Impact of macronutrient content of meals on postprandial glucose control in the context of closed‐loop insulin delivery: A randomized cross‐over study

2018 ◽  
Vol 20 (11) ◽  
pp. 2695-2699 ◽  
Author(s):  
Véronique Gingras ◽  
Lisa Bonato ◽  
Virginie Messier ◽  
Amélie Roy‐Fleming ◽  
Mohamed R. Smaoui ◽  
...  
Keyword(s):  
Glucose Control ◽  
Closed Loop ◽  
Insulin Delivery ◽  
Postprandial Glucose ◽  
Macronutrient Content

scholarly journals Women’s Experiences of Day-and-Night Closed-Loop Insulin Delivery During Type 1 Diabetes Pregnancy

2018 ◽  
Vol 12 (6) ◽  
pp. 1125-1131 ◽  
Author(s):  
Conor Farrington ◽  
Zoe Stewart ◽  
Roman Hovorka ◽  
Helen Murphy
Keyword(s):  
Type 1 Diabetes ◽  
Glucose Control ◽  
Closed Loop ◽  
Insulin Delivery ◽  
Peace Of Mind ◽  
Women’S Experiences ◽  
Closed Loop Systems ◽  
Women's Experiences

Aims: Closed-loop insulin delivery has the potential to improve day-to-day glucose control in type 1 diabetes pregnancy. However, the psychosocial impact of day-and-night usage of automated closed-loop systems during pregnancy is unknown. Our aim was to explore women’s experiences and relationships between technology experience and levels of trust in closed-loop therapy. Methods: We recruited 16 pregnant women with type 1 diabetes to a randomized crossover trial of sensor-augmented pump therapy compared to automated closed-loop therapy. We conducted semistructured qualitative interviews at baseline and follow-up. Findings from follow-up interviews are reported here. Results: Women described benefits and burdens of closed-loop systems during pregnancy. Feelings of improved glucose control, excitement and peace of mind were counterbalanced by concerns about technical glitches, CGM inaccuracy, and the burden of maintenance requirements. Women expressed varied but mostly high levels of trust in closed-loop therapy. Conclusions: Women displayed complex psychosocial responses to day-and-night closed-loop therapy in pregnancy. Clinicians should consider closed-loop therapy not just in terms of its potential impact on biomedical outcomes but also in terms of its impact on users’ lives.

scholarly journals Home use of closed-loop insulin delivery for overnight glucose control in adults with type 1 diabetes: a 4-week, multicentre, randomised crossover study

2014 ◽  
Vol 2 (9) ◽  
pp. 701-709 ◽  
Author(s):  
Hood Thabit ◽  
Alexandra Lubina-Solomon ◽  
Marietta Stadler ◽  
Lalantha Leelarathna ◽  
Emma Walkinshaw ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Crossover Study ◽  
Glucose Control ◽  
Closed Loop ◽  
Insulin Delivery

scholarly journals A Deep Learning Framework for Automatic Meal Detection and Estimation in Artificial Pancreas Systems

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 466
Author(s):  
John Daniels ◽  
Pau Herrero ◽  
Pantelis Georgiou
Keyword(s):  
Deep Learning ◽  
Quantile Regression ◽  
Glucose Control ◽  
Closed Loop ◽  
Artificial Pancreas ◽  
Postprandial Glucose ◽  
User Engagement ◽  
Frequent User ◽  
Learning Framework ◽  
Closed Loop Systems

Current artificial pancreas (AP) systems are hybrid closed-loop systems that require manual meal announcements to manage postprandial glucose control effectively. This poses a cognitive burden and challenge to users with T1D since this relies on frequent user engagement to maintain tight glucose control. In order to move towards fully automated closed-loop glucose control, we propose an algorithm based on a deep learning framework that performs multitask quantile regression, for both meal detection and carbohydrate estimation. Our proposed method is evaluated in silico on 10 adult subjects from the UVa/Padova simulator with a Bio-inspired Artificial Pancreas (BiAP) control algorithm over a 2 month period. Three different configurations of the AP are evaluated -BiAP without meal announcement (BiAP-NMA), BiAP with meal announcement (BiAP-MA), and BiAP with meal detection (BiAP-MD). We present results showing an improvement of BiAP-MD over BiAP-NMA, demonstrating 144.5 ± 6.8 mg/dL mean blood glucose level (−4.4 mg/dL, p< 0.01) and 77.8 ± 6.3% mean time between 70 and 180 mg/dL (+3.9%, p< 0.001). This improvement in control is realised without a significant increase in mean in hypoglycaemia (+0.1%, p= 0.4). In terms of detection of meals and snacks, the proposed method on average achieves 93% precision and 76% recall with a detection delay time of 38 ± 15 min (92% precision, 92% recall, and 37 min detection time for meals only). Furthermore, BiAP-MD handles hypoglycaemia better than BiAP-MA based on CVGA assessment with fewer control errors (10% vs. 20%). This study suggests that multitask quantile regression can improve the capability of AP systems for postprandial glucose control without increasing hypoglycaemia.

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