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Baumgartner M, Kuhn C, Nakas CT, Herzig D, Bally L. Carbohydrate Estimation Accuracy of Two Commercially Available Smartphone Applications vs Estimation by Individuals With Type 1 Diabetes: A Comparative Study. J Diabetes Sci Technol 2024:19322968241264744. [PMID: 39058316 DOI: 10.1177/19322968241264744] [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: 07/28/2024]
Abstract
BACKGROUND Despite remarkable progress in diabetes technology, most systems still require estimating meal carbohydrate (CHO) content for meal-time insulin delivery. Emerging smartphone applications may obviate this need, but performance data in relation to patient estimates remain scarce. OBJECTIVE The objective is to assess the accuracy of two commercial CHO estimation applications, SNAQ and Calorie Mama, and compare their performance with the estimation accuracy of people with type 1 diabetes (T1D). METHODS Carbohydrate estimates of 53 individuals with T1D (aged ≥16 years) were compared with those of SNAQ (food recognition + quantification) and Calorie Mama (food recognition + adjustable standard portion size). Twenty-six cooked meals were prepared at the hospital kitchen. Each participant estimated the CHO content of two meals in three different sizes without assistance. Participants then used SNAQ for CHO quantification in one meal and Calorie Mama for the other (all three sizes). Accuracy was the estimate's deviation from ground-truth CHO content (weight multiplied by nutritional facts from recipe database). Furthermore, the applications were rated using the Mars-G questionnaire. RESULTS Participants' mean ± standard deviation (SD) absolute error was 21 ± 21.5 g (71 ± 72.7%). Calorie Mama had a mean absolute error of 24 ± 36.5 g (81.2 ± 123.4%). With a mean absolute error of 13.1 ± 11.3 g (44.3 ± 38.2%), SNAQ outperformed the estimation accuracy of patients and Calorie Mama (both P > .05). Error consistency (quantified by the within-participant SD) did not significantly differ between the methods. CONCLUSIONS SNAQ may provide effective CHO estimation support for people with T1D, particularly those with large or inconsistent CHO estimation errors. Its impact on glucose control remains to be evaluated.
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Affiliation(s)
- Michelle Baumgartner
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Department of Health Sciences and Technology, Eidgenössische Technische Hochschule Zurich, Zurich, Switzerland
| | - Christian Kuhn
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Christos T Nakas
- School of Agricultural Sciences, Laboratory of Biometry, University of Thessaly, Volos, Greece
- University Institute of Clinical Chemistry, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - David Herzig
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Lia Bally
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
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Amorim D, Miranda F, Santos A, Graça L, Rodrigues J, Rocha M, Pereira MA, Sousa C, Felgueiras P, Abreu C. Assessing Carbohydrate Counting Accuracy: Current Limitations and Future Directions. Nutrients 2024; 16:2183. [PMID: 39064626 PMCID: PMC11279647 DOI: 10.3390/nu16142183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2024] [Revised: 06/25/2024] [Accepted: 07/05/2024] [Indexed: 07/28/2024] Open
Abstract
Diabetes mellitus is a prevalent chronic autoimmune disease with a high impact on global health, affecting millions of adults and resulting in significant morbidity and mortality. Achieving optimal blood glucose levels is crucial for diabetes management to prevent acute and long-term complications. Carbohydrate counting (CC) is widely used by patients with type 1 diabetes to adjust prandial insulin bolus doses based on estimated carbohydrate content, contributing to better glycemic control and improved quality of life. However, accurately estimating the carbohydrate content of meals remains challenging for patients, leading to errors in bolus insulin dosing. This review explores the current limitations and challenges in CC accuracy and emphasizes the importance of personalized educational programs to enhance patients' abilities in carbohydrate estimation. Existing tools for assessing patient learning outcomes in CC are discussed, highlighting the need for individualized approaches tailored to each patient's needs. A comprehensive review of the relevant literature was conducted to identify educational programs and assessment tools dedicated to training diabetes patients on carbohydrate counting. The research aims to provide insights into the benefits and limitations of existing tools and identifies future research directions to advance personalized CC training approaches. By adopting a personalized approach to CC education and assessment, healthcare professionals can empower patients to achieve better glycemic control and improve diabetes management. Moreover, this review identifies potential avenues for future research, paving the way for advancements in personalized CC training and assessment approaches and further enhancing diabetes management strategies.
