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Ahn DT. Automated Bolus Calculators and Connected Insulin Pens: A Smart Combination for Multiple Daily Injection Insulin Therapy. J Diabetes Sci Technol 2022; 16:605-609. [PMID: 34933594 PMCID: PMC9294589 DOI: 10.1177/19322968211062624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Although automated bolus calculators (ABCs) have become a mainstay in insulin pump therapy, they have not achieved similar levels of adoption by persons with diabetes (PWD) using multiple daily injections of insulin (MDI). Only a small number of blood glucose meters (BGMs) have incorporated ABC functionality and the proliferation of unregulated ABC smartphone apps raised safety concerns and eventually led to Food and Drug Administration (FDA)-mandated regulatory oversight for these types of apps. With the recent introduction of smartphone-connected insulin pens, manufacturer-supported companion ABC apps may offer an ideal solution for PWD and health care professionals that reduces errors of mental math when calculating bolus insulin dosing, increases the quality of diabetes data reporting, and improves glycemic outcomes.
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Affiliation(s)
- David T Ahn
- Mary & Dick Allen Diabetes
Center, Hoag Memorial Hospital Presbyterian, Newport Beach, CA, USA
- David Ahn, MD, Mary & Dick
Allen Diabetes Center, Hoag Memorial Hospital Presbyterian, 520
Superior Avenue, Suite 150, Newport Beach, CA 92663, USA.
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Pinsker JE, Church MM, Brown SA, Voelmle MK, Bode BW, Narron B, Huyett LM, Lee JB, O'Connor J, Benjamin E, Dumais B, Ly TT. Clinical Evaluation of a Novel CGM-Informed Bolus Calculator with Automatic Glucose Trend Adjustment. Diabetes Technol Ther 2022; 24:18-25. [PMID: 34491825 PMCID: PMC8783627 DOI: 10.1089/dia.2021.0140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Background: Expert opinion guidelines and limited data from clinical trials recommend adjustment to bolus insulin doses based on continuous glucose monitor (CGM) trend data, yet minimal evidence exists to support this approach. We performed a clinical evaluation of a novel CGM-informed bolus calculator (CIBC) with automatic insulin bolus dose adjustment based on CGM trend used with sensor-augmented pump therapy. Materials and Methods: In this multicenter, outpatient study, participants 6-70 years of age with type 1 diabetes (T1D) used the Omnipod® 5 System in Manual Mode, first for 7 days without a connected CGM (standard bolus calculator, SBC, phase 1) and then for 7 days with a connected CGM using the CIBC (CIBC phase 2). The integrated bolus calculator used stored pump settings plus user-estimated meal size and/or either a manually entered capillary glucose value (SBC phase) or an imported current CGM value and trend (CIBC phase) to recommend a bolus amount. The CIBC automatically increased or decreased the suggested bolus amount based on the CGM trend. Results: Twenty-five participants, (mean ± standard deviation) 27 ± 15 years of age, with T1D duration 12 ± 9 years and A1C 7.0% ± 0.9% completed the study. There were significantly fewer sensor readings <70 mg/dL 4 h postbolus with the CIBC compared to the SBC (2.1% ± 2.0% vs. 2.8 ± 2.7, P = 0.03), while percent of sensor readings >180 and 70-180 mg/dL remained the same. There was no difference in insulin use or number of boluses given between the two phases. Conclusion: The CIBC was safe when used with the Omnipod 5 System in Manual Mode, with fewer hypoglycemic readings in the postbolus period compared to the SBC. This trial was registered at ClinicalTrials.gov (NCT04320069).
