The pJITAI Toolbox: A No-Code Platform for Creating Personalized Adaptive mHealth Interventions using Reinforcement Learning

The pJITAI Toolbox: A No-Code Platform for Creating Personalized Adaptive mHealth Interventions using Reinforcement Learning

We propose to build and release the personalizing JITAI (pJITAI) Toolbox, a no-code webbased system to allow mHealth researchers to create, configure, and deploy RL algorithms for mHealth interventions. The pJITAI Toolbox features a web-based graphical user interface that guides users (health scientists) through a series of steps to design and configure the personalizing component of their mHealth intervention. In each step, the user makes a small number of decisions regarding a subset of factors that are required to specify for configuring the RL for their application. Once the configuration is complete, the Toolbox will generate a hosted, customized web service to run the specified RL algorithm, and documentation for how to integrate the RL into a standard (non-personalized) JITAI.

Principal Investigator

Mark Newman, PhD
Professor of Information, School of Information
Professor of Electrical Engineering and Computer Science, College of Engineering
University of Michigan

Key Collaborators

Funding Source

Focus Area