Obesity is a growing problem linked with diabetes, cardiovascular disease, and some cancers. In the United States, 70 percent of American adults are overweight or obese, and related health care costs are estimated to be $147 billion annually. Past studies have shown that programs combining exercise and improved diet can lead to weight loss, and thus result in significant reduction in associated chronic illnesses. The challenge is ensuring continued participation in these labor-intensive and often expensive programs. There is a growing belief that the data and interactive possibilities of increasingly ubiquitous digital, mobile, and wireless technologies have the potential to dramatically reduce costs and increase participation in these programs. However, significant knowledge gaps remain with respect to how the data generated by these devices can be used to model and predict behavior in a way that enables the design of effective, personalized, dynamically updated weight-loss strategies. If successful, this EArly-Grant for Exploratory Research (EAGER) project will address these gaps, resulting in foundational mathematical models and computer algorithms for weight loss treatment personalization, utilizing data from digital, mobile, and wireless technologies as inputs. Such foundational tools have the potential to be used in the future to engineer innovative approaches to lower costs and increase efficacy of weight loss interventions, in order to reduce obesity.
This project will lead to development of novel approaches for quantitative modeling of behavioral-change for weight loss through physical activity and diet, where the model is then used for personalized decision-making and treatments. Existing behavioral-change models focus on retrospective understanding and aggregate-level analysis for the purpose of improving treatments at an aggregate-level; however, new modeling approaches like the ones that will be developed in this project are needed for personalized decision-making. The approach of this project will be to convert qualitative models of behavioral-change in health care, such as social cognitive theory, into a mathematical modeling framework. These modeling frameworks will be chosen in a manner that will enable the quantitative validation, comparison, analysis, optimization, and automated decision-making of the resulting models of behavioral-change. Among the anticipated contributions of this project are: quantitative comparison of different qualitative models of behavioral-change, identification of a "best" model of behavioral-change for weight loss, and new stochastic programming frameworks for novel forms of uncertainty found in new medical technologies and in models with "irrational" behavior. Taken in combination, these contributions will form a framework for quantitative modeling of behavioral-change that can be used for design of personalized health care treatments.