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Affiliation(s)
- Jordan E. Pinsker
- Sansum Diabetes Research Institute, Santa Barbara, California, USA
- Results of this study were presented in abstract form at the 14th International Conference on Advanced Technologies & Treatments for Diabetes, June 2021
| | - Mei Mei Church
- Sansum Diabetes Research Institute, Santa Barbara, California, USA
- Results of this study were presented in abstract form at the 14th International Conference on Advanced Technologies & Treatments for Diabetes, June 2021
| | - Sue A. Brown
- Division of Endocrinology, Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
- Results of this study were presented in abstract form at the 14th International Conference on Advanced Technologies & Treatments for Diabetes, June 2021
| | - Mary K. Voelmle
- Division of Endocrinology, Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia, USA
- Results of this study were presented in abstract form at the 14th International Conference on Advanced Technologies & Treatments for Diabetes, June 2021
| | - Bruce W. Bode
- Atlanta Diabetes Associates, Atlanta, Georgia, USA
- Results of this study were presented in abstract form at the 14th International Conference on Advanced Technologies & Treatments for Diabetes, June 2021
| | - Brooke Narron
- Atlanta Diabetes Associates, Atlanta, Georgia, USA
- Results of this study were presented in abstract form at the 14th International Conference on Advanced Technologies & Treatments for Diabetes, June 2021
| | - Lauren M. Huyett
- Insulet Corporation, Acton, Massachusetts, USA
- Results of this study were presented in abstract form at the 14th International Conference on Advanced Technologies & Treatments for Diabetes, June 2021
| | - Joon Bok Lee
- Insulet Corporation, Acton, Massachusetts, USA
- Results of this study were presented in abstract form at the 14th International Conference on Advanced Technologies & Treatments for Diabetes, June 2021
| | - Jason O'Connor
- Insulet Corporation, Acton, Massachusetts, USA
- Results of this study were presented in abstract form at the 14th International Conference on Advanced Technologies & Treatments for Diabetes, June 2021
| | - Eric Benjamin
- Insulet Corporation, Acton, Massachusetts, USA
- Results of this study were presented in abstract form at the 14th International Conference on Advanced Technologies & Treatments for Diabetes, June 2021
| | - Bonnie Dumais
- Insulet Corporation, Acton, Massachusetts, USA
- Results of this study were presented in abstract form at the 14th International Conference on Advanced Technologies & Treatments for Diabetes, June 2021
| | - Trang T. Ly
- Insulet Corporation, Acton, Massachusetts, USA
- Results of this study were presented in abstract form at the 14th International Conference on Advanced Technologies & Treatments for Diabetes, June 2021
- Address correspondence to: Trang T. Ly, MBBS, FRACP, PhD, Insulet Corporation, 100 Nagog Park, Acton, MA 01720, USA
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Avari P, Leal Y, Herrero P, Wos M, Jugnee N, Arnoriaga-Rodríguez M, Thomas M, Liu C, Massana Q, Lopez B, Nita L, Martin C, Fernández-Real JM, Oliver N, Fernández-Balsells M, Reddy M. Safety and Feasibility of the PEPPER Adaptive Bolus Advisor and Safety System: A Randomized Control Study. Diabetes Technol Ther 2021; 23:175-186. [PMID: 33048581 DOI: 10.1089/dia.2020.0301] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Background: The Patient Empowerment through Predictive Personalized Decision Support (PEPPER) system provides personalized bolus advice for people with type 1 diabetes. The system incorporates an adaptive insulin recommender system (based on case-based reasoning, an artificial intelligence methodology), coupled with a safety system, which includes predictive glucose alerts and alarms, predictive low-glucose suspend, personalized carbohydrate recommendations, and dynamic bolus insulin constraint. We evaluated the safety and efficacy of the PEPPER system compared to a standard bolus calculator. Methods: This was an open-labeled multicenter randomized controlled crossover study. Following 4-week run-in, participants were randomized to PEPPER/Control or Control/PEPPER in a 1:1 ratio for 12 weeks. Participants then crossed over after a washout period. The primary end-point was percentage time in range (TIR, 3.9-10.0 mmol/L [70-180 mg/dL]). Secondary outcomes included glycemic variability, quality of life, and outcomes on the safety system and insulin recommender. Results: Fifty-four participants on multiple daily injections (MDI) or insulin pump completed the run-in period, making up the intention-to-treat analysis. Median (interquartile range) age was 41.5 (32.3-49.8) years, diabetes duration 21.0 (11.5-26.0) years, and HbA1c 61.0 (58.0-66.1) mmol/mol. No significant difference was observed for percentage TIR between the PEPPER and Control groups (62.5 [52.1-67.8] % vs. 58.4 [49.6-64.3] %, respectively, P = 0.27). For quality of life, participants reported higher perceived hypoglycemia with the PEPPER system despite no objective difference in time spent in hypoglycemia. Conclusions: The PEPPER system was safe, but did not change glycemic outcomes, compared to control. There is wide scope for integrating PEPPER into routine diabetes management for pump and MDI users. Further studies are required to confirm overall effectiveness. Clinical trial registration: ClinicalTrials.gov NCT03849755.
