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Shehab M, Cohen RM, Brehm B, Bakas T. Accuracy and Feasibility of Using a Smartphone Application for Carbohydrate Counting Versus Traditional Carbohydrate Counting for Adults With Insulin-Treated Diabetes. J Diabetes Sci Technol 2024:19322968241248606. [PMID: 38682598 DOI: 10.1177/19322968241248606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/01/2024]
Abstract
BACKGROUND Patients with insulin-treated diabetes struggle with performing accurate carbohydrate counting for proper blood glucose control. Little is known about the comparative accuracy and feasibility of carbohydrate counting methods. PURPOSE The purpose of this study was to determine whether carbohydrate counting using a smartphone application is more accurate and feasible than a traditional method. THEORETICAL/CONCEPTUAL FRAMEWORK Based on a conceptual model derived from the Technology Acceptance Model, feasibility was defined as usefulness, ease of use, and behavioral intention to use each method. METHODS A standardized meal was presented to 20 adults with insulin-treated diabetes who counted carbohydrates using traditional and smartphone methods. Accuracy was measured by comparing carbohydrate counting estimates with the standardized meal values. Perceived feasibility (usefulness, ease of use, behavioral intention) was measured using rating forms derived from the Technology Acceptance Model. RESULTS The number of training and estimation minutes were significantly higher for the traditional method than the smartphone method (Z = -3.83, P < .05; Z = -2.30, P < .05). The traditional method took an additional 1.4 minutes for estimation and 12.5 minutes for training. There were no significant differences in accuracy between traditional and smartphone methods for carbohydrate counting (Wilcoxon signed-rank test, Z = -1.10, P = .28). There were no significant differences between traditional and smartphone methods for feasibility (usefulness, Z = -.10, P = .95; ease of use, Z = -.36, P = .72; or behavioral intention, Z = -.94, P = .35). CONCLUSION While both traditional and smartphone methods were found to be similar in terms of accuracy and feasibility, the smartphone method took less time for training and for carbohydrate estimation.
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Affiliation(s)
- Mohammad Shehab
- University of Cincinnati College of Nursing, Cincinnati, OH, USA
| | - Robert M Cohen
- University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Cincinnati Veterans Affairs Medical Center, Cincinnati, OH, USA
| | - Bonnie Brehm
- University of Cincinnati College of Nursing, Cincinnati, OH, USA
| | - Tamilyn Bakas
- University of Cincinnati College of Nursing, Cincinnati, OH, USA
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2
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Haidar A, Legault L, Raffray M, Gouchie-Provencher N, Jafar A, Devaux M, Ghanbari M, Rabasa-Lhoret R. A Randomized Crossover Trial to Compare Automated Insulin Delivery (the Artificial Pancreas) With Carbohydrate Counting or Simplified Qualitative Meal-Size Estimation in Type 1 Diabetes. Diabetes Care 2023; 46:1372-1378. [PMID: 37134305 PMCID: PMC10300520 DOI: 10.2337/dc22-2297] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 04/02/2023] [Indexed: 05/05/2023]
Abstract
OBJECTIVE Qualitative meal-size estimation has been proposed instead of quantitative carbohydrate (CHO) counting with automated insulin delivery. We aimed to assess the noninferiority of qualitative meal-size estimation strategy. RESEARCH DESIGN AND METHODS We conducted a two-center, randomized, crossover, noninferiority trial to compare 3 weeks of automated insulin delivery with 1) CHO counting and 2) qualitative meal-size estimation in adults with type 1 diabetes. Qualitative meal-size estimation categories were low, medium, high, or very high CHO and were defined as <30 g, 30-60 g, 60-90 g, and >90 g CHO, respectively. Prandial insulin boluses were calculated as the individualized insulin to CHO ratios multiplied by 15, 35, 65, and 95, respectively. Closed-loop algorithms were otherwise identical in the two arms. The primary outcome was time in range 3.9-10.0 mmol/L, with a predefined noninferiority margin of 4%. RESULTS A total of 30 participants completed the study (n = 20 women; age 44 (SD 17) years; A1C 7.4% [0.7%]). The mean time in the 3.9-10.0 mmol/L range was 74.1% (10.0%) with CHO counting and 70.5% (11.2%) with qualitative meal-size estimation; mean difference was -3.6% (8.3%; noninferiority P = 0.78). Frequencies of times at <3.9 mmol/L and <3.0 mmol/L were low (<1.6% and <0.2%) in both arms. Automated basal insulin delivery was higher in the qualitative meal-size estimation arm (34.6 vs. 32.6 units/day; P = 0.003). CONCLUSIONS Though the qualitative meal-size estimation method achieved a high time in range and low time in hypoglycemia, noninferiority was not confirmed.
