Detection and Control of Unannounced Exercise in the Artificial Pancreas Without Additional Physiological Signals

Ramkissoon, Charrise Mary; Bertachi, Arthur; Beneyto, Aleix; Bondia, Jorge; Vehı́, Josep

doi:10.1109/jbhi.2019.2898558

Cited by 21 publications

(22 citation statements)

References 32 publications

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“…Current artificial pancreas systems also have several challenges that need to be addressed if they are to be used in conjunction with bioelectronic medicine. Among them, the delays in interstitial glucose sensing and hormonal subcutaneous absorption (Ramli et al 2019;Taleb et al 2019;Gingras et al 2018;Herrero et al 2017), failures in the insulin pump (Meneghetti et al 2018), and glycaemic control during meals (Ramkissoon et al 2018) and exercise (Ramkissoon et al 2020) are the most critical. Despite these challenges, the use of the artificial pancreas is associated with high levels of satisfaction and quality of life, and reduced fear of hypoglycaemic events among the users (Ramli et al 2019).…”

Section: Challengesmentioning

confidence: 99%

Closed-loop bioelectronic medicine for diabetes management

2020

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Modulation of the nervous system by delivering electrical or pharmaceutical agents has contributed to the development of novel treatments to serious health disorders. Recent advances in multidisciplinary research has enabled the emergence of a new powerful therapeutic approach called bioelectronic medicine. Bioelectronic medicine exploits the fact that every organ in our bodies is neurally innervated and thus electrical interfacing with peripheral nerves can be a potential pathway for diagnosing or treating diseases such as diabetes. In this context, a plethora of studies have confirmed the important role of the nervous system in maintaining a tight regulation of glucose homeostasis. This has initiated new research exploring the opportunities of bioelectronic medicine for improving glucose control in people with diabetes, including regulation of gastric emptying, insulin sensitivity, and secretion of pancreatic hormones. Moreover, the development of novel closed-loop strategies aims to provide effective, specific and safe interfacing with the nervous system, and thereby targeting the organ of interest. This is especially valuable in the context of chronic diseases such as diabetes, where closed-loop bioelectronic medicine promises to provide real-time, autonomous and patient-specific therapies. In this article, we present an overview of the state-of-the-art for closed-loop neuromodulation systems in relation to diabetes and discuss future related opportunities for management of this chronic disease.

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Section: Challengesmentioning

confidence: 99%

Closed-loop bioelectronic medicine for diabetes management

2020

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“…Furthermore, the DRB algorithm may not be applicable in fully automatic AP systems, in which patients do not need to announce meals. Finally, the adjustments of IOB is a major task for AP systems which considers the SAFE layer, and the proper adjustment of this constraint also plays an important role even during physical activity [39,40], by reducing the amount of injected insulin during and after exercise.…”

Section: Scenario Cmentioning

confidence: 99%

Dynamic Rule-Based Algorithm to Tune Insulin-on-Board Constraints for a Hybrid Artificial Pancreas System

Bertachi

Biagi

Beneyto

et al. 2020

Journal of Healthcare Engineering

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The artificial pancreas (AP) is a system intended to control blood glucose levels through automated insulin infusion, reducing the burden of subjects with type 1 diabetes to manage their condition. To increase patients’ safety, some systems limit the allowed amount of insulin active in the body, known as insulin-on-board (IOB). The safety auxiliary feedback element (SAFE) layer has been designed previously to avoid overreaction of the controller and thus avoiding hypoglycemia. In this work, a new method, so-called “dynamic rule-based algorithm,” is presented in order to adjust the limits of IOB in real time. The algorithm is an extension of a previously designed method which aimed to adjust the limits of IOB for a meal with 60 grams of carbohydrates (CHO). The proposed method is intended to be applied on hybrid AP systems during 24 h operation. It has been designed by combining two different strategies to set IOB limits for different situations: (1) fasting periods and (2) postprandial periods, regardless of the size of the meal. The UVa/Padova simulator is considered to assess the performance of the method, considering challenging scenarios. In silico results showed that the method is able to reduce the time spent in hypoglycemic range, improving patients’ safety, which reveals the feasibility of the approach to be included in different control algorithms.

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“…The main challenges for BG regulation in T1D are the disturbances in terms of meals, exercise, stress, and variability (inter-patient and intra-patient). The UVa/Padova simulator allows the incorporation of different meal scenarios for the virtual patient (VP) population, allowing researchers to analyze the effectiveness of a control algorithm [16][17][18][19][20][21][22], validate optimization and adaptation strategies for insulin delivery [23][24][25][26], develop disturbance detection algorithms for meals [27][28][29] and exercise [30], develop methods for mitigating the risks of hypoglycemia [31,32], and integrate machine learning algorithms into conventional diabetes therapy and bolus calculator for the treatment of T1D patients [33][34][35]. In the literature, the meal scenarios used for testing BG regulation effectiveness are based on typical values considering three meals per day [36][37][38][39][40][41][42][43][44][45][46][47].…”

Section: Introductionmentioning

confidence: 99%

Generation of Virtual Patient Populations That Represent Real Type 1 Diabetes Cohorts

et al. 2021

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Preclinical testing and validation of therapeutic strategies developed for patients with type 1 diabetes (T1D) require a cohort of virtual patients (VPs). However, current simulators provide a limited number of VPs, lack real-life scenarios, and inadequately represent intra- and inter-day variability in insulin sensitivity and blood glucose (BG) profile. The generation of a realistic scenario was achieved by using the meal patterns, insulin profiles (basal and bolus), and exercise sessions estimated as disturbances using clinical data from a cohort of 14 T1D patients using the Medtronic 640G insulin pump provided by the Hospital Clínic de Barcelona. The UVa/Padova’s cohort of adult patients was used for the generation of a new cohort of VPs. Insulin model parameters were optimized and adjusted in a day-by-day fashion to replicate the clinical data to create a cohort of 75 VPs. All primary and secondary outcomes reflecting the BG profile of a T1D patient were analyzed and compared to the clinical data. The mean BG 166.3 versus 162.2 mg/dL ( = 0.19), coefficient of variation 32% versus 33% ( = 0.54), and percent of time in range (70 to 180 mg/dL) 59.6% versus 66.8% ( = 0.35) were achieved. The proposed methodology for generating a cohort of VPs is capable of mimicking the BG metrics of a real cohort of T1D patients from the Hospital Clínic de Barcelona. It can adopt the inter-day variations in the BG profile, similar to the observed clinical data, and thus provide a benchmark for preclinical testing of control techniques and therapy strategies for T1D patients.

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Detection and Control of Unannounced Exercise in the Artificial Pancreas Without Additional Physiological Signals

Cited by 21 publications

References 32 publications

Closed-loop bioelectronic medicine for diabetes management

Closed-loop bioelectronic medicine for diabetes management

Dynamic Rule-Based Algorithm to Tune Insulin-on-Board Constraints for a Hybrid Artificial Pancreas System

Generation of Virtual Patient Populations That Represent Real Type 1 Diabetes Cohorts

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