Incorporating an Exercise Detection, Grading, and Hormone Dosing Algorithm Into the Artificial Pancreas Using Accelerometry and Heart Rate

Jacobs, Peter G.; Resalat, Navid; Youssef, Joseph El; Reddy, Ravi; Branigan, Deborah; Preiser, Nicholas; Condon, John R.; Castle, Jessica R.

doi:10.1177/1932296815609371

Cited by 98 publications

(83 citation statements)

References 29 publications

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“…Few patients achieve this even with intensive insulin treatment [1]. New approaches with automatic glucose controlled insulin and glucagon delivery, known as a dual-hormone artificial pancreas (AP), may offer a solution to improve glycemic control [2][3][4][5][6]. To design and tune control algorithms for AP devices prior to in vivo tests, a validated simulation model capturing the dynamics between glucose, insulin and glucagon is needed to perform helpful in silico experiments [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Cross-Validation of a Glucose-Insulin-Glucagon Pharmacodynamics Model for Simulation Using Data From Patients With Type 1 Diabetes

Wendt

Ranjan

Møller

et al. 2017

J Diabetes Sci Technol

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Background: Currently, no consensus exists on a model describing endogenous glucose production (EGP) as a function of glucagon concentrations. Reliable simulations to determine the glucagon dose preventing or treating hypoglycemia or to tune a dual-hormone artificial pancreas control algorithm need a validated glucoregulatory model including the effect of glucagon. Methods: Eight type 1 diabetes (T1D) patients each received a subcutaneous (SC) bolus of insulin on four study days to induce mild hypoglycemia followed by a SC bolus of saline or 100, 200, or 300 µg of glucagon. Blood samples were analyzed for concentrations of glucagon, insulin, and glucose. We fitted pharmacokinetic (PK) models to insulin and glucagon data using maximum likelihood and maximum a posteriori estimation methods. Similarly, we fitted a pharmacodynamic (PD) model to glucose data. The PD model included multiplicative effects of insulin and glucagon on EGP. Bias and precision of PD model test fits were assessed by mean predictive error (MPE) and mean absolute predictive error (MAPE). Results: Assuming constant variables in a subject across nonoutlier visits and using thresholds of ±15% MPE and 20% MAPE, we accepted at least one and at most three PD model test fits in each of the seven subjects. Thus, we successfully validated the PD model by leave-one-out cross-validation in seven out of eight T1D patients. Conclusions: The PD model accurately simulates glucose excursions based on plasma insulin and glucagon concentrations. The reported PK/PD model including equations and fitted parameters allows for in silico experiments that may help improve diabetes treatment involving glucagon for prevention of hypoglycemia.

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Section: Introductionmentioning

confidence: 99%

Cross-Validation of a Glucose-Insulin-Glucagon Pharmacodynamics Model for Simulation Using Data From Patients With Type 1 Diabetes

Wendt

Ranjan

Møller

et al. 2017

J Diabetes Sci Technol

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“…Several approaches have been investigated for the management of exercise during the operation of the single-hormone artificial pancreas [18][19][20][21][22]. A fully reactive system that is driven by glucose sensor data alone fails to prevent exerciseinduced hypoglycaemia, particularly because the delay in insulin absorption reduces the effectiveness of its suspension after the start of exercise [21].…”

Section: Introductionmentioning

confidence: 99%

“…A fully reactive system that is driven by glucose sensor data alone fails to prevent exerciseinduced hypoglycaemia, particularly because the delay in insulin absorption reduces the effectiveness of its suspension after the start of exercise [21]. The addition of a heart rate or activity sensor to the single-hormone artificial pancreas might reduce the risk of hypoglycaemia, although at the expense of an increased burden for the patient (via wearing multiple sensors) [22]. The use of glucagon in the dual-hormone artificial pancreas might be particularly beneficial during exercise, but limited data exist to quantify the additional benefits compared with the single-hormone artificial pancreas.…”

