In Silico Trials of an Open-Source Android-Based Artificial Pancreas: A New Paradigm to Test Safety and Efficacy of Do-It-Yourself Systems

Toffanin, Chiara; Kozak, Milos; Šumnı́k, Zdenĕk; Cobelli, Claudio; Petruželková, Lenka

doi:10.1089/dia.2019.0375

Cited by 41 publications

(48 citation statements)

References 34 publications

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“…24 This implementation of oref1 in AndroidAPS with unannounced meals and SMB has been tested in other studies, primarily in realworld usage with meal announcements, and also studied in silico. [25][26][27] AndroidAPS code (https://github.com/MilosKozak/ AndroidAPS) and documentation (https://androidaps. readthedocs.io/en/latest/EN/) are available online.…”

Section: Androidapsmentioning

confidence: 99%

Full closed loop open‐source algorithm performance comparison in pigs with diabetes

Lal

Maikawa

Lewis³

et al. 2021

Clinical & Translational Med

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Understanding how automated insulin delivery (AID) algorithm features impact glucose control under full closed loop delivery represents a critical step toward reducing patient burden by eliminating the need for carbohydrate entries at mealtimes. Here, we use a pig model of diabetes to compare AndroidAPS and Loop open‐source AID systems without meal announcements. Overall time‐in‐range (70–180 mg/dl) for AndroidAPS was 58% ± 5%, while time‐in‐range for Loop was 35% ± 5%. The effect of the algorithms on time‐in‐range differed between meals and overnight. During the overnight monitoring period, pigs had an average time‐in‐range of 90% ± 7% when on AndroidAPS compared to 22% ± 8% on Loop. Time‐in‐hypoglycemia also differed significantly during the lunch meal, whereby pigs running AndroidAPS spent an average of 1.4% (+0.4/−0.8)% in hypoglycemia compared to 10% (+3/−6)% for those using Loop. As algorithm design for closed loop systems continues to develop, the strategies employed in the OpenAPS algorithm (known as oref1) as implemented in AndroidAPS for unannounced meals may result in a better overall control for full closed loop systems.

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Section: Androidapsmentioning

confidence: 99%

Full closed loop open‐source algorithm performance comparison in pigs with diabetes

Lal

Maikawa

Lewis³

et al. 2021

Clinical & Translational Med

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“…The study affirmed the safety, feasibility, and efficacy of AndroidAPS over SmartGuard Ò technology during and after prolonged physical activity [36]. In 2019, an in silico study using UVA/Padova Type 1 Diabetes Simulator proved that the AndroidAPS algorithm works safely and effectively and demonstrates great potential for use in home settings [37]. A most recent study has explained how a DIYAPS supported a woman with T1D to achieve two subsequent pregnancies [38].…”

Section: Current Literature On Diyapsmentioning

confidence: 73%

The Do-It-Yourself Artificial Pancreas: A Comprehensive Review

Kesavadev

Srinivasan

Saboo³

et al. 2020

Diabetes Ther

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Diabetes technology (DT) has accomplished tremendous progress in the past decades, aiming to convert these technologies as viable treatment options for the benefit of patients with diabetes (PWD). Despite the advances, PWD face multiple challenges with the efficient management of type 1 diabetes. Most of the promising and innovative technological developments are not accessible to a larger proportion of PWD. The slow pace of development and commercialization, overpricing, and lack of peer support are few such factors leading to inequitable access to the innovations in DT. Highly motivated and tech-savvy members of the diabetes community have therefore come up with the #WeAreNotWaiting movement and started developing their own do-it-yourself artificial pancreas systems (DIYAPS) integrating continuous glucose monitoring (CGM), insulin pumps, and smartphone technology to run openly shared algorithms to achieve appreciable glycemic control and quality of life (QoL). These systems use tailor-made interventions to achieve automated insulin delivery (AID) and are not commercialized or regulated. Online social network megatrends such as GitHub, CGM in the Cloud, and Twitter have been providing platforms to share these open source technologies and user experiences. Observational studies, anecdotal evidence, and realworld patient stories revealed significant improvements in time in range (TIR), time in hypoglycemia (TIHypo), HbA1c levels, and QoL after the initiation of DIYAPS. But this unregulated do-it-yourself (DIY) approach is perceived with great circumspection by healthcare professionals (HCP), regulatory bodies, and device manufacturers, making users the ultimate riskbearers. The use of the regularized CGM and insulin pump with unauthorized algorithms makes them off-label and has been a matter of great concern. Besides these, lack of safety data, funding or insurance coverage, ethical, and legal issues are roadblocks to the unanimous acceptance of these systems among patients with type 1 diabetes (T1D). A multi-agency approach is necessary to evaluate the risks, and to delineate the incumbency and liability of Digital Features To view digital features for this article go to

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“…Insulin on board and glucose levels at mealtime were included as adjustments. Insulin on board was calculated as described in Toffanin et al 5 with an insulin peak time of 89 min 6 and a duration of action of 5 h. For the latter, the infused insulin dose per minute was considered a discrete bolus. In the results, we report the posterior mean and the 95% credible interval (CrI) based on the 2.5% and 97.5% quantile of the posterior distribution.…”

Section: Methodsmentioning

confidence: 99%

Effect of nutrition on postprandial glucose control in hospitalized patients with type 2 diabetes receiving fully automated closed‐loop insulin therapy

Banholzer

Herzig

Piazza

et al. 2020

Diabetes Obesity Metabolism

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Fully automated closed‐loop insulin delivery may offer a novel way to manage diabetes in hospital. However, postprandial glycaemic control remains challenging. We aimed to assess the effect of nutritional intake on postprandial glucose control in hospitalized patients with type 2 diabetes receiving fully closed‐loop insulin therapy. The effects of different meal types and macronutrient composition on sensor glucose time‐in‐target (TIT, 3.9‐10.0 mmol/L) and mean sensor glucose were assessed with hierarchical linear models using a Bayesian estimation approach. TIT was lower and the mean sensor glucose slightly higher, after breakfast compared with lunch and dinner, whereas the insulin dose was higher. Across meals, when carbohydrates were replaced by fat, or to a lesser extent by protein, postprandial glucose control improved. For breakfast, a 3.9% improvement in TIT was observed when 10% of the energy from carbohydrates was replaced by fat. Improvements were slightly lower during lunch and dinner (3.2% and 3.4%) or when carbohydrates were replaced by protein (2.2 and 2.7%, respectively). We suggest that reducing carbohydrate at the expense of fat or protein, could further improve glucose control during fully closed‐loop insulin therapy in hospital.

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In Silico Trials of an Open-Source Android-Based Artificial Pancreas: A New Paradigm to Test Safety and Efficacy of Do-It-Yourself Systems

Cited by 41 publications

References 34 publications

Full closed loop open‐source algorithm performance comparison in pigs with diabetes

Full closed loop open‐source algorithm performance comparison in pigs with diabetes

The Do-It-Yourself Artificial Pancreas: A Comprehensive Review

Effect of nutrition on postprandial glucose control in hospitalized patients with type 2 diabetes receiving fully automated closed‐loop insulin therapy

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