Published on in Vol 18, No 5 (2016): May

Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study

Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study

Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study

Journals

  1. Ladyzynski P, Krzymien J, Foltynski P, Rachuta M, Bonalska B. Accuracy of Automatic Carbohydrate, Protein, Fat and Calorie Counting Based on Voice Descriptions of Meals in People with Type 1 Diabetes. Nutrients 2018;10(4):518 View
  2. Doupis J, Festas G, Tsilivigos C, Efthymiou V, Kokkinos A. Smartphone-Based Technology in Diabetes Management. Diabetes Therapy 2020;11(3):607 View
  3. Dehais J, Anthimopoulos M, Shevchik S, Mougiakakou S. Two-View 3D Reconstruction for Food Volume Estimation. IEEE Transactions on Multimedia 2017;19(5):1090 View
  4. Bellei E, Biduski D, Cechetti N, De Marchi A. Diabetes Mellitus m-Health Applications: A Systematic Review of Features and Fundamentals. Telemedicine and e-Health 2018;24(11):839 View
  5. Hassannejad H, Matrella G, Ciampolini P, De Munari I, Mordonini M, Cagnoni S. Automatic diet monitoring: a review of computer vision and wearable sensor-based methods. International Journal of Food Sciences and Nutrition 2017;68(6):656 View
  6. den Braber N, Vollenbroek-Hutten M, Oosterwijk M, Gant C, Hagedoorn I, van Beijnum B, Hermens H, Laverman G. Requirements of an Application to Monitor Diet, Physical Activity and Glucose Values in Patients with Type 2 Diabetes: The Diameter. Nutrients 2019;11(2):409 View
  7. Gillingham M, Li Z, Beck R, Calhoun P, Castle J, Clements M, Dassau E, Doyle F, Gal R, Jacobs P, Patton S, Rickels M, Riddell M, Martin C. Assessing Mealtime Macronutrient Content: Patient Perceptions Versus Expert Analyses via a Novel Phone App. Diabetes Technology & Therapeutics 2021;23(2):85 View
  8. Archundia Herrera M, Chan C. Narrative Review of New Methods for Assessing Food and Energy Intake. Nutrients 2018;10(8):1064 View
  9. Tay W, Kaur B, Quek R, Lim J, Henry C. Current Developments in Digital Quantitative Volume Estimation for the Optimisation of Dietary Assessment. Nutrients 2020;12(4):1167 View
  10. Topchii I. Hyperphosphatemia and karbamylation proteins — risk factors for cardiovascular disease. Shidnoevropejskij zurnal vnutrisnoi ta simejnoi medicini 2017;2017(1):39 View
  11. Vasiloglou M, Christodoulidis S, Reber E, Stathopoulou T, Lu Y, Stanga Z, Mougiakakou S. What Healthcare Professionals Think of “Nutrition & Diet” Apps: An International Survey. Nutrients 2020;12(8):2214 View
  12. Hartz J, Yingling L, Powell-Wiley T. Use of Mobile Health Technology in the Prevention and Management of Diabetes Mellitus. Current Cardiology Reports 2016;18(12) View
  13. Cappon G, Acciaroli G, Vettoretti M, Facchinetti A, Sparacino G. Wearable Continuous Glucose Monitoring Sensors: A Revolution in Diabetes Treatment. Electronics 2017;6(3):65 View
  14. Contreras I, Vehi J. Artificial Intelligence for Diabetes Management and Decision Support: Literature Review. Journal of Medical Internet Research 2018;20(5):e10775 View
  15. Khazen W, Jeanne J, Demaretz L, Schäfer F, Fagherazzi G. Rethinking the Use of Mobile Apps for Dietary Assessment in Medical Research. Journal of Medical Internet Research 2020;22(6):e15619 View
  16. Vasiloglou , Fletcher , Poulia . Challenges and Perspectives in Nutritional Counselling and Nursing: A Narrative Review. Journal of Clinical Medicine 2019;8(9):1489 View
  17. Lu Y, Stathopoulou T, Vasiloglou M, Pinault L, Kiley C, Spanakis E, Mougiakakou S. goFOODTM: An Artificial Intelligence System for Dietary Assessment. Sensors 2020;20(15):4283 View
  18. Vasiloglou M, Mougiakakou S, Aubry E, Bokelmann A, Fricker R, Gomes F, Guntermann C, Meyer A, Studerus D, Stanga Z. A Comparative Study on Carbohydrate Estimation: GoCARB vs. Dietitians. Nutrients 2018;10(6):741 View
  19. Reber E, Gomes F, Vasiloglou M, Schuetz P, Stanga Z. Nutritional Risk Screening and Assessment. Journal of Clinical Medicine 2019;8(7):1065 View
  20. Bally L, Dehais J, Nakas C, Anthimopoulos M, Laimer M, Rhyner D, Rosenberg G, Zueger T, Diem P, Mougiakakou S, Stettler C. Carbohydrate Estimation Supported by the GoCARB System in Individuals With Type 1 Diabetes: A Randomized Prospective Pilot Study. Diabetes Care 2017;40(2):e6 View
