This paper is in the following e-collection/theme issue:

mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1399) Human Factors and Usability Case Studies (787) Food Sciences and Nutrition Engineering (54) Imaging Informatics (193) Machine Learning (1465) Instruments and Questionnaires for Nutrition and Food Intake (166) Usability of Apps and User Perceptions of mHealth (974) Apps, Mobile, Wearables for Diabetes (298) Diabetes Education and Self-Management in Childhood and Adolescence (35)

Published on in Vol 8, No 10 (2020): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/22074, first published .
Carbohydrate Counting App Using Image Recognition for Youth With Type 1 Diabetes: Pilot Randomized Control Trial

Carbohydrate Counting App Using Image Recognition for Youth With Type 1 Diabetes: Pilot Randomized Control Trial

Carbohydrate Counting App Using Image Recognition for Youth With Type 1 Diabetes: Pilot Randomized Control Trial

Authors of this article:

Jeffrey E Alfonsi1, 2 Author Orcid Image ;   Elizabeth E Y Choi2 Author Orcid Image ;   Taha Arshad3 Author Orcid Image ;   Stacie-Ann S Sammott3 Author Orcid Image ;   Vanita Pais3 Author Orcid Image ;   Cynthia Nguyen4 Author Orcid Image ;   Bryan R Maguire5 Author Orcid Image ;   Jennifer N Stinson4, 6 Author Orcid Image ;   Mark R Palmert3, 7 Author Orcid Image
Article Authors Cited by (46) Tweetations (5) Metrics

