ABSTRACT Type 1 diabetes (T1D) affects ~1 million American adults and increases the risk of mortality attributable to cardiovascular disease by 800%. Current evidence-based T1D self-management interventions target glycemic control but ignore other modifiable health concerns prevalent in T1D such as hypertension and obesity. Exercise interventions could provide a novel solution if they could innovatively address the diabetes management and psychosocial challenges around exercise posed by T1D. Continuous glucose monitoring (CGM) allows patients and providers to comprehensively track the short- and long-term outcomes of exercise. Evidence-based interventions to translate CGM technology into sustainable adherence to exercise-related behaviors are lacking. Our human-delivered pilot intervention provided previously sedentary adults with T1D access to exercise videos and monthly client-centered discussions of their CGM and exercise data with an exercise coach. Participants said these improved exercise management behavioral skills and motivation, but only transiently. They stated a need for more frequent and sustained contact, requiring automated mobile tools that this proposal will develop. These tools include just-in-time adaptive text messages to overcome exercise barriers at times of vulnerability, weekly personalized reviews of short-term exercise safety hazards with tips to avoid them, and monthly personalized evaluation of long-term impact of exercise on blood glucose levels via Bayesian modeling. The program represents stage 1 of the NIH intervention development model: intervention generation, refinement, modification, adaptation. These steps will be accomplished by a feasibility study evaluating user satisfaction and mathematical robustness of an alpha version, using these results to modify the alpha version into a beta version, and then testing the beta version in a nonrandomized crossover clinical trial. Lastly, the databank of biobehavioral metrics generated by this trial (exercise, CGM, mood and sleep diaries for ~ 7,000 person-days) will be subjected to dimensionality reduction to identify biobehavioral subtypes of baseline and early intervention data. We will test whether these subtypes help predict longer-term intervention response and/or flag specific biobehavioral feature combinations that drive intervention responsiveness. These findings will lay a foundation for Dr. Ash’s future work developing precision medicine approaches. Alongside this research Dr. Ash will complete training in the domains of 1) diabetes management and technology; 2) mobile health (mHealth) intervention development; and 3) dimensionality reduction analytics. The training plan includes a strategic combination of mentor-led trainings, coursework, grant writing, and attendance at relevant conferences and workshops. Dr. Ash has assembled a mentoring team in T1D self-management and technology, multiple health behavior change intervention development, mHealth developmen...