  21. Eldridge A, Piernas C, Illner A, Gibney M, Gurinović M, de Vries J, Cade J. Evaluation of New Technology-Based Tools for Dietary Intake Assessment—An ILSI Europe Dietary Intake and Exposure Task Force Evaluation. Nutrients 2018;11(1):55 View
  22. Angelini S, Alicastro G, Dionisi S, Di Muzio M. Structure and Characteristics of Diabetes Self-management Applications. CIN: Computers, Informatics, Nursing 2019;37(7):340 View
  23. Roversi C, Vettoretti M, Del Favero S, Facchinetti A, Sparacino G. Modeling Carbohydrate Counting Error in Type 1 Diabetes Management. Diabetes Technology & Therapeutics 2020;22(10):749 View
  24. Van Asbroeck S, Matthys C. Use of Different Food Image Recognition Platforms in Dietary Assessment: Comparison Study. JMIR Formative Research 2020;4(12):e15602 View
  25. Vasiloglou M, van der Horst K, Stathopoulou T, Jaeggi M, Tedde G, Lu Y, Mougiakakou S. The Human Factor in Automated Image-Based Nutrition Apps: Analysis of Common Mistakes Using the goFOOD Lite App. JMIR mHealth and uHealth 2021;9(1):e24467 View
  26. Alfonsi J, Choi E, Arshad T, Sammott S, Pais V, Nguyen C, Maguire B, Stinson J, Palmert M. Carbohydrate Counting App Using Image Recognition for Youth With Type 1 Diabetes: Pilot Randomized Control Trial. JMIR mHealth and uHealth 2020;8(10):e22074 View
  27. Höchsmann C, Martin C. Review of the validity and feasibility of image-assisted methods for dietary assessment. International Journal of Obesity 2020;44(12):2358 View
  28. Herzig D, Nakas C, Stalder J, Kosinski C, Laesser C, Dehais J, Jaeggi R, Leichtle A, Dahlweid F, Stettler C, Bally L. Volumetric Food Quantification Using Computer Vision on a Depth-Sensing Smartphone: Preclinical Study. JMIR mHealth and uHealth 2020;8(3):e15294 View
  29. Roux de Bézieux H, Bullard J, Kolterman O, Souza M, Perraudeau F. Medical Food Assessment Using a Smartphone App With Continuous Glucose Monitoring Sensors: Proof-of-Concept Study. JMIR Formative Research 2021;5(3):e20175 View
  30. Lu Y, Stathopoulou T, Vasiloglou M, Christodoulidis S, Stanga Z, Mougiakakou S. An Artificial Intelligence-Based System to Assess Nutrient Intake for Hospitalised Patients. IEEE Transactions on Multimedia 2021;23:1136 View
  31. Roversi C, Vettoretti M, Del Favero S, Facchinetti A, Choudhary P, Sparacino G. Impact of Carbohydrate Counting Error on Glycemic Control in Open-Loop Management of Type 1 Diabetes: Quantitative Assessment Through an In Silico Trial. Journal of Diabetes Science and Technology 2022;16(6):1541 View
  32. Joubert M, Meyer L, Doriot A, Dreves B, Jeandidier N, Reznik Y. Prospective Independent Evaluation of the Carbohydrate Counting Accuracy of Two Smartphone Applications. Diabetes Therapy 2021;12(7):1809 View
  33. Buck S, Krauss C, Waldenmaier D, Liebing C, Jendrike N, Högel J, Pfeiffer B, Haug C, Freckmann G. Evaluation of Meal Carbohydrate Counting Errors in Patients with Type 1 Diabetes. Experimental and Clinical Endocrinology & Diabetes 2022;130(07):475 View
  34. Tahir G, Loo C. A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment. Healthcare 2021;9(12):1676 View
  35. Amugongo L, Kriebitz A, Boch A, Lütge C. Mobile Computer Vision-Based Applications for Food Recognition and Volume and Calorific Estimation: A Systematic Review. Healthcare 2022;11(1):59 View
  36. Thornton L, Osman B, Champion K, Green O, Wescott A, Gardner L, Stewart C, Visontay R, Whife J, Parmenter B, Birrell L, Bryant Z, Chapman C, Lubans D, Slade T, Torous J, Teesson M, Van de Ven P. Measurement Properties of Smartphone Approaches to Assess Diet, Alcohol Use, and Tobacco Use: Systematic Review. JMIR mHealth and uHealth 2022;10(2):e27337 View
  37. F. de Carvalho D, Kaymak U, Van Gorp P, van Riel N. A Markov model for inferring event types on diabetes patients data. Healthcare Analytics 2022;2:100024 View
  38. Vasiloglou M, Marcano I, Lizama S, Papathanail I, Spanakis E, Mougiakakou S. Multimedia Data-Based Mobile Applications for Dietary Assessment. Journal of Diabetes Science and Technology 2023;17(4):1056 View
  39. 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
