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Optimizing Behavioral mHealth Interventions Using Control Systems Engineering: The Control Optimization Trial

Date Wed, Sep 30
Time 2: 00 PM - 3: 00 PM

This free online event is a part of the National Institutes of Health (NIH) Office of Disease Prevention's "Methods: Mind the Gap" webinar series. This month's webinar features Daniel E. Rivera, Ph.D., Professor of Chemical Engineering in the School for Engineering of Matter, Transport and Energy (SEMTE) at Arizona State University (ASU). Dr. Rivera will discuss "Optimizing Behavioral mHealth Interventions Using Control Systems Engineering: The Control Optimization Trial".

This presentation will build on aspects of the Mind the Gap webinar presented June 4, 2019, by Eric B. Hekler—“Using Control Systems Engineering To Optimize Adaptive Mobile Health Interventions.” Control systems engineering is a broadly applicable field that considers how to adjust system variables over time to improve targeted outcomes. It is responsible for diverse consumer products such as cruise control, the home thermostat, and the artificial pancreas. It is receiving increasing attention in mobile health (mHealth) as a means to design and optimize behavioral interventions for physical activity, smoking cessation, and obesity.  The talk will establish the relevance of control engineering to mHealth using two interventions currently under development—Just Walk, an intervention to promote walking in sedentary adults, and Healthy Mom Zone, an intervention for managing gestational weight gain in overweight/obese pregnant women. Both interventions are predicated on a novel experimental design known as the Control Optimization Trial (COT), which takes advantage of a priori information available to the user to facilitate modeling (accomplished via system identification) and integrates it with controller design. Dr. Daniel Rivera will discuss his experience in advancing these concepts within a Team Science environment as well as the contrast between control systems engineering and machine learning approaches, such as reinforcement learning.

About Daniel E. Rivera: Daniel E. Rivera, Ph.D., is a Professor of Chemical Engineering in the School for Engineering of Matter, Transport and Energy (SEMTE) at Arizona State University (ASU) in Tempe, Arizona. He received a B.S. degree in chemical engineering from University of Rochester, an M.S. degree in chemical engineering from University of Wisconsin-Madison, and a Ph.D. in chemical engineering from California Institute of Technology in Pasadena, California. Prior to joining ASU, he was a member of the Control Systems Section of Shell Development Company in Houston, Texas. His research interests span the topics of dynamic modeling using system identification, robust process control, and applications of control engineering to problems in supply chain management and behavioral medicine. In 2007, he received a Mentored Quantitative Research Career Development Award (K25) from the National Institute on Drug Abuse to examine how dynamical systems and control engineering approaches can be used to optimize interventions for the prevention and treatment of drug abuse. He was named a Distinguished Member of the Institute of Electrical and Electronics Engineers (IEEE) Control Systems Society in 2019 and is the 2020 recipient of the David Himmelblau Award for Innovations in Computer-Based Chemical Engineering Education from the American Institute of Chemical Engineers (AIChE).

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