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Affiliation(s)
- Débora Amorim
- Applied Digital Transformation Laboratory (Adit-LAB), Polytechnic Institute of Viana do Castelo, Rua Escola Industrial e Comercial de Nun’Álvares, 4900-347 Viana do Castelo, Portugal;
| | - Francisco Miranda
- Polytechnic Institute of Viana do Castelo, Rua Escola Industrial e Comercial de Nun’Álvares, 4900-347 Viana do Castelo, Portugal;
- Center for Research and Development in Mathematics and Applications (CIDMA), Department of Mathematics, University of Aveiro, 3810-193 Aveiro, Portugal
- proMetheus, Polytechnic Institute of Viana do Castelo, Rua Escola Industrial e Comercial de Nun’Álvares, 4900-347 Viana do Castelo, Portugal
| | - Andreia Santos
- School of Health of the Polytechnic Institute of Viana do Castelo, Rua Escola Industrial e Comercial de Nun’Alvares, 4900-347 Viana do Castelo, Portugal; (A.S.); (P.F.)
| | - Luís Graça
- Health Sciences Research Unit: Nursing (UICISA: E), School of Health of the Polytechnic Institute of Viana do Castelo, Rua Escola Industrial e Comercial de Nun’Alvares, 4900-347 Viana do Castelo, Portugal; (L.G.); (M.R.); (M.A.P.); (C.S.)
| | - João Rodrigues
- Center for Translational Health and Medical Biotechnology Research (TBIO)/Health Research Network (RISE-Health), School of Health of the Polytechnic Institute of Porto, Rua Dr. António Bernardino de Almeida 400, 4200-072 Porto, Portugal;
| | - Mara Rocha
- Health Sciences Research Unit: Nursing (UICISA: E), School of Health of the Polytechnic Institute of Viana do Castelo, Rua Escola Industrial e Comercial de Nun’Alvares, 4900-347 Viana do Castelo, Portugal; (L.G.); (M.R.); (M.A.P.); (C.S.)
| | - Maria Aurora Pereira
- Health Sciences Research Unit: Nursing (UICISA: E), School of Health of the Polytechnic Institute of Viana do Castelo, Rua Escola Industrial e Comercial de Nun’Alvares, 4900-347 Viana do Castelo, Portugal; (L.G.); (M.R.); (M.A.P.); (C.S.)
| | - Clementina Sousa
- Health Sciences Research Unit: Nursing (UICISA: E), School of Health of the Polytechnic Institute of Viana do Castelo, Rua Escola Industrial e Comercial de Nun’Alvares, 4900-347 Viana do Castelo, Portugal; (L.G.); (M.R.); (M.A.P.); (C.S.)
| | - Paula Felgueiras
- School of Health of the Polytechnic Institute of Viana do Castelo, Rua Escola Industrial e Comercial de Nun’Alvares, 4900-347 Viana do Castelo, Portugal; (A.S.); (P.F.)
| | - Carlos Abreu
- Applied Digital Transformation Laboratory (Adit-LAB), Polytechnic Institute of Viana do Castelo, Rua Escola Industrial e Comercial de Nun’Álvares, 4900-347 Viana do Castelo, Portugal;
- Polytechnic Institute of Viana do Castelo, Rua Escola Industrial e Comercial de Nun’Álvares, 4900-347 Viana do Castelo, Portugal;
- Center for MicroElectroMechanical Systems (CMEMS-UMINHO), University of Minho, Campus Azurém, 4800-058 Guimarães, Portugal
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ElSayed NA, Aleppo G, Bannuru RR, Beverly EA, Bruemmer D, Collins BS, Darville A, Ekhlaspour L, Hassanein M, Hilliard ME, Johnson EL, Khunti K, Lingvay I, Matfin G, McCoy RG, Perry ML, Pilla SJ, Polsky S, Prahalad P, Pratley RE, Segal AR, Seley JJ, Stanton RC, Gabbay RA. 5. Facilitating Positive Health Behaviors and Well-being to Improve Health Outcomes: Standards of Care in Diabetes-2024. Diabetes Care 2024; 47:S77-S110. [PMID: 38078584 PMCID: PMC10725816 DOI: 10.2337/dc24-s005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, an interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.
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Choi JS, Ma D, Wolfson JA, Wyman JF, Adam TJ, Fu HN. Associations Between Psychosocial Needs, Carbohydrate-Counting Behavior, and App Satisfaction: A Randomized Crossover App Trial on 92 Adults With Diabetes. Comput Inform Nurs 2023; 41:1026-1036. [PMID: 38062548 PMCID: PMC10746294 DOI: 10.1097/cin.0000000000001073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
To examine whether psychosocial needs in diabetes care are associated with carbohydrate counting and if carbohydrate counting is associated with satisfaction with diabetes applications' usability, a randomized crossover trial of 92 adults with type 1 or 2 diabetes requiring insulin therapy tested two top-rated diabetes applications, mySugr and OnTrack Diabetes. Survey responses on demographics, psychosocial needs (perceived competence, autonomy, and connectivity), carbohydrate-counting frequency, and application satisfaction were modeled using mixed-effect linear regressions to test associations. Participants ranged between 19 and 74 years old (mean, 54 years) and predominantly had type 2 diabetes (70%). Among the three tested domains of psychosocial needs, only competence-not autonomy or connectivity-was found to be associated with carbohydrate-counting frequency. No association between carbohydrate-counting behavior and application satisfaction was found. In conclusion, perceived competence in diabetes care is an important factor in carbohydrate counting; clinicians may improve adherence to carbohydrate counting with strategies designed to improve perceived competence. Carbohydrate-counting behavior is complex; its impact on patient satisfaction of diabetes application usability is multifactorial and warrants consideration of patient demographics such as sex as well as application features for automated carbohydrate counting.
