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.