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Affiliation(s)
- Parizad Avari
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - Yenny Leal
- Diabetes, Endocrinology and Nutrition Unit, Hospital Universitari Dr. Josep Trueta, Institut d'Investigació Biomèdica de Girona, Girona, Spain
| | - Pau Herrero
- Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom
| | - Marzena Wos
- Diabetes, Endocrinology and Nutrition Unit, Hospital Universitari Dr. Josep Trueta, Institut d'Investigació Biomèdica de Girona, Girona, Spain
| | - Narvada Jugnee
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - María Arnoriaga-Rodríguez
- Diabetes, Endocrinology and Nutrition Unit, Hospital Universitari Dr. Josep Trueta, Institut d'Investigació Biomèdica de Girona, Girona, Spain
| | - Maria Thomas
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - Chengyuan Liu
- Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom
- Centre for Aerospace Manufactuiring, University of Nottingham, London, United Kingdom
| | - Quim Massana
- eXiT Research Group, Institut d'Informàtica i Aplicacions, University of Girona, Girona, Spain
| | - Beatriz Lopez
- eXiT Research Group, Institut d'Informàtica i Aplicacions, University of Girona, Girona, Spain
| | - Lucian Nita
- Department of Research & Development, RomSoft SRL, Iasi, Romania
| | - Clare Martin
- School of Engineering, Computing, and Mathematics, Oxford Brookes University, Oxford, United Kingdom
| | - José Manuel Fernández-Real
- Diabetes, Endocrinology and Nutrition Unit, Hospital Universitari Dr. Josep Trueta, Institut d'Investigació Biomèdica de Girona, Girona, Spain
- Department of Medical Sciences, Faculty of Medicine, University of Girona, Girona, Spain
- CIBEROBN Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
| | - Nick Oliver
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - Mercè Fernández-Balsells
- Diabetes, Endocrinology and Nutrition Unit, Hospital Universitari Dr. Josep Trueta, Institut d'Investigació Biomèdica de Girona, Girona, Spain
| | - Monika Reddy
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
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Brzan PP, Rotman E, Pajnkihar M, Klanjsek P. Mobile Applications for Control and Self Management of Diabetes: A Systematic Review. J Med Syst 2016; 40:210. [PMID: 27520615 DOI: 10.1007/s10916-016-0564-8] [Citation(s) in RCA: 78] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2016] [Accepted: 08/02/2016] [Indexed: 10/21/2022]
Abstract
Mobile applications (apps) can be very useful software on smartphones for all aspects of people's lives. Chronic diseases, such as diabetes, can be made manageable with the support of mobile apps. Applications on smartphones can also help people with diabetes to control their fitness and health. A systematic review of free apps in the English language for smartphones in three of the most popular mobile app stores: Google Play (Android), App Store (iOS) and Windows Phone Store, was performed from November to December 2015. The review of freely available mobile apps for self-management of diabetes was conducted based on the criteria for promoting diabetes self-management as defined by Goyal and Cafazzo (monitoring blood glucose level and medication, nutrition, physical exercise and body weight). The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) was followed. Three independent experts in the field of healthcare-related mobile apps were included in the assessment for eligibility and testing phase. We tested and evaluated 65 apps (21 from Google Play Store, 31 from App Store and 13 from Windows Phone Store). Fifty-six of these apps did not meet even minimal requirements or did not work properly. While a wide selection of mobile applications is available for self-management of diabetes, current results show that there are only nine (5 from Google Play Store, 3 from App Store and 1 from Windows Phone Store) out of 65 reviewed mobile apps that can be versatile and useful for successful self-management of diabetes based on selection criteria. The levels of inclusion of features based on selection criteria in selected mobile apps can be very different. The results of the study can be used as a basis to prvide app developers with certain recommendations. There is a need for mobile apps for self-management of diabetes with more features in order to increase the number of long-term users and thus influence better self-management of the disease.
