Personalized Digital Health Information to Substantiate Human-Delivered Exercise Support for Adults With Type 1 Diabetes.

Abstract

Pilot-test personalized digital health information to substantiate human-delivered exercise support for adults with type 1 diabetes (T1D). Single-group, 2-week baseline observation, then 10-week intervention with follow-up observation. Community-based sample participating remotely with physician oversight. Volunteers aged 18 to 65 years with T1D screened for medical readiness for exercise intervention offerings. N = 20 enrolled, and N = 17 completed all outcomes with 88% to 91% biosensor adherence. Feedback on personalized data from continuous glucose monitoring (CGM), its intersection with other ecological data sets (exercise, mood, and sleep), and other informational and motivational elements (exercise videos, text-based exercise coach, and self-monitoring diary). Feasibility (use metrics and assessment completion), safety (mild and severe hypoglycemia, and diabetic ketoacidosis), acceptability (system usability scale, single items, and interview themes), and standard clinical and psychosocial assessments. Participants increased exercise from a median of 0 (Interquartile range, 0-21) to 64 (20-129) minutes per week ( P = 0.001, d = 0.71) with no severe hypoglycemia or ketoacidosis. Body mass index increased (29.5 ± 5.1 to 29.8 ± 5.4 kg/m 2 , P = 0.02, 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 because of improved exercise management behavioral skills, although derived motivation was transient. The intervention was feasible, safe, and acceptable. However, there is a need for more intensive, sustained support. Future interventions should perform analytics upon the digital health information and molecular biomarkers (eg, genomics) to make exercise support tools that are more personalized, automated, and intensive than our present offerings.