An Automated Insulin Delivery System with Automatic Meal Bolus Based on a Hand-Gesturing Algorithm.

IF 5.7 2区 医学 Q1 ENDOCRINOLOGY & METABOLISM Diabetes technology & therapeutics Pub Date : 2024-09-01 Epub Date: 2024-03-12 DOI:10.1089/dia.2023.0529
Anirban Roy, Benyamin Grosman, Andrea Benedetti, Bahman Engheta, Diana Miller, Maya Laron-Hirsh, Yael Cohen, Roseline Ré, Shannon N Edd, Robert Vigersky, Ohad Cohen, Amir Tirosh
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Abstract

Background: Carbohydrate counting (CC) and meal announcements, before eating, introduce a significant burden for individuals managing type 1 diabetes (T1D). An automated insulin delivery system with automatic bolusing that eliminates the need for CC and premeal bolusing (i.e., a hands-free closed-loop [HFCL] system) was assessed in a feasibility trial of adults with T1D. Methods: The system included the MiniMed™ 780G pump and a smartphone-paired smartwatch with the Klue application (Klue, Inc.) that detects eating and drinking gestures. A smartphone algorithm converted gestures into carb amounts that were transmitted to the pump for automatic bolusing. For 5 days, participants (N = 17, 18-75 years of age) used the system at home with meal announcements based on traditional CC, with the Klue application disabled (Home-stay phase). Thereafter, participants moved to a supervised hotel setting, where the Klue application was enabled for 5 days and meals were not announced (Hotel-stay phase). Participants consumed the same eight test meals (six solid and two liquid) of varying caloric and carb size at the same time and day of the week for both phases, and glycemic metrics were compared. Otherwise, there were no other meal restrictions. Results: The overall time in range (70-180 mg/dL) was 83.4% ± 7.0% and 80.6% ± 6.7% for the Home-stay and Hotel-stay, respectively (P = 0.08). The average time at <70 mg/dL was 3.1% and 3.0% (P = 0.9144), respectively, and the average time at >180 mg/dL was 13.5% and 16.3% (P = 0.1046), respectively. Postprandial glycemia following low-carb test meals was similar between the two phases. The system's ability to accommodate high-carb meals was somewhat limited. There were no episodes of severe hypoglycemia or diabetic ketoacidosis. Conclusion: Preliminary findings show that a HFCL system was safe and maintained overall glycemic control, similar to that observed with traditional CC and manual meal bolusing. By eliminating these daily T1D burdens, a HFCL system may improve quality of life for individuals with T1D. ClinicalTrials.gov number: NCT04964128.

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基于手工绘图算法的胰岛素自动给药系统(AID)。
背景:进餐前的碳水化合物计算(CC)和餐前通知给 1 型糖尿病(T1D)患者带来了很大的负担。在一项针对成人 T1D 患者的可行性试验中,我们评估了一种自动胰岛素给药 (AID) 系统,该系统可自动注药,无需 CC 和餐前注药(即免提闭环系统):该系统包括 MiniMed™ 780G 泵和智能手机配对的智能手表,智能手表上的 Klue 应用程序(Klue, Inc.)智能手机算法会将手势转换成碳水化合物的量,并将其传输到泵中进行自动注药。参与者(17 人,18-75 岁)在家中使用了该系统 5 天,系统根据传统的 CC 播报膳食,并禁用了 Klue 应用程序(在家使用阶段)。之后,参与者转移到受监督的酒店环境中,在那里启用 Klue 应用程序 5 天,但不公布餐点(酒店住宿阶段)。在这两个阶段中,参与者在一周的同一时间和同一天食用相同的 8 份测试餐(6 份固体餐和 2 份液体餐),这些餐的热量和碳水化合物含量各不相同,并对血糖指标进行比较。除此之外,没有其他膳食限制:家庭住宿和酒店住宿的总体血糖在范围内(70-180 mg/dL)的时间分别为 83.4±7.0%和 80.6±6.7%(P=0.08)。180毫克/分升时的平均时间分别为13.5%和16.3%(P=0.1046)。两个阶段的低碳水化合物测试餐后血糖值相似。该系统对高碳水化合物餐的适应能力有限。没有发生严重低血糖或糖尿病酮症酸中毒:初步研究结果表明,免提闭环系统是安全的,并能保持总体血糖控制,与传统的 CC 和人工给餐法所观察到的结果相似。通过消除这些 T1D 患者的日常负担,HFCL 系统可提高 T1D 患者的生活质量。
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来源期刊
Diabetes technology & therapeutics
Diabetes technology & therapeutics 医学-内分泌学与代谢
CiteScore
10.60
自引率
14.80%
发文量
145
审稿时长
3-8 weeks
期刊介绍: Diabetes Technology & Therapeutics is the only peer-reviewed journal providing healthcare professionals with information on new devices, drugs, drug delivery systems, and software for managing patients with diabetes. This leading international journal delivers practical information and comprehensive coverage of cutting-edge technologies and therapeutics in the field, and each issue highlights new pharmacological and device developments to optimize patient care.
期刊最新文献
Safety of Options to "Boost" (Enhancing Insulin Infusion Rates) and "Ease-Off" (Reducing Insulin Infusion Rates) in CamAPS FX Hybrid Closed-Loop System: A Real-World Analysis. Clinical Utility of Serum C-peptide Concentration for Hospitalized Patients with Hyperglycemia. An Automated Insulin Delivery System with Automatic Meal Bolus Based on a Hand-Gesturing Algorithm. Noninvasive Real-Time Glucose Monitoring Is in the Near Future. Accuracy of a Real-Time Continuous Glucose Monitor in Pediatric Diabetic Ketoacidosis Admissions.
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