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Herrero P, Pesl P, Bondia J, Reddy M, Oliver N, Georgiou P, Toumazou C. Method for automatic adjustment of an insulin bolus calculator: in silico robustness evaluation under intra-day variability. Comput Methods Programs Biomed 2015; 119:1-8. [PMID: 25733405 DOI: 10.1016/j.cmpb.2015.02.003] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2014] [Revised: 01/23/2015] [Accepted: 02/04/2015] [Indexed: 06/04/2023]
Abstract
BACKGROUND AND OBJECTIVE Insulin bolus calculators are simple decision support software tools incorporated in most commercially available insulin pumps and some capillary blood glucose meters. Although their clinical benefit has been demonstrated, their utilisation has not been widespread and their performance remains suboptimal, mainly because of their lack of flexibility and adaptability. One of the difficulties that people with diabetes, clinicians and carers face when using bolus calculators is having to set parameters and adjust them on a regular basis according to changes in insulin requirements. In this work, we propose a novel method that aims to automatically adjust the parameters of a bolus calculator. Periodic usage of a continuous glucose monitoring device is required for this purpose. METHODS To test the proposed method, an in silico evaluation under real-life conditions was carried out using the FDA-accepted Type 1 diabetes mellitus (T1DM) UVa/Padova simulator. Since the T1DM simulator does not incorporate intra-subject variability and uncertainty, a set of modifications were introduced to emulate them. Ten adult and ten adolescent virtual subjects were assessed over a 3-month scenario with realistic meal variability. The glycaemic metrics: mean blood glucose; percentage time in target; percentage time in hypoglycaemia; risk index, low blood glucose index; and blood glucose standard deviation, were employed for evaluation purposes. A t-test statistical analysis was carried out to evaluate the benefit of the presented algorithm against a bolus calculator without automatic adjustment. RESULTS The proposed method statistically improved (p<0.05) all glycemic metrics evaluating hypoglycaemia on both virtual cohorts: percentage time in hypoglycaemia (i.e. BG<70 mg/dl) (adults: 2.7±4.0 vs. 0.4±0.7, p=0.03; adolescents: 7.1±7.4 vs. 1.3±2.4, p=0.02) and low blood glucose index (LBGI) (adults: 1.1±1.3 vs. 0.3±0.2, p=0.002; adolescents: 2.0±2.19 vs. 0.7±1.4, p=0.05). A statistically significant improvement was also observed on the blood glucose standard deviation (BG SD mg/dL) (adults: 33.5±13.7 vs. 29.2±8.3, p=0.01; adolescents: 63.7±22.7 vs. 44.9±23.9, p=0.01). Apart from a small increase in mean blood glucose on the adult cohort (129.9±11.9 vs. 133.9±11.6, p=0.03), the rest of the evaluated metrics, despite showing an improvement trend, did not experience a statistically significant change. CONCLUSIONS A novel method for automatically adjusting the parameters of a bolus calculator has the potential to improve glycemic control in T1DM diabetes management.
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Affiliation(s)
- Pau Herrero
- Centre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom.
| | - Peter Pesl
- Centre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom
| | - Jorge Bondia
- Institut Universitari d'Automàtica i Informàtica Industrial, Universitat Politècnica de València, València, Spain
| | - Monika Reddy
- Charing Cross Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Nick Oliver
- Charing Cross Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Pantelis Georgiou
- Centre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom
| | - Christofer Toumazou
- Centre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom
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