Journals

  1. Chiang Y, Chang C, Yu H, Tsay P, Lo F, Chen C, Lin W, Hsu C, An C, Moons P. Developing the “Healthcare CEO App” for patients with type 1 diabetes transitioning from adolescence to young adulthood: A mixed‐methods study. Nursing Open 2023;10(3):1755 View
  2. Chen Z, Kusuma J, Shiao S. Validating Healthy Eating Index, Glycemic Index, and Glycemic Load with Modern Diets for E-Health Era. Nutrients 2023;15(5):1263 View
  3. Moon S, Jung I, Park C. Current Advances of Artificial Pancreas Systems: A Comprehensive Review of the Clinical Evidence. Diabetes & Metabolism Journal 2021;45(6):813 View
  4. Amorim D, Miranda F, Ferreira L, Abreu C. Data-Driven Carbohydrate Counting Accuracy Monitoring: A Personalized Approach. Procedia Computer Science 2022;204:900 View
  5. Murphy J, McSharry J, Hynes L, Molloy G. A Smartphone App to Support Adherence to Inhaled Corticosteroids in Young Adults With Asthma: Multi-Methods Feasibility Study. JMIR Formative Research 2021;5(9):e28784 View
  6. Chotwanvirat P, Hnoohom N, Rojroongwasinkul N, Kriengsinyos W. Feasibility Study of an Automated Carbohydrate Estimation System Using Thai Food Images in Comparison With Estimation by Dietitians. Frontiers in Nutrition 2021;8 View
  7. Gautier T, Ziegler L, Gerber M, Campos-Náñez E, Patek S. Artificial intelligence and diabetes technology: A review. Metabolism 2021;124:154872 View
  8. Lam T, Cheung M, Munro Y, Lim K, Shung D, Sung J. Randomized Controlled Trials of Artificial Intelligence in Clinical Practice: Systematic Review. Journal of Medical Internet Research 2022;24(8):e37188 View
  9. Fu H, Wyman J, Peden-McAlpine C, Draucker C, Schleyer T, Adam T. App Design Features Important for Diabetes Self-management as Determined by the Self-Determination Theory on Motivation: Content Analysis of Survey Responses From Adults Requiring Insulin Therapy. JMIR Diabetes 2023;8:e38592 View
  10. Motaib I, Aitlahbib F, Fadil A, Z.Rhmari Tlemcani F, Elamari S, Laidi S, Chadli A. Predicting poor glycemic control during Ramadan among non-fasting patients with diabetes using artificial intelligence based machine learning models. Diabetes Research and Clinical Practice 2022;190:109982 View
  11. Chandrasekhar A, Saini D, Padhi R. An artificial pancreas system in android phones: A dual app architecture. Pervasive and Mobile Computing 2023;91:101767 View
  12. Salas-Groves E, Galyean S, Alcorn M, Childress A. Behavior Change Effectiveness Using Nutrition Apps in People With Chronic Diseases: Scoping Review. JMIR mHealth and uHealth 2023;11:e41235 View
  13. Yun J, Shin J, Lee H, Kim D, Choi I, Kim M. Characteristics and Potential Challenges of Digital-Based Interventions for Children and Young People: Scoping Review. Journal of Medical Internet Research 2023;25:e45465 View
  14. Newman C, Ero A, Dunne F. Glycaemic control and novel technology management strategies in pregestational diabetes mellitus. Frontiers in Endocrinology 2023;13 View
  15. Deniz-Garcia A, Fabelo H, Rodriguez-Almeida A, Zamora-Zamorano G, Castro-Fernandez M, Alberiche Ruano M, Solvoll T, Granja C, Schopf T, Callico G, Soguero-Ruiz C, Wägner A. Quality, Usability, and Effectiveness of mHealth Apps and the Role of Artificial Intelligence: Current Scenario and Challenges. Journal of Medical Internet Research 2023;25:e44030 View
  16. AlBabtain S, AlAfif N, AlDisi D, AlZahrani S. Manual and Application-Based Carbohydrate Counting and Glycemic Control in Type 1 Diabetes Subjects: A Narrative Review. Healthcare 2023;11(7):934 View
  17. Pi L, Shi X, Wang Z, Zhou Z. Effect of smartphone apps on glycemic control in young patients with type 1 diabetes: A meta-analysis. Frontiers in Public Health 2023;11 View
  18. Zhang M, Zhang H, Zhu R, Yang H, Chen M, Wang X, Li Z, Xiong Z. Factors affecting the willingness of patients with type 2 diabetes to use digital disease management applications: a cross-sectional study. Frontiers in Public Health 2023;11 View
  19. Clerc A. Nutrition education to type 1 diabetes patients: few changes over the time. Frontiers in Clinical Diabetes and Healthcare 2023;4 View
  20. Sze W, Kow S. Perspectives and Needs of Malaysian Patients With Diabetes for a Mobile Health App Support on Self-Management of Diabetes: Qualitative Study. JMIR Diabetes 2023;8:e40968 View
  21. Uliana G, Camara L, Paracampo C, da Costa J, Gomes D. Characteristics of carbohydrate counting practice associated with adequacy of glycated hemoglobin in adults with type 1 diabetes mellitus in Brazil. Frontiers in Endocrinology 2023;14 View
  22. Wiyono L, Ghitha N, Clarisa D, Larasati A. Carbohydrate counting implementation on pediatric type 1 diabetes mellitus: systematic review and meta-analysis. Annals of Pediatric Endocrinology & Metabolism 2023;28(3):206 View
  23. Weiss R. Closed loop insulin delivery–Opportunities and limitations. Journal of Diabetes 2023;15(12):1103 View
  24. Zrubka Z, Kertész G, Gulácsi L, Czere J, Hölgyesi Á, Nezhad H, Mosavi A, Kovács L, Butte A, Péntek M. The Reporting Quality of Machine Learning Studies on Pediatric Diabetes Mellitus: Systematic Review. Journal of Medical Internet Research 2024;26:e47430 View