  40. Sullivan V, Rebholz C. Nutritional Epidemiology and Dietary Assessment for Patients With Kidney Disease: A Primer. American Journal of Kidney Diseases 2023;81(6):717 View
  41. Zuppinger C, Taffé P, Burger G, Badran-Amstutz W, Niemi T, Cornuz C, Belle F, Chatelan A, Paclet Lafaille M, Bochud M, Gonseth Nusslé S. Performance of the Digital Dietary Assessment Tool MyFoodRepo. Nutrients 2022;14(3):635 View
  42. Huang H, You S, Di Tizio L, Li C, Raftery E, Ehmke C, Steiger C, Li J, Wentworth A, Ballinger I, Gwynne D, Nan K, Liang J, Li J, Byrne J, Collins J, Tamang S, Ishida K, Halperin F, Traverso G. An automated all-in-one system for carbohydrate tracking, glucose monitoring, and insulin delivery. Journal of Controlled Release 2022;343:31 View
  43. Legay C, Krasniqi T, Bourdet A, Bonny O, Bochud M. Methods for the dietary assessment of adult kidney stone formers: a scoping review. Journal of Nephrology 2022;35(3):821 View
  44. Lee H, Ahn J, Lee J. Development and Validation of a Questionnaire on the Feasibility of a Mobile Dietary Self-Monitoring Application. Korean Journal of Community Nutrition 2022;27(2):146 View
  45. Sasaki Y, Sato K, Kobayashi S, Asakura K. Nutrient and Food Group Prediction as Orchestrated by an Automated Image Recognition System in a Smartphone App (CALO mama): Validation Study. JMIR Formative Research 2022;6(1):e31875 View
  46. Schönenberger K, Cossu L, Prendin F, Cappon G, Wu J, Fuchs K, Mayer S, Herzig D, Facchinetti A, Bally L. Digital Solutions to Diagnose and Manage Postbariatric Hypoglycemia. Frontiers in Nutrition 2022;9 View
  47. Bzikowska-Jura A, Sobieraj P, Raciborski F. Low Comparability of Nutrition-Related Mobile Apps against the Polish Reference Method—A Validity Study. Nutrients 2021;13(8):2868 View
  48. C Braga B, Nguyen P, Aberman N, Doyle F, Folson G, Hoang N, Huynh P, Koch B, McCloskey P, Tran L, Hughes D, Gelli A. Exploring an Artificial Intelligence–Based, Gamified Phone App Prototype to Track and Improve Food Choices of Adolescent Girls in Vietnam: Acceptability, Usability, and Likeability Study. JMIR Formative Research 2022;6(7):e35197 View
  49. Harmon S, Heitkemper E, Mamykina L, Hwang M. Are We Healthier Together? Two Strategies for Supporting Macronutrient Assessment Skills and How the Crowd Can Help (or Not). Proceedings of the ACM on Human-Computer Interaction 2022;6(CSCW2):1 View
  50. Dalakleidi K, Papadelli M, Kapolos I, Papadimitriou K. Applying Image-Based Food-Recognition Systems on Dietary Assessment: A Systematic Review. Advances in Nutrition 2022;13(6):2590 View
  51. Sultana J, Ahmed B, Masud M, Huq A, Ali M, Naznin M. A Study on Food Value Estimation From Images: Taxonomies, Datasets, and Techniques. IEEE Access 2023;11:45910 View
  52. Chen X, Kamavuako E. Vision-Based Methods for Food and Fluid Intake Monitoring: A Literature Review. Sensors 2023;23(13):6137 View
  53. Mansouri M, Benabdellah Chaouni S, Jai Andaloussi S, Ouchetto O. Deep Learning for Food Image Recognition and Nutrition Analysis Towards Chronic Diseases Monitoring: A Systematic Review. SN Computer Science 2023;4(5) View
  54. Bul K, Holliday N, Bhuiyan M, Clark C, Allen J, Wark P. Usability and Preliminary Efficacy of an Artificial Intelligence–Driven Platform Supporting Dietary Management in Diabetes: Mixed Methods Study. JMIR Human Factors 2023;10:e43959 View
  55. Shonkoff E, Cara K, Pei X, Chung M, Kamath S, Panetta K, Hennessy E. AI-based digital image dietary assessment methods compared to humans and ground truth: a systematic review. Annals of Medicine 2023;55(2) View
  56. Choi J, Ma D, Wolfson J, Wyman J, Adam T, Fu H. Associations Between Psychosocial Needs, Carbohydrate-Counting Behavior, and App Satisfaction: A Randomized Crossover App Trial on 92 Adults With Diabetes. CIN: Computers, Informatics, Nursing 2023;41(12):1026 View
  57. Tosun F, Teixeira A, Abdalmoaty M, Ahlén A, Dey S. Quickest detection of bias injection attacks on the glucose sensor in the artificial pancreas under meal disturbances. Journal of Process Control 2024;135:103162 View
  58. Schenk J, Boynton A, Kulik P, Zyuzin A, Neuhouser M, Kristal A. The Use of Three-Dimensional Images and Food Descriptions from a Smartphone Device Is Feasible and Accurate for Dietary Assessment. Nutrients 2024;16(6):828 View

