AS01 Closed-loop System and Algorithm

505 - EFFICACY OF REAL-TIME MEAL DETECTION AND REMINDERS ON APPLE WATCH

Session Type
E-POSTER DISCUSSION
Session Name
E-POSTER DISCUSSION 01

Abstract

Background and Aims

Klue uses an Apple watch to detect hand motions indicative of eating or drinking. We hypothesized that real-time meal reminders delivered by Klue could decrease missed meal boluses and hemoglobin A1c (HbA1c) in adolescents and young adults with diabetes and 4 missed meal boluses in the previous 2 weeks.

Methods

This was a randomized, crossover, unmasked clinical study. Participants using continuous glucose monitor (CGM) with an insulin pump were randomized to either Klue for the first 6 weeks or standard care. There were 3 definitions for a meal occurrence: (1) Klue detection, (2) CGM rate of change of 2mg/dL/min for 20 minutes after cubic spline smoothing and (3) boluses for carbohydrates. Boluses were classified as premeal if ≤30 minutes prior to a CGM event, late if after CGM event but within 2 hours, and missed if no bolus within 2 hours.

Results

17 participants (mean age 17.7±4.6 years) were enrolled and 8 were randomized to start with Klue. The patients on Klue had a HbA1c decrease of 0.5% compared to the usual care arm (p=0.004). There were significantly fewer missed meal boluses (p < 0.00001) with Klue utilization. On average, Klue detected a missed meal bolus 18 minutes prior to a significant CGM rate of change and there was 1 Klue false-positive every 2-3 days.

Conclusions

Automated meal reminders improved HbA1c and reduced the number of late meal boluses. In addition to improving compliance, this technology has the potential to provide more rapid meal announcements to a closed-loop insulin delivery system.

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