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Affiliation(s)
- Ahmad Haidar
- Department of Biomedical Engineering, McGill University, Montréal, Quebéc, Canada
- The Research Institute of McGill University Health Centre, Montréal, Québec, Canada
| | - Laurent Legault
- Montreal Children's Hospital, McGill University Health Centre, Montréal, Québec, Canada
| | - Marie Raffray
- Institut de Recherches Cliniques de Montréal, Montréal, Québec, Canada
| | | | - Adnan Jafar
- Department of Biomedical Engineering, McGill University, Montréal, Quebéc, Canada
| | - Marie Devaux
- Institut de Recherches Cliniques de Montréal, Montréal, Québec, Canada
| | - Milad Ghanbari
- Department of Biomedical Engineering, McGill University, Montréal, Quebéc, Canada
| | - Rémi Rabasa-Lhoret
- Institut de Recherches Cliniques de Montréal, Montréal, Québec, Canada
- Nutrition Department, Faculty of Medicine, Université de Montréal, Montréal, Québec, Canada
- Montreal Diabetes Research Center and Endocrinology Division Centre Hospitalier de l’Université de Montréal, Saint-Denis Montréal, Québec, Canada
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3
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Huang HW, You SS, Di Tizio L, Li C, Raftery E, Ehmke C, Steiger C, Li J, Wentworth A, Ballinger I, Gwynne D, Nan K, Liang JY, Li J, Byrne JD, Collins J, Tamang S, Ishida K, Halperin F, Traverso G. An automated all-in-one system for carbohydrate tracking, glucose monitoring, and insulin delivery. J Control Release 2022; 343:31-42. [PMID: 34998917 DOI: 10.1016/j.jconrel.2022.01.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 12/21/2021] [Accepted: 01/01/2022] [Indexed: 12/12/2022]
Abstract
Glycemic control through titration of insulin dosing remains the mainstay of diabetes mellitus treatment. Insulin therapy is generally divided into dosing with long- and short-acting insulin, where long-acting insulin provides basal coverage and short-acting insulin supports glycemic excursions associated with eating. The dosing of short-acting insulin often involves several steps for the user including blood glucose measurement and integration of potential carbohydrate loads to inform safe and appropriate dosing. The significant burden placed on the user for blood glucose measurement and effective carbohydrate counting can manifest in substantial effects on adherence. Through the application of computer vision, we have developed a smartphone-based system that is able to detect the carbohydrate load of food by simply taking a single image of the food and converting that information into a required insulin dose by incorporating a blood glucose measurement. Moreover, we report the development of comprehensive all-in-one insulin delivery systems that streamline all operations that peripheral devices require for safe insulin administration, which in turn significantly reduces the complexity and time required for titration of insulin. The development of an autonomous system that supports maximum ease and accuracy of insulin dosing will transform our ability to more effectively support patients with diabetes.
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Affiliation(s)
- Hen-Wei Huang
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Division of Gastroenterology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Siheng Sean You
- Division of Gastroenterology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Luca Di Tizio
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Canchen Li
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Erin Raftery
- Division of Gastroenterology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Claas Ehmke
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Christoph Steiger
- Division of Gastroenterology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Junwei Li
- Division of Gastroenterology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Adam Wentworth
- Division of Gastroenterology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ian Ballinger
- Division of Gastroenterology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Declan Gwynne
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Division of Gastroenterology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Kewang Nan
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Jia Y Liang
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Division of Gastroenterology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Jason Li
- Division of Gastroenterology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - James D Byrne
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Division of Gastroenterology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Joy Collins
- Division of Gastroenterology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Siddartha Tamang
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Keiko Ishida
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Florencia Halperin
- Division of Endocrinology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Giovanni Traverso
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Division of Gastroenterology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
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4
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Templer S. Closed-Loop Insulin Delivery Systems: Past, Present, and Future Directions. Front Endocrinol (Lausanne) 2022; 13:919942. [PMID: 35733769 PMCID: PMC9207329 DOI: 10.3389/fendo.2022.919942] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 05/06/2022] [Indexed: 12/16/2022] Open
Abstract
Closed-loop (artificial pancreas) systems for automated insulin delivery have been likened to the holy grail of diabetes management. The first iterations of glucose-responsive insulin delivery were pioneered in the 1960s and 1970s, with the development of systems that used venous glucose measurements to dictate intravenous infusions of insulin and dextrose in order to maintain normoglycemia. Only recently have these bulky, bedside technologies progressed to miniaturized, wearable devices. These modern closed-loop systems use interstitial glucose sensing, subcutaneous insulin pumps, and increasingly sophisticated algorithms. As the number of commercially available hybrid closed-loop systems has grown, so too has the evidence supporting their efficacy. Future challenges in closed-loop technology include the development of fully closed-loop systems that do not require user input for meal announcements or carbohydrate counting. Another evolving avenue in research is the addition of glucagon to mitigate the risk of hypoglycemia and allow more aggressive insulin dosing.
