EAGER: Collaborative Research: mHABIT - Towards Building a Living Lab for mHealth Analytical and Behavioral Research using Internet of Things

该项目将为使用物联网（mHABIT）的移动健康分析和行为研究创建一个生活实验室，为新的分析模型和行为实验平台（BEP）建立一个通用的基础设施，以通过移动和传感器技术了解人类健康和健康行为以及生活方式改变的驱动因素。采用跨学科的方法，该项目将加强对社区中人类行为和智能技术互动的理解。研究人员将与国内外合作伙伴利用试验台，从计量经济学、机器学习、行为经济学和随机现场实验中汲取知识，开发新的分析和实验方法。该项目将为可扩展的原型技术平台做出贡献，并为改善用户健康和保健以及医疗保健效率提供新的解决方案。总体而言，该项目将把先进的物联网基础设施与物理世界的仪器化版本相结合，以改善生活质量、健康和福祉，以及社区的可持续性。医疗保健提供商可以轻松部署从该项目开发的方法和基础设施，以支持数据收集、分析、解决方案和评估。该项目的见解将通过持续使用新兴技术，为智能社区的设计提供政策建议。此外，更广泛的影响包括向公众、代表性不足的群体传播研究成果，以及广泛部署技术。该项目开发的基础设施将收集和分析大规模和细粒度的用户GPS轨迹数据和RFID跟踪数据，并与EHR数据相关联，以检查驱动用户参与移动健康的因素，他们与临床内外医生的互动，以及他们在个人生活方式中做出的改变，以改善他们的健康结果。为了评估用户健康行为和决策的学习，研究人员计划通过与美国和海外的医疗保健提供者合作，在mHABIT生活实验室实施BEP的试点部署，设计和实施新颖的移动干预措施，并从因果关系的角度评估移动健康技术的有效性。

This project will create a living lab for mHealth Analytical and Behavioral Research using Internet of Things (mHABIT) to build a generalizable infrastructure for new analytical models and a Behavioral Experimentation Platform (BEP) to understand drivers of human health and wellness behavior and lifestyle changes through mobile and sensor technologies. Using an interdisciplinary approach, this project will enhance the understanding of human behavior and interactions with smart technologies in communities. The investigators will leverage test beds with domestic and international partners to advance knowledge towards developing new analytical and experimental methods drawn from econometrics, machine learning, behavioral economics and randomized field experiments. This project will contribute to a scalable prototype technology platform and lead to new solutions for improving user health and wellness, and healthcare efficiency. Overall, this project will integrate advanced Internet-of-Things infrastructures with an instrumented version of the physical world to improve quality of life, health and wellbeing, and sustainability of communities. The methods and infrastructures developed from this project can be easily deployed by healthcare providers to support data collection, analytics, solution and evaluation. The insights from this project will suggest policy implications towards the design of smart community through sustained usage of emerging technology. Moreover, broader impact includes dissemination of research to the public, underrepresented groups, and widespread deployment of the technology. The infrastructure developed from this project will collect and analyze large-scale and fine-grained user GPS trajectory data and RFID tracking data, linked with the EHR data, to examine what factors drive users' engagement with mHealth, their interactions with doctors inside and outside the clinical setting, and what changes they make in their personal lifestyle to improve their health outcomes. To evaluate the learning of user health behavior and decision making, the investigators plan to implement a pilot deployment of the BEP in the mHABIT living lab, by partnering with healthcare providers in the US and overseas, to design and implement novel mobile-enabled interventions and evaluate the effectiveness of mHealth technology from a causal perspective.