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资助，名为“智能与互联健康”（SCH），旨在加速创新方法的开发和使用，以支持急需的医疗转型。全民医疗保健。肥胖流行是近年来心脏病、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，是一种科学尚未成功理解的行为，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新的范式，但直接关注饮食和活动的与肥胖相关的努力不仅受到行为跟踪（特别是饮食摄入量）准确性的阻碍，而且还由于缺乏行为理论和方法而受到阻碍。个性化即时适应性干预（JITAI）的动态模型。当前的行为科学表明，家庭饮食动态（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机会，将原位肥胖研究和干预的重点从已证明难以监测、建模和修改的行为（例如，吃什么和吃多少）转移到家庭用餐时间和家庭食物环境（例如，谁在吃饭、何时、何地、与谁一起吃饭、人际压力），提供通过遥感监测和建模 (M2) 行为的机会，以及通过个性化、适应性强、真实的方式成功改变行为的潜力-时间该项目提出了 M2FED，这是一个由家庭信标、无线和可穿戴传感器以及智能手机组成的集成系统，用于收集同步的实时 FED 数据，这些数据将用于基于该数据迭代开发动态、情境化的 FED 系统模型。技术、表意模型以及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，以解决肥胖预防和治疗实时行为建模的远程、连续数据捕获的基本挑战。行为科学家传统上无法访问实时数据和动态模型，而工程师也没有专业知识来确定要监视和建模的内容或要提供哪些反馈。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，并且具有更大的积极和长期改变的潜力。实现 M2FED 系统的基本技术研究挑战包括独特的个人家庭定位、饮食检测、混响环境中的谈话压力和情绪评估，以及包括整个家庭系统本身的异构传感和通信系统的系统框架。基本的行为研究挑战包括基于过去和当前对 FED 状态以及对 FED 产生时间和因果影响的内部和人际状态和事件的观察来对 FED 进行实时建模。虽然该项目是在肥胖/美联储关系（其本身有可能对人类健康和医疗保健成本产生广泛影响）的背景下进行的，但该项目还概括了一个框架，包括基于证据的系统和实验平台，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外展和教育机会，向公众通报（并被公众通报），并培养一支能够更好地应对健康管理和健康保护方面以人类行为为中心的基本挑战的劳动力队伍。

项目成果

A Weakly Supervised Learning Framework for Detecting Social Anxiety and Depression

用于检测社交焦虑和抑郁的弱监督学习框架

• DOI: 10.1145/3214284
• 发表时间: 2018-07-05
• 期刊: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
• 作者: Asif Salekin;J. W. Eberle;Jeffrey J. Glenn;B. Teachman;J. Stankovic
• 通讯作者: J. Stankovic

Dynamic Integration of Heterogeneous Transportation Modes Under Disruptive Events

颠覆性事件下异构交通模式的动态整合

• 发表时间: 2018-01
• 期刊: ACM/IEEE International Conference on Cyber-Physical Systems
• 作者: Yuan, Y.;Zhang, D.;Miao, F.;Stankovic, J.;He, T.;Pappas, G.;Lin, S.
• 通讯作者: Lin, S.

M2G: A Monitor of Monitoring Systems with Ground Truth Validation Features for Research-Oriented Residential Applications

M2G：具有地面实况验证功能的监控系统监视器，适用于研究型住宅应用

• 发表时间: 2017-01
• 期刊: MASS
• 作者: Ma, C.;Alam, R.;Bell, B.;de la Haye, K.;Spruijt;Lach, J.;Stankovic, J.
• 通讯作者: Stankovic, J.