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Affiliation(s)
- Joshua S. Choi
- Center for Biomedical Informatics, Regenstrief Institute, Inc., Indianapolis, IN, United States
- School of Medicine, Indiana University, Indianapolis, IN, United States
| | - Darren Ma
- Minnetonka High School, Minnetonka, MN, United States
| | - Julian A. Wolfson
- School of Public Health, University of Minnesota, Minneapolis, MN, United States
| | - Jean F. Wyman
- School of Nursing, University of Minnesota, Minneapolis, MN, United States
| | - Terrence J. Adam
- College of Pharmacy, University of Minnesota, Minneapolis, MN, United States
- Institute for Health Informatics, University of Minnesota, Minneapolis MN, United States
| | - Helen N. Fu
- Center for Biomedical Informatics, Regenstrief Institute, Inc., Indianapolis, IN, United States
- Richard M. Fairbank School of Public Health, Indiana University, Indianapolis, MN, United States
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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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Newman C, Ero A, Dunne FP. Glycaemic control and novel technology management strategies in pregestational diabetes mellitus. Front Endocrinol (Lausanne) 2023; 13:1109825. [PMID: 36714590 PMCID: PMC9877346 DOI: 10.3389/fendo.2022.1109825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 12/21/2022] [Indexed: 01/15/2023] Open
Abstract
Introduction Pregestational diabetes (PGDM) is an increasingly common and complex condition that infers risk to both mother and infant. To prevent serious morbidity, strict glycaemic control is essential. The aim of this review is to review the glucose sensing and insulin delivering technologies currently available for women with PGDM. Methods We reviewed online databases for articles relating to technology use in pregnancy using a combination of keywords and MeSH headings. Relevant articles are included below. Results A number of technological advancements have improved care and outcomes for women with PGDM. Real time continuous glucose monitoring (rtCGM) offers clear advantages in terms of infants size and neonatal intensive care unit admissions; and further benefits are seen when combined with continuous subcutaneous insulin delivery (insulin pump) and algorithms which continuously adjust insulin levels to glucose targets (hybrid closed loop). Other advancements including flash or intermittent scanning CGM (isCGM) and stand-alone insulin pumps do not confer as many advantages for women and their infants, however they are increasingly used outside of pregnancy and many women enter pregnancy already using these devices. Discussion This article offers a discussion of the most commonly used technologies in pregnancy and evaluates their current and future roles.
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Affiliation(s)
- Christine Newman
- School of Medicine, College of Medicine, Nursing and Health Science, University of Galway, Galway, Ireland
- Department of Diabetes and Endocrinology, Galway University Hospital, Galway, Ireland
- Diabetes Collaborative Clinical Trials Network, University of Galway, Galway, Ireland
| | - Adesuwa Ero
- Department of Diabetes and Endocrinology, Galway University Hospital, Galway, Ireland
| | - Fidelma P. Dunne
- School of Medicine, College of Medicine, Nursing and Health Science, University of Galway, Galway, Ireland
- Department of Diabetes and Endocrinology, Galway University Hospital, Galway, Ireland
- Diabetes Collaborative Clinical Trials Network, University of Galway, Galway, Ireland
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Nutrition Intervention in Cardiac Rehabilitation: A REVIEW OF THE LITERATURE AND STRATEGIES FOR THE FUTURE. J Cardiopulm Rehabil Prev 2021; 41:383-388. [PMID: 34727557 DOI: 10.1097/hcr.0000000000000660] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
OBJECTIVE Despite guideline consensus that quality of nutrition affects most modifiable cardiovascular disease risk factors, the implementation of dietary interventions varies considerably in cardiac rehabilitation (CR) programs. The purpose of this review is to highlight the current existing literature and provide recommendations on best practices for nutrition interventions and future research that support secondary prevention outcomes. REVIEW METHODS The review examines original investigations, systematic reviews, and guidelines regarding nutrition intervention in CR. SUMMARY Nutrition intervention in CR plays an integral role in the success of patients; however, the literature is limited and standardization of practice is in its infancy. The role of a qualified registered dietician nutritionist, standardization of dietary assessments, individualized and intensive nutrition interventions, and application of specific behavior change techniques are central components in improving diet in CR. This review provides an overview of the evidence-based cardioprotective diets, nutritional interventions and behavioral strategies in CR, and explores areas for best practices and opportunities for innovation in the delivery of nutrition intervention in CR.
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