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

该项目由美国国家科学基金会（National Science Foundation）和国立卫生研究院（National Institutes of Health）联合发起，名为“智能和互联健康”（SCH），旨在加速创新方法的开发和使用，以支持整个人口急需的医疗保健转型。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。其中一个主要的行为原因，即饮食摄入，是一种科学几乎无法理解的行为，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一种新的范式，但肥胖相关的研究主要集中在饮食和活动上，这不仅受到行为跟踪（特别是饮食摄入）准确性的阻碍，而且缺乏个性化即时适应性干预（JITAIs）的行为理论和动态模型。目前的行为科学表明，家庭饮食动态（FED）对儿童和父母的饮食摄入量和肥胖率有很大的影响。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被证明难以监测、建模和修改的行为（例如，吃什么和吃多少）转变为家庭用餐时间和家庭食物环境（例如，谁在吃、何时、何地、与谁一起吃、人际压力），为通过遥感监测和建模（M2）行为提供了机会。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M2FED，这是一个集成了家庭信标、无线和可穿戴传感器以及智能手机的集成系统，可以收集同步的实时FED数据，这些数据将用于迭代开发基于该数据的动态、情境化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，以实现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，而工程师也没有专业知识来确定需要监控和建模的内容，或者提供什么样的反馈。该项目结合了互补的专业知识，开发了一种截然不同的儿童肥胖方法，重点关注行为，即饮食而不是饮食，可以更准确地监测和建模，并具有更大的积极和长期改变的潜力。实现M2FED系统的基础技术研究挑战包括独特的个人在家定位、饮食检测、在混响环境下的谈话压力和情绪评估，以及一个系统的系统框架，包括跨家庭系统本身的异构传感和通信系统。基础行为研究的挑战包括基于过去和正在进行的对美联储状态的观察，以及对美联储产生时间和因果影响的内部和人际状态和事件，对美联储进行实时建模。虽然本项目是在肥胖/美联储关系的背景下进行的（这本身就有可能对人类健康和医疗成本产生全面影响），但该项目也概括了一个框架，包括一个以证据为基础的系统和一个实验平台，扩展到儿童肥胖和行为改变以外的系统和应用。这项工作的多学科性质也提供了新的推广和教育机会，告知（和被告知）公众，并准备一个更有能力的劳动力，以解决健康管理和健康保护的基本人类行为为中心的挑战。

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.