  25. Elamin S, Redzuan A, Aziz S, Hamdan S, Masmuzidin M, Shah N. Impacts of Educational Interventions on Glycemic Control in Children and Adolescents with Type 1 Diabetes Mellitus. Archives of Pharmacy Practice 2023;14(4):13 View
  26. Lee D, Lee H, Cheon C, Yoon J. Healthcare coaching program for youth with type 1 diabetes in South Korea: a pilot study. Child Health Nursing Research 2024;30(1):17 View
  27. Abdul-Halim M, Baharudin N, Abdul-Hamid H, Mohamed-Yassin M, Daud M, Badlishah-Sham S, Abdul-Razak S, Ramli A. Factors associated with usability of the EMPOWER-SUSTAIN Global Cardiovascular Risks Self-Management Booklet© among individuals with metabolic syndrome in primary care: a cross-sectional study. BMC Primary Care 2024;25(1) View
  28. Khalifa M, Albadawy M. Artificial intelligence for diabetes: Enhancing prevention, diagnosis, and effective management. Computer Methods and Programs in Biomedicine Update 2024;5:100141 View
  29. Hasanah I, Sari A, Nursalam N, Safinatunnaja B, Krisnana I, Basuni H, Haikal Z, Ramdani W. Impact of Mobile and Web Health Apps on Pediatric Chronic Disease Management and Quality of Life: An Update Systematic Review. Africa Journal of Nursing and Midwifery 2024 View
  30. Tarricone R, Petracca F, Svae L, Cucciniello M, Ciani O. Which behaviour change techniques work best for diabetes self-management mobile apps? Results from a systematic review and meta-analysis of randomised controlled trials. eBioMedicine 2024;103:105091 View
  31. Iqhrammullah M, Yudhistira Refin R, Fitria Andika F, Amirah S, Fahd Abdurrahman M, Alina M, Yufika A, Abdullah A. Dropout rate in clinical trials of smartphone apps for diabetes management: A meta-analysis. Diabetes Research and Clinical Practice 2024;212:111723 View
  32. Scidà G, Corrado A, Abuqwider J, Lupoli R, Rainone C, Della Pepa G, Masulli M, Annuzzi G, Bozzetto L. Postprandial Glucose Control With Different Hybrid Closed-Loop Systems According to Type of Meal in Adults With Type 1 Diabetes. Journal of Diabetes Science and Technology 2024 View
  33. Lubasinski N, Thabit H, Nutter P, Harper S. What Is the Tech Missing? Nutrition Reporting in Type 1 Diabetes. Nutrients 2024;16(11):1690 View
  34. Liang Y, Xiao R, Huang F, Lin Q, Guo J, Zeng W, Dong J. AI nutritionist: Intelligent software as the next generation pioneer of precision nutrition. Computers in Biology and Medicine 2024;178:108711 View
  35. Wu Y, Long T, Huang J, Zhang Q, Forbes A, Li M. Delivering a Smartphone Serious Game-Based Intervention to Promote Resilience for Adolescents With Type 1 Diabetes: A Feasibility Study. Journal of Pediatric Health Care 2024;38(6):893 View
  36. Crystal A, Valero M, Nino V, Ingram K. Empowering Diabetics: Advancements in Smartphone-Based Food Classification, Volume Measurement, and Nutritional Estimation. Sensors 2024;24(13):4089 View
  37. Amorim D, Miranda F, Santos A, Graça L, Rodrigues J, Rocha M, Pereira M, Sousa C, Felgueiras P, Abreu C. Assessing Carbohydrate Counting Accuracy: Current Limitations and Future Directions. Nutrients 2024;16(14):2183 View
  38. Sheng B, Pushpanathan K, Guan Z, Lim Q, Lim Z, Yew S, Goh J, Bee Y, Sabanayagam C, Sevdalis N, Lim C, Lim C, Shaw J, Jia W, Ekinci E, Simó R, Lim L, Li H, Tham Y. Artificial intelligence for diabetes care: current and future prospects. The Lancet Diabetes & Endocrinology 2024;12(8):569 View
  39. Court R, Swallow V, El-Yousfi S, Gray-Burrows K, Sotir F, Wheeler G, Kellar I, Lee J, Mitchell R, Mlynarczyk W, Ramavath A, Dimitri P, Phillips B, Prodgers L, Pownall M, Kowalczyk M, Branchflower J, Powell L, Bhanbhro S, Weighall A, Martin-Kerry J. Children and young people’s preferences and needs when using health technology to self-manage a long-term condition: a scoping review. Archives of Disease in Childhood 2024;109(10):826 View
  40. Oei K, Choi E, Bar-Dayan A, Stinson J, Palmert M, Alfonsi J, Hamilton J. An Image-Recognition Dietary Assessment App for Adolescents with Obesity: Usability and Feasibility Testing (Pilot Randomized Controlled Trial) (Preprint). JMIR Formative Research 2024 View
  41. Abdelmalak N, Burns J, Suhlrie L, Laxy M, Stephan A. Consideration of inequalities in effectiveness trials of mHealth applications – a systematic assessment of studies from an umbrella review. International Journal for Equity in Health 2024;23(1) View
  42. Ewers B, Blond M, Bruun J, Vilsbøll T. Comparing the Effectiveness of Different Dietary Educational Approaches for Carbohydrate Counting on Glycemic Control in Adults with Type 1 Diabetes: Findings from the DIET-CARB Study, a Randomized Controlled Trial. Nutrients 2024;16(21):3745 View
  43. Chotwanvirat P, Prachansuwan A, Sridonpai P, Kriengsinyos W. Advancements in Using AI for Dietary Assessment Based on Food Images: Scoping Review. Journal of Medical Internet Research 2024;26:e51432 View

Books/Policy Documents

  1. Matsumoto A. Wearable Biosensing in Medicine and Healthcare. View
  2. Chen C, Laffel L. Textbook of Diabetes. View