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的mHealth分析和行为研究(MHABIT)创建一个活实验室，为新的分析模型构建可推广的基础设施，并创建一个行为实验平台(BEP)，以了解通过移动和传感器技术推动人类健康和健康行为和生活方式变化的驱动因素。使用跨学科的方法，该项目将加强对人类行为的理解，并在社区中与智能技术进行互动。研究人员将利用与国内和国际合作伙伴的试验台，向开发来自计量经济学、机器学习、行为经济学和随机实地试验的新分析和实验方法发展知识。该项目将为可扩展的原型技术平台做出贡献，并带来新的解决方案，以改善用户的健康和健康，并提高医疗保健效率。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表化版本相结合，以提高社区的生活质量、健康和福祉以及可持续发展。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。这个项目的洞察力将对通过持续使用新兴技术来设计智能社区提出政策建议。此外，更广泛的影响包括向公众传播研究、代表性不足的群体以及技术的广泛部署。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，以检查哪些因素推动用户参与mHealth，他们与临床环境内外的医生互动，以及他们对个人生活方式做出了哪些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情况，调查人员计划在mHABIT生活实验室实施BEP的试点部署，通过与美国和海外的医疗保健提供者合作，设计和实施新的移动使能干预措施，并从因果角度评估mHealth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.