时- 精确的移动健康干预。这种干预，当动态地个性化到每一时刻时 每个人的生物-心理-社会-环境背景，将在 医疗保健，使患者能够开始并维持直接 管理、治疗，在某些情况下甚至阻止医疗条件的发展。有条理的 围绕着三个技术研发项目，MDOT代表着一个独特的国家 将开发多种技术创新并支持将其转化为研究和 MHealth社区以可轻松部署的可穿戴设备、可穿戴设备应用程序和 智能手机，以及与之配套的mHealth云系统，都是开源的。 技术研究与发展司将制定、评估和传播分析人口规模多模式时间序列的方法 移动健康生物标记物，使研究能够识别瞬时风险因素和推动 不利的健康后果，同时解释了这些数据来源固有的不确定性和缺失性。 Tr&d1将在三个目标下做到这一点。目标1将解决移动健康领域研究和开发中缺失的传感器数据 使用新型深度概率神经网络的最新归罪模型，该网络利用分层 生物标志物计算图的性质。目标2将解决压缩生物标记物集合的问题 作为特定不良健康事件的风险因素纳入单一风险分值，以支持在线适应 TR&D2中的决策规则，使用包括不良事件及其 上下文。除了风险评分，我们还将开发干预和参与者接受程度的模型 参与度，它补充了指导干预设计的风险评估。目标3将开始铲球 提供基于模型的工具以确定哪些潜在风险因素在 针对不同个体的不同背景，以支持干预设计过程。Tr&d1将起作用 及其协作项目，以确保其专注于移动医疗面临的最紧迫问题 研究社区。T&D将以软件的形式向项目和社区传播其技术 包和基于云的数据分析工具，以确保调查人员使用这些技术 是MDOT调查小组的外部人员。技术研究与开发主任将与其他研究与开发部门协同工作 项目，培训和传播核心，以及行政核心，以最大限度地提高研究和 技术研究与开发技术的社会影响。 1