Books/Policy Documents

  1. Kowatsch T, Fischer-Taeschler D, Putzing F, Bürki P, Stettler C, Chiesa-Tanner G, Fleisch E. Digitale Transformation von Dienstleistungen im Gesundheitswesen VI. View
  2. Allegra D, Erba D, Farinella G, Grazioso G, Maci P, Stanco F, Tomaselli V. Image Analysis and Processing – ICIAP 2019. View
  3. Allegra D, Anthimopoulos M, Dehais J, Lu Y, Stanco F, Farinella G, Mougiakakou S. New Trends in Image Analysis and Processing – ICIAP 2017. View
  4. Contreras I, Bertachi A, Biagi L, Oviedo S, Ramkissoon C, Vehi J. Artificial Intelligence in Precision Health. View
  5. Gambo I, Massenon R, Yange T, Ikono R, Omodunbi T, Babatope K. Health Informatics: A Computational Perspective in Healthcare. View
  6. Mansour A, Amroun K, Habbas Z. Advances in Multidisciplinary Medical Technologies ─ Engineering, Modeling and Findings. View
  7. Dhanapal A, Sylvia Subapriya M. , Subramaniam K, Appukutty M. Integrating AI in IoT Analytics on the Cloud for Healthcare Applications. View
  8. Panagiotou M, Papathanail I, Abdur Rahman L, Brigato L, Bez N, Vasiloglou M, Stathopoulou T, de Galan B, Pedersen-Bjergaard U, van der Horst K, Mougiakakou S. Computer Analysis of Images and Patterns. View