Effect of automated bio-behavioral feedback on the control of Type 1 Diabetes

October 20, 2017 by School of Medicine Webmaster

OBJECTIVE To test the effect of an automated system providing real-time estimates of HbA1c, glucose variability, and risk for hypoglycemia.

RESEARCH DESIGN AND METHODS For 1 year, 120 adults with type 1 diabetes (69 female/51 male, age = 39.1 [14.3] years, duration of diabetes 20.3 [12.9] years, HbA1c = 8.0 [1.5]), performed self-monitoring of blood glucose (SMBG) and received feedback at three increasingly complex levels, each continuing for 3 months: level 1—routine SMBG; level 2—adding estimated HbA1c, hypoglycemia risk, and glucose variability; and level 3—adding estimates of symptoms potentially related to hypoglycemia. The subjects were randomized to feedback sequences of either levels 1-2-3 or levels 2-3-1. HbA1c, symptomatic hypoglycemia, and blood glucose awareness were evaluated at baseline and at the end of each level.

RESULTS For all subjects, HbA1c was reduced from 8.0 to 7.6 from baseline to the end of study (P = 0.001). This effect was confined to subjects with baseline HbA1c >8.0 (from 9.3 to 8.5, P < 0.001). Incidence of symptomatic moderate/severe hypoglycemia was reduced from 5.72 to 3.74 episodes/person/month (P = 0.019), more prominently for subjects with a history of severe hypoglycemia (from 7.20 to 4.00 episodes, P = 0.008) and for those who were hypoglycemia unaware (from 6.44 to 3.71 episodes, P = 0.045). The subjects’ ratings of the feedback were positive, with up to 89% approval of the provided features.

CONCLUSIONS Feedback of SMBG data and summary SMBG-based measures resulted in improvement in average glycemic control and reduction in moderate/severe hypoglycemia. These effects were most prominent in subjects who were at highest risk at the baseline.