Section: Introductionmentioning

confidence: 99%

Efficacy of single-hormone and dual-hormone artificial pancreas during continuous and interval exercise in adult patients with type 1 diabetes: randomised controlled crossover trial

et al. 2016

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Aims/hypothesis The aim of this study was to assess whether the dual-hormone (insulin and glucagon) artificial pancreas reduces hypoglycaemia compared with the single-hormone (insulin alone) artificial pancreas during two types of exercise. Methods An open-label randomised crossover study comparing both systems in 17 adults with type 1 diabetes (age, 37.2 ± 13.6 years; HbA 1c, 8.0 ± 1.0% [63.9 ± 10.2 mmol/mol]) during two exercise types on an ergocycle and matched for energy expenditure: continuous (60% V : O 2peak for 60 min) and interval (2 min alternating periods at 85% and 50% V : O 2peak for 40 min, with two 10 min periods at 45% V : O 2peak at the start and end of the session). Blocked randomisation (size of four) with a 1:1:1:1 allocation ratio was computer generated. The artificial pancreas was applied from 15:30 hours until 19:30 hours; exercise was started at 18:00 hours and announced 20 min earlier to the systems. The study was conducted at the Institut de recherches cliniques de Montréal. Results During single-hormone control compared with dualhormone control, exercise-induced hypoglycaemia (plasma glucose <3.3 mmol/l with symptoms or <3.0 mmol/l regardless of symptoms) was observed in four (23.5%) vs two (11.8%) interventions (p = 0.5) for continuous exercise and in six (40%) vs one (6.25%) intervention (p = 0.07) for interval exercise. For the pooled analysis (single vs dual hormone), the median (interquartile range) percentage time spent at glucose levels below 4.0 mmol/l was 11% (0.0-46.7%) vs 0% (0-0%; p = 0.0001) and at glucose levels between 4.0 and 10.0 mmol/l was 71.4% (53.2-100%) vs 100% (100-100%; p = 0.003). Higher doses of glucagon were needed during continuous (0.126 ± 0.057 mg) than during interval exercise (0.093 ± 0.068 mg) (p = 0.03), with no reported side-effects in all interventions.A. Haidar and R. Rabasa-Lhoret are joint senior authors.Electronic supplementary material The online version of this article (doi:10.1007/s00125-016-4107-0) contains peer-reviewed but unedited supplementary material, which is available to authorised users.* Rémi Rabasa-Lhoret

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“…The glucose information along with additional types of information from accelerometers, and other sensors can be combined to deliver a recommended insulin infusion rate that can change almost continuously in response to changes in the metabolic milieu. 28 In such a system, it is necessary for decisions to be made locally with a processor located either worn by or carried by the patient (or somehow in the vicinity of the patient at all times) rather than by a cloud-based server which is going to suffer from potential delays in processing or temporary data dropout in case of data transmission problems. 29 Artificial pancreas systems in use and under development all use fog or edge computing systems.…”

Section: Diabetes Devices Currently Using Fog Computingmentioning

confidence: 99%

Fog Computing and Edge Computing Architectures for Processing Data From Diabetes Devices Connected to the Medical Internet of Things

Klonoff

2017

J Diabetes Sci Technol

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The Internet of Things (IoT) is generating an immense volume of data. With cloud computing, medical sensor and actuator data can be stored and analyzed remotely by distributed servers. The results can then be delivered via the Internet. The number of devices in IoT includes such wireless diabetes devices as blood glucose monitors, continuous glucose monitors, insulin pens, insulin pumps, and closed-loop systems. The cloud model for data storage and analysis is increasingly unable to process the data avalanche, and processing is being pushed out to the edge of the network closer to where the datagenerating devices are. Fog computing and edge computing are two architectures for data handling that can offload data from the cloud, process it nearby the patient, and transmit information machine-to-machine or machine-to-human in milliseconds or seconds. Sensor data can be processed near the sensing and actuating devices with fog computing (with local nodes) and with edge computing (within the sensing devices). Compared to cloud computing, fog computing and edge computing offer five advantages: (1) greater data transmission speed, (2) less dependence on limited bandwidths, (3) greater privacy and security, (4) greater control over data generated in foreign countries where laws may limit use or permit unwanted governmental access, and (5) lower costs because more sensor-derived data are used locally and less data are transmitted remotely. Connected diabetes devices almost all use fog computing or edge computing because diabetes patients require a very rapid response to sensor input and cannot tolerate delays for cloud computing.

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Incorporating an Exercise Detection, Grading, and Hormone Dosing Algorithm Into the Artificial Pancreas Using Accelerometry and Heart Rate

Cited by 98 publications

References 29 publications

Cross-Validation of a Glucose-Insulin-Glucagon Pharmacodynamics Model for Simulation Using Data From Patients With Type 1 Diabetes

Cross-Validation of a Glucose-Insulin-Glucagon Pharmacodynamics Model for Simulation Using Data From Patients With Type 1 Diabetes

Efficacy of single-hormone and dual-hormone artificial pancreas during continuous and interval exercise in adult patients with type 1 diabetes: randomised controlled crossover trial

Fog Computing and Edge Computing Architectures for Processing Data From Diabetes Devices Connected to the Medical Internet of Things

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