SCH: INT: Collaborative Research: Monitoring and Modeling Family Eating Dynamics (M2 FED): Reducing Obesity Without Focusing on Diet and Activity

SCH：INT：合作研究：家庭饮食动态监测和建模 (M2 FED)：在不关注饮食和活动的情况下减少肥胖

• 批准号: 1521722
• 负责人: John Stankovic
• 金额: $ 68.93万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1521722&HistoricalAwards=false
• 关键词: SCH; INT; Collaborative; Research; Monitoring

This project is funded under a joint solicitation between the National Science Foundation and the National Institutes of Health, named "Smart and Connected Health" (SCH), which aims to accelerate the development and use of innovative approaches that would support the much needed transformation of healthcare across the entire population. The obesity epidemic is the primary cause of recent increases in heart disease, diabetes, cancer, and other diseases that place an untenable strain on healthcare and public health. One of the primary behavioral causes, i.e. dietary intake, is a behavior that science has had little success in understanding, much less affecting. Recent advances in remote sensing have provided a new paradigm for tracking human behavior, but obesity-related efforts focused directly on diet and activity have been hampered by not only the accuracy of behavior tracking (especially dietary intake) but also the lack of behavioral theories and dynamic models for personalized just-in-time, adaptive interventions (JITAIs). Current behavioral science suggests that family eating dynamics (FED) have high potential to impact child and parent dietary intake and obesity rates. The confluence of technology research and behavioral science research creates the opportunity to change the focus of in situ obesity research and intervention from behaviors that have proven difficult to monitor, model, and modify (e.g., what and how much is being eaten) to the family mealtime and home food environment (e.g., who is eating, when, where, with whom, interpersonal stress), providing opportunities for monitoring and modeling (M2) behavior via remote sensing, and the potential for successful behavior modification via personalized, adaptable, real-time feedback.This project proposes M2FED, an integrated system of in-home beacons, wireless and wearable sensors, and smartphones that collects synchronized real-time FED data that will be used to iteratively develop dynamic, contextualized FED systems models based on that data. The technology, ideographic models, and techniques to iteratively develop those models can guide future JITAIs and thus have a downstream positive impact on diet and ultimately obesity. The project brings together behavioral scientists, system scientists, obesity experts, computer scientists, and electrical engineers to address fundamental challenges of remote, continuous data capture for real-time behavior modeling for obesity prevention and treatment. Behavioral scientists traditionally have not had access to real-time data and dynamic models, while engineers have not had the expertise to identify what to monitor and model or what feedback to provide. This project connects complimentary expertise to develop a dramatically different approach to childhood obesity, focusing on behaviors, i.e. FED rather than diet, that can be more accurately monitored and modeled and have greater potential for positive and long-term modification. Fundamental technology research challenges in realizing the M2FED system include unique individual in-home localization, eating detection, conversation stress and mood assessment in reverberant environments, and a system-of-systems framework that includes heterogeneous sensing and communication systems across the family system itself. Fundamental behavioral research challenges include real-time modeling of FED based on past and ongoing observations of FED states and intra- and interpersonal states and events that create temporal and causal impact on FED. While this project is performed within the context of the obesity/FED relationship (which itself has the potential for sweeping impacts on human health and healthcare costs), the project also generalizes a framework, including both an evidence-based system and an experimental platform that extends to systems and applications beyond childhood obesity and behavior modification. The multidisciplinary nature of this work also provides new outreach and educational opportunities, informing (and being informed by) the public and preparing a workforce that is better equipped to address the fundamental human-behavior-centric challenges of health management and wellness preservation.

该项目由美国国家科学基金会和美国国立卫生研究院联合征集资金，名为“智能互联健康”（SCH），旨在加速创新方法的开发和使用，以支持急需的医疗保健转型整个人口。肥胖症的流行是最近心脏病、糖尿病、癌症和其他疾病增加的主要原因，这些疾病给医疗保健和公共卫生带来了难以承受的压力。主要的行为原因之一，即饮食摄入，是一种科学在理解方面几乎没有成功的行为，更不用说影响了。遥感技术的最新进展为跟踪人类行为提供了一个新的范例，但与肥胖相关的努力直接集中在饮食和活动上，不仅受到行为跟踪（特别是饮食摄入）准确性的阻碍，而且还缺乏行为理论和个性化即时适应性干预（JITAIs）的动态模型。目前的行为科学表明，家庭饮食动力学（FED）很有可能影响儿童和父母的饮食摄入量和肥胖率。技术研究和行为科学研究的融合创造了改变原位肥胖研究和干预的重点的机会，这些研究和干预已经被证明难以监测、建模和修改的行为（例如，正在吃什么和吃多少）到家庭用餐时间和家庭食物环境（例如，谁在吃饭，什么时候，在哪里，和谁在一起，人际压力），通过遥感提供监测和建模（M2）行为的机会，以及通过个性化，适应性强，实时反馈成功改变行为的潜力。该项目提出了M2 FED，一个集成系统的家庭信标，无线和可穿戴传感器，以及收集同步实时FED数据的智能手机，这些数据将用于基于该数据迭代开发动态的、情境化的FED系统模型。技术、表意模型和迭代开发这些模型的技术可以指导未来的JITAI，从而对饮食和最终的肥胖产生下游的积极影响。该项目汇集了行为科学家，系统科学家，肥胖专家，计算机科学家和电气工程师，以解决远程，连续数据捕获的基本挑战，用于肥胖预防和治疗的实时行为建模。传统上，行为科学家无法获得实时数据和动态模型，而工程师则没有专业知识来确定要监控和建模什么，或者提供什么反馈。该项目将互补的专业知识结合起来，开发一种截然不同的儿童肥胖方法，重点关注行为，即FED而不是饮食，可以更准确地监测和建模，并具有更大的积极和长期修改潜力。实现M2 FED系统的基础技术研究挑战包括独特的个人家庭定位，进食检测，混响环境中的谈话压力和情绪评估，以及包括跨家庭系统本身的异构传感和通信系统的系统框架。基本行为研究的挑战包括基于FED状态的过去和正在进行的观察以及对FED产生时间和因果影响的内部和人际状态和事件的FED实时建模。虽然该项目是在肥胖/FED关系的背景下进行的（这本身就有可能对人类健康和医疗保健成本产生全面影响），但该项目还概括了一个框架，包括一个循证系统和一个实验平台，该平台扩展到儿童肥胖和行为矫正之外的系统和应用。这项工作的多学科性质还提供了新的推广和教育机会，告知（并被告知）公众，并准备更好地应对健康管理和健康保护的基本人类行为为中心的挑战的劳动力。

项目成果

Dynamic Integration of Heterogeneous Transportation Modes under Disruptive Events

• DOI: 10.1109/iccps.2018.00015
• 发表时间: 2018-04
• 期刊: 2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS)
• 作者: Yukun Yuan;Desheng Zhang;Fei Miao;J. Stankovic;T. He;George Pappas;Shan Lin

M^2G: A Monitor of Monitoring Systems with Ground Truth Validation Features for Research-Oriented Residential Applications

M^2G：具有地面实况验证功能的监测系统监视器，适用于研究型住宅应用

• DOI: 10.1109/mass.2017.107
• 发表时间: 2017
• 期刊: 2017 IEEE 14th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)
• 作者: Meiyi Ma;Ridwan Alam;B. Bell;K. Haye;D. Spruijt;J. Lach;J. Stankovic
• 通讯作者: J. Stankovic