I am an exercise physiologist leveraging big data analytics and behavioral frameworks to develop and implement personalized self-management interventions for youth and adults with type 1 diabetes (T1D). During my postdoctoral fellowships I tested the feasibility of various approaches, leading me to transition from in-person peer groups to personalized, informatics-based digital methods. For my NIDDK mentored research scientist award (1K01DK129441) I am developing an informatics-based digital application to promote safe exercise in middle-aged adults with T1D. Training foci have included mobile health intervention development, diabetes technology, and machine learning to evaluate interventions, predict intervention response based on time series data, and cluster individuals by their responses. These skills are critical to the automated personalization of interventions. While my award is focused on middle-aged adults with T1D, I have also applied the methods to older adults with heart failure. I am separately developing a similar approach for type 2 diabetes (T2D), funded by VA HSR&D to conduct formative interviews and then VA Office of Connected Care to conduct the CTL-T2D project described in the current proposal’s pilot data. I have since been an MPI on two other VA projects focused on the implementation of consumer-grade wearable devices into clinical workflow for other diseases. I have also worked on evidence reviews and consensus panels for quality evaluation of wearable technology, including the Sports Tech Quality Framework (https://strn.co/news-insights/2023/05/23/Experts-collaborate-to-establish-the-Sports-Technology-Quality-Framework).