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5
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Gillingham MB, Li Z, Beck RW, Calhoun P, Castle JR, Clements M, Dassau E, Doyle FJ, Gal RL, Jacobs P, Patton SR, Rickels MR, Riddell M, Martin CK. Assessing Mealtime Macronutrient Content: Patient Perceptions Versus Expert Analyses via a Novel Phone App. Diabetes Technol Ther 2021; 23:85-94. [PMID: 32833544 PMCID: PMC7868577 DOI: 10.1089/dia.2020.0357] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Background: People with type 1 diabetes estimate meal carbohydrate content to accurately dose insulin, yet, protein and fat content of meals also influences postprandial glycemia. We examined accuracy of macronutrient content estimation via a novel phone app. Participant estimates were compared with expert nutrition analyses performed via the Remote Food Photography Method© (RFPM©). Methods: Data were collected through a novel phone app. Participants were asked to take photos of meals/snacks on the day of and day after scheduled exercise, enter carbohydrate estimates, and categorize meals as low, typical, or high protein and fat. Glycemia was measured via continuous glucose monitoring. Results: Participants (n = 48) were 15-68 years (34 ± 14 years); 40% were female. The phone app plus RFPM© analysis captured 88% ± 29% of participants' estimated total energy expenditure. The majority (70%) of both low-protein and low-fat meals were accurately classified. Only 22% of high-protein meals and 17% of high-fat meals were accurately classified. Forty-nine percent of meals with <30 g of carbohydrates were overestimated by an average of 25.7 ± 17.2 g. The majority (64%) of large carbohydrate meals (≥60 g) were underestimated by an average of 53.6 ± 33.8 g. Glycemic response to large carbohydrate meals was similar between participants who underestimated or overestimated carbohydrate content, suggesting that factors beyond carbohydrate counting may impact postprandial glycemic response. Conclusions: Accurate estimation of total macronutrients in meals could be leveraged to improve insulin decision support tools and closed loop insulin delivery systems; development of tools to improve macronutrient estimation skills should be considered.
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Affiliation(s)
- Melanie B. Gillingham
- Oregon Health and Sciences University, Portland, Oregon, USA
- Address correspondence to: Melanie B. Gillingham, PhD, RD, Department of Molecular and Medical Genetics, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Mailcode L103, Portland, OR 97239, USA
| | - Zoey Li
- Jaeb Center for Health Research, Tampa, Florida, USA
| | - Roy W. Beck
- Jaeb Center for Health Research, Tampa, Florida, USA
| | - Peter Calhoun
- Jaeb Center for Health Research, Tampa, Florida, USA
| | | | - Mark Clements
- Children's Mercy Hospital, Kansas City, Missouri, USA
| | - Eyal Dassau
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
| | - Francis J. Doyle
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
| | - Robin L. Gal
- Jaeb Center for Health Research, Tampa, Florida, USA
| | - Peter Jacobs
- Oregon Health and Sciences University, Portland, Oregon, USA
| | | | - Michael R. Rickels
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | - Corby K. Martin
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, Louisiana, USA
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6
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Zhu T, Li K, Kuang L, Herrero P, Georgiou P. An Insulin Bolus Advisor for Type 1 Diabetes Using Deep Reinforcement Learning. SENSORS (BASEL, SWITZERLAND) 2020; 20:E5058. [PMID: 32899979 PMCID: PMC7570884 DOI: 10.3390/s20185058] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 08/25/2020] [Accepted: 09/04/2020] [Indexed: 12/31/2022]
Abstract
(1) Background: People living with type 1 diabetes (T1D) require self-management to maintain blood glucose (BG) levels in a therapeutic range through the delivery of exogenous insulin. However, due to the various variability, uncertainty and complex glucose dynamics, optimizing the doses of insulin delivery to minimize the risk of hyperglycemia and hypoglycemia is still an open problem. (2) Methods: In this work, we propose a novel insulin bolus advisor which uses deep reinforcement learning (DRL) and continuous glucose monitoring to optimize insulin dosing at mealtime. In particular, an actor-critic model based on deep deterministic policy gradient is designed to compute mealtime insulin doses. The proposed system architecture uses a two-step learning framework, in which a population model is first obtained and then personalized by subject-specific data. Prioritized memory replay is adopted to accelerate the training process in clinical practice. To validate the algorithm, we employ a customized version of the FDA-accepted UVA/Padova T1D simulator to perform in silico trials on 10 adult subjects and 10 adolescent subjects. (3) Results: Compared to a standard bolus calculator as the baseline, the DRL insulin bolus advisor significantly improved the average percentage time in target range (70-180 mg/dL) from 74.1%±8.4% to 80.9%±6.9% (p<0.01) and 54.9%±12.4% to 61.6%±14.1% (p<0.01) in the the adult and adolescent cohorts, respectively, while reducing hypoglycemia. (4) Conclusions: The proposed algorithm has the potential to improve mealtime bolus insulin delivery in people with T1D and is a feasible candidate for future clinical validation.
