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.
Abstract
Beibei Li
Beibei’s research interests lie at the intersection between social and technical aspects of information technology. She is especially interested in the interaction between human decisions and recent technological disruptions in the markets. Specifically, the ubiquitous adoption and usage of mobile, web and sensor technologies today have completely changed the way individuals behave and make decisions. These technologies have led to the pervasive digitization of individual behavior across digital and physical environments at a very fine-grained level. This information provides us with a new lens through which we can better monitor, understand, and optimize the individual decision making. By looking into these digital footprints of individuals and their interactions with technologies, Beibei is interested in designing effective strategies for technology platforms and policy makers to improve technology design and economic welfare. To reach these goals, Beibei applies inter-disciplinary approaches combining AI and machine learning, economics and econometrics, and randomized experiments.
Beibei is the recent recipient of the INFORMS ISS Sandy Slaughter Early Career Award in 2019 and the Anna Loomis McCandless Chair Professorship at Carnegie Mellon University in 2015. Beibei's research has been published in Management Science, Marketing Science, Information Systems Research, Management Information Systems Quarterly, and several top IS, Economics, Marketing and CS conferences. She is the winner of the Best Paper Award at the 20th International World Wide Web Conference (WWW 2011), two Best Paper Awards and one Best Impact Paper Award at the International Conference on Information Systems (ICIS 2012, 2019, 2022), over ten Best Paper Awards at major journals and workshops in IT & Management (ISR, WITS, WISE, CIST). She has won three NSF Awards with over $3M total funding, seven Faculty Research Awards from NBER Amazon, Google, Facebook, Adobe and Marketing Science Institute (MSI), and a WCAI Research Award from Wharton Customer Analytics Initiative. Beibei is the winner of the Junior Marketing Researcher Award at the Big Data Marketing Conference 2015. She is also the winner of the INFORMS ISS Nunamaker-Chen Dissertation Award , the ACM SIGMIS Best Doctoral Dissertation Award and the Herman E. Krooss Doctoral Dissertation Award in 2012-2013.
Prior to getting her PhD, Beibei held a BS and a MS in Computer Science. She also minored in Fashion Design.
Performance Period: 09/01/2016 - 08/31/2019
Institution: Carnegie-Mellon University
Award Number: 1637007
Core Areas:
Health and Wellbeing