Methods: Mind the Gap

Webinar Series

Using Micro-Randomized Trials To Optimize the Design of Mobile Health Interventions

July 9, 2019
Dr. Klasnja
Predrag Klasnja, Ph.D.

University of Michigan

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About the Webinar

Mobile Health (mHealth) interventions often include theory-based components, such as goal-setting, self-monitoring, planning, etc. To implement these components in a concrete technology, such as an app, researchers and designers must make many decisions about how exactly the intervention components should work (for example, the timing of delivery of each component, the specifics of user interaction)—decisions that go beyond the guidance provided by the theories on which the components are based.

In this presentation, Dr. Klasnja describes how micro-randomized trials can be used to make data-driven decisions about how exactly individual components of mHealth interventions should work to optimize their effectiveness. He argues that a key value of micro-randomized trials during intervention development is their ability to generate data for informing decisions about the many specifics—from the design of the interface to the adaptation algorithms—that must be determined to implement an mHealth intervention. Data from micro-randomized trials enable such decisions to be made in ways that maximize intervention effectiveness while minimizing user burden.

About Predrag Klasnja

Dr. Predrag "Pedja" Klasnja is an Assistant Professor in the School of Information at the University of Michigan and a Scientific Investigator at the Kaiser Permanente Washington Health Research Institute. He works at the intersection of human-computer interaction and behavioral science, and he studies how mobile technologies can help individuals make and sustain lifestyle changes needed to improve their health. He is particularly interested in the design and evaluation of just-in-time adaptive interventions, technologies that continuously adapt their functioning to provide optimal support to individuals as their needs and circumstances change.