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Affiliation(s)
- Taiyu Zhu
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (T.Z.); (L.K.); (P.H.); (P.G.)
| | - Kezhi Li
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (T.Z.); (L.K.); (P.H.); (P.G.)
- Institute of Health Informatics, University College London, London NW1 2DA, UK
| | - Lei Kuang
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (T.Z.); (L.K.); (P.H.); (P.G.)
| | - Pau Herrero
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (T.Z.); (L.K.); (P.H.); (P.G.)
| | - Pantelis Georgiou
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (T.Z.); (L.K.); (P.H.); (P.G.)
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7
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Contreras I, Vehi J. Artificial Intelligence for Diabetes Management and Decision Support: Literature Review. J Med Internet Res 2018; 20:e10775. [PMID: 29848472 PMCID: PMC6000484 DOI: 10.2196/10775] [Citation(s) in RCA: 183] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Revised: 05/15/2018] [Accepted: 05/15/2018] [Indexed: 01/03/2023] Open
Abstract
Background Artificial intelligence methods in combination with the latest technologies, including medical devices, mobile computing, and sensor technologies, have the potential to enable the creation and delivery of better management services to deal with chronic diseases. One of the most lethal and prevalent chronic diseases is diabetes mellitus, which is characterized by dysfunction of glucose homeostasis. Objective The objective of this paper is to review recent efforts to use artificial intelligence techniques to assist in the management of diabetes, along with the associated challenges. Methods A review of the literature was conducted using PubMed and related bibliographic resources. Analyses of the literature from 2010 to 2018 yielded 1849 pertinent articles, of which we selected 141 for detailed review. Results We propose a functional taxonomy for diabetes management and artificial intelligence. Additionally, a detailed analysis of each subject category was performed using related key outcomes. This approach revealed that the experiments and studies reviewed yielded encouraging results. Conclusions We obtained evidence of an acceleration of research activity aimed at developing artificial intelligence-powered tools for prediction and prevention of complications associated with diabetes. Our results indicate that artificial intelligence methods are being progressively established as suitable for use in clinical daily practice, as well as for the self-management of diabetes. Consequently, these methods provide powerful tools for improving patients’ quality of life.
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Affiliation(s)
- Ivan Contreras
- Modeling, Identification and Control Laboratory, Institut d'Informatica i Aplicacions, Universitat de Girona, Girona, Spain
| | - Josep Vehi
- Modeling, Identification and Control Laboratory, Institut d'Informatica i Aplicacions, Universitat de Girona, Girona, Spain.,Centro de Investigación Biomédica en Red de Diabetes y Enfermadades Metabólicas Asociadas, Girona, Spain
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8
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Gingras V, Taleb N, Roy-Fleming A, Legault L, Rabasa-Lhoret R. The challenges of achieving postprandial glucose control using closed-loop systems in patients with type 1 diabetes. Diabetes Obes Metab 2018; 20:245-256. [PMID: 28675686 PMCID: PMC5810921 DOI: 10.1111/dom.13052] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Revised: 06/27/2017] [Accepted: 06/29/2017] [Indexed: 01/17/2023]
Abstract
For patients with type 1 diabetes, closed-loop delivery systems (CLS) combining an insulin pump, a glucose sensor and a dosing algorithm allowing a dynamic hormonal infusion have been shown to improve glucose control when compared with conventional therapy. Yet, reducing glucose excursion and simplification of prandial insulin doses remain a challenge. The objective of this literature review is to examine current meal-time strategies in the context of automated delivery systems in adults and children with type 1 diabetes. Current challenges and considerations for post-meal glucose control will also be discussed. Despite promising results with meal detection, the fully automated CLS has yet failed to provide comparable glucose control to CLS with carbohydrate-matched bolus in the post-meal period. The latter strategy has been efficient in controlling post-meal glucose using different algorithms and in various settings, but at the cost of a meal carbohydrate counting burden for patients. Further improvements in meal detection algorithms or simplified meal-priming boluses may represent interesting avenues. The greatest challenges remain in regards to the pharmacokinetic and dynamic profiles of available rapid insulins as well as sensor accuracy and lag-time. New and upcoming faster acting insulins could provide important benefits. Multi-hormone CLS (eg, dual-hormone combining insulin with glucagon or pramlintide) and adjunctive therapy (eg, GLP-1 and SGLT2 inhibitors) also represent promising options. Meal glucose control with the artificial pancreas remains an important challenge for which the optimal strategy is still to be determined.
