Faster insulin action is associated with improved glycaemic outcomes during closed-loop insulin delivery and sensor-augmented pump therapy in adults with type 1 diabetes.

We aimed to evaluate the relationship between insulin pharmacodynamics and glycaemic outcomes during closed-loop insulin delivery and sensor-augmented pump therapy. We retrospectively analysed data from a multicentre randomized control trial involving 32 adults with type 1 diabetes receiving day-and-night closed-loop insulin delivery and sensor-augmented pump therapy over 12 weeks. We estimated time-to-peak insulin action (t max,IA ) and insulin sensitivity ( S I ) during both interventions, and correlated these with demographic factors and glycaemic outcomes. During both interventions, t max,IA was positively correlated with pre- and post-intervention HbA1c (r = 0.50-0.52, P < .01) and mean glucose (r = 0.45-0.62, P < .05), and inversely correlated with time sensor glucose, which was in target range 3.9 to 10 mmol/L (r = -0.64 to -0.47, P < .05). Increased body mass index was associated with higher t max,I and lower S I (both P < .05). During closed-loop insulin delivery, t max,IA was positively correlated with glucose variability ( P < .05). Faster insulin action is associated with improved glycaemic control during closed-loop insulin delivery and sensor-augmented pump therapy.

Behavioral Patterns and Associations with Glucose Control During 12-Week Randomized Free-Living Clinical Trial of Day and Night Hybrid Closed-Loop Insulin Delivery in Adults with Type 1 Diabetes.

OBJECTIVES: We evaluated patterns of meal intake, insulin bolus delivery, and fingerstick glucose measurements during hybrid closed-loop and sensor-augmented pump (SAP) therapy, including associations with glucose control. METHODS: Data were retrospectively analyzed from pump-treated adults with type 1 diabetes who underwent, in random order, 12 weeks free-living closed-loop (n = 32) and 12 weeks SAP (n = 33) periods. We quantified daily patterns of main meals, snacks, prandial insulin boluses, correction boluses, and fingerstick glucose measurements by analyzing data recorded on the study glucometer and on study insulin pump. RESULTS: We analyzed 1942 closed-loop days and 2530 SAP days. The total number of insulin boluses was reduced during closed-loop versus SAP periods by mean 1.0 per day (95% confidence interval 0.6-1.4, P < 0.001) mainly because of a reduced number of correction boluses by mean 0.7 per day (0.4-1.0, P < 0.001). Other behavioral patterns were unchanged. The carbohydrate content of snacks but not the number of snacks was positively correlated with (1) glycemic variability as measured by standard deviation of sensor glucose (closed-loop P < 0.05; SAP P < 0.01), (2) mean sensor glucose (P < 0.05), and (3) postintervention HbA1c (P < 0.05). Behavioral patterns explained 47% of between-subject variance in glucose variability during SAP period and 30%-33% of variance of means sensor glucose and postintervention HbA1c. CONCLUSION: Fewer correction boluses are delivered during closed-loop period. The size of snacks appears to worsen glucose control possibly because of carbohydrate-rich content of snacks. Modifiable behavioral patterns may be important determinants of glucose control.

Closing the Loop in Adults, Children and Adolescents With Suboptimally Controlled Type 1 Diabetes Under Free Living Conditions: A Psychosocial Substudy.

OBJECTIVE: The objective was to explore psychosocial experiences of closed loop technology for adults, children, and adolescents with type 1 diabetes and their parents taking part in two multicenter, free-living, randomized crossover home studies. METHODS: Participants using insulin pump therapy were randomized to either 12 weeks of automated closed-loop glucose control, then 12 weeks of sensor augmented insulin pump therapy (open loop), or vice versa. Closed loop was used for 24 hours by adults and overnight only by children and adolescents. Participants completed the Diabetes Technology Questionnaire (DTQ) periodically and shared their views in semistructured interviews. This analysis characterizes the impact of the technology, positive and negative aspects of living with the device, alongside participants' expectations, hopes, and anxieties. RESULTS: Participants were 32 adults, age 38.6 ± 9.6 years, 55% male, and 26 children, mean age 12 years (range 6-18 years), 54% male. DTQ results indicated moderately favorable impact of, and satisfaction with, both open and closed loop interventions, but little evidence of a comparative advantage of either. Key positive themes included perceived improved blood glucose control, improved general well-being, particularly on waking, improved sleep, reduced burden of diabetes, and visibility of data. Key negative themes included having to carry around the equipment and dislike of the pump and second cannula (ie, sensor) inserted. CONCLUSIONS: Overall, participants reported a positive experience of the closed loop technology. Results are consistent with previous research with size of equipment continuing to be a problem. Progress is being made in the usability of the closed-loop system.

Variability of Insulin Requirements Over 12 Weeks of Closed-Loop Insulin Delivery in Adults With Type 1 Diabetes.

OBJECTIVE: To quantify variability of insulin requirements during closed-loop insulin delivery. RESEARCH DESIGN AND METHODS: We retrospectively analyzed overnight, daytime, and total daily insulin amounts delivered during a multicenter closed-loop trial involving 32 adults with type 1 diabetes. Participants applied hybrid day-and-night closed-loop insulin delivery under free-living home conditions over 12 weeks. The coefficient of variation was adopted to measure variability of insulin requirements in individual subjects. RESULTS: Data were analyzed from 1,918 nights, 1,883 daytime periods and 1,564 total days characterized by closed-loop use over 85% of time. Variability of overnight insulin requirements (mean [SD] coefficient of variation 31% [4]) was nearly twice as high as variability of total daily requirements (17% [3], P < 0.001) and was also higher than variability of daytime insulin requirements (22% [4], P < 0.001). CONCLUSIONS: Overnight insulin requirements were significantly more variable than daytime and total daily amounts. This may explain why some people with type 1 diabetes report frustrating variability in morning glycemia.