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<p>Objective: Evaluate feasibility, safety, acceptability, and estimate efficacy of an exercise promotion intervention for adults with type 1 diabetes (T1D) and sub-optimal baseline exercise patterns.Design: Single-group, 2-week baseline observation then 10-week intervention with follow-up observation.Setting: Community-based sample participating remotely with physician oversightParticipants: Volunteers 18-65 years old with T1D screened for medical readiness for exercise intervention offerings. N=20 enrolled, N=17 completing all outcomes with 88%-91% biosensor adherence. Intervention: Feedback on big data from continuous glucose monitoring (CGM), its intersection with other ecological datasets (exercise, mood, sleep), and other informational and motivational elements (exercise videos, text-based exercise coach, self-monitoring diary). Main Outcome Measures: Feasibility (use metrics, assessment completion), safety (mild and severe hypoglycemia, diabetic ketoacidosis), acceptability (system usability scale, single items, interview themes), standard clinical and psychosocial assessments.Results: Participants increased exercise from a median of 0 (IQR 0, 21) to 64 (20, 129) minutes per week (p=.004, d=0.71) with no severe hypoglycemia or ketoacidosis. Body mass index increased (29.5±5.1 to 29.8±5.4 kg/m2, p= .02, d= 0.57). Highest satisfaction ratings were for CGM use (89%) and data on exercise and its intersection with CGM and sleep (94%). Satisfaction was primarily due to improved exercise management behavioral skills, although derived motivation was transient.Conclusions: The use of personalized big data from biosensors to substantiate human-delivered, client-centered exercise support for T1D is feasible and acceptable for interventions involving big data collection. However, there is a need for more intensive, sustained support. The big data could support informatics-based, automated motivational tools.</p>