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Affiliation(s)
- Véronique Gingras
- Institut de Recherches Cliniques de Montréal, Montreal, Quebec, Canada
- Department of nutrition, Université de Montréal, Montreal, Quebec, Canada
| | - Nadine Taleb
- Institut de Recherches Cliniques de Montréal, Montreal, Quebec, Canada
- Department of biomedical sciences, Université de Montréal, Montreal, Quebec, Canada
| | - Amélie Roy-Fleming
- Institut de Recherches Cliniques de Montréal, Montreal, Quebec, Canada
- Department of nutrition, Université de Montréal, Montreal, Quebec, Canada
| | - Laurent Legault
- Institut de Recherches Cliniques de Montréal, Montreal, Quebec, Canada
- Montreal Children’s Hospital, McGill University Health Center, Montreal, Quebec, Canada
| | - Rémi Rabasa-Lhoret
- Institut de Recherches Cliniques de Montréal, Montreal, Quebec, Canada
- Department of nutrition, Université de Montréal, Montreal, Quebec, Canada
- Montreal Diabetes Research Center (MDRC), Montreal, Quebec, Canada
- Research Center of the Université de Montréal Hospital Center (CRCHUM), Montreal, Quebec, Canada
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9
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Daskalaki E, Diem P, Mougiakakou SG. Model-Free Machine Learning in Biomedicine: Feasibility Study in Type 1 Diabetes. PLoS One 2016; 11:e0158722. [PMID: 27441367 PMCID: PMC4956312 DOI: 10.1371/journal.pone.0158722] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2015] [Accepted: 06/21/2016] [Indexed: 11/23/2022] Open
Abstract
Although reinforcement learning (RL) is suitable for highly uncertain systems, the applicability of this class of algorithms to medical treatment may be limited by the patient variability which dictates individualised tuning for their usually multiple algorithmic parameters. This study explores the feasibility of RL in the framework of artificial pancreas development for type 1 diabetes (T1D). In this approach, an Actor-Critic (AC) learning algorithm is designed and developed for the optimisation of insulin infusion for personalised glucose regulation. AC optimises the daily basal insulin rate and insulin:carbohydrate ratio for each patient, on the basis of his/her measured glucose profile. Automatic, personalised tuning of AC is based on the estimation of information transfer (IT) from insulin to glucose signals. Insulin-to-glucose IT is linked to patient-specific characteristics related to total daily insulin needs and insulin sensitivity (SI). The AC algorithm is evaluated using an FDA-accepted T1D simulator on a large patient database under a complex meal protocol, meal uncertainty and diurnal SI variation. The results showed that 95.66% of time was spent in normoglycaemia in the presence of meal uncertainty and 93.02% when meal uncertainty and SI variation were simultaneously considered. The time spent in hypoglycaemia was 0.27% in both cases. The novel tuning method reduced the risk of severe hypoglycaemia, especially in patients with low SI.
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Affiliation(s)
- Elena Daskalaki
- Diabetes Technology Research Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland
| | - Peter Diem
- Division of Endocrinology, Diabetes and Clinical Nutrition, Bern University Hospital “Inselspital”, 3010 Bern, Switzerland
| | - Stavroula G. Mougiakakou
- Diabetes Technology Research Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland
- Division of Endocrinology, Diabetes and Clinical Nutrition, Bern University Hospital “Inselspital”, 3010 Bern, Switzerland
- * E-mail:
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