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Citing this Article

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Published on 11.05.16 in Vol 18, No 5 (2016): May

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

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

Daniel Rhyner, Hannah Loher, Joachim Dehais, Marios Anthimopoulos, Sergey Shevchik, Ransford Henry Botwey, David Duke, Christoph Stettler, Peter Diem, Stavroula Mougiakakou

J Med Internet Res 2016 (May 11); 18(5):e101 

According to Crossref, the following articles are citing this article (DOI 10.2196/jmir.5567):

(note that this is only a small subset of citations)

  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
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  2. Doupis J, Festas G, Tsilivigos C, Efthymiou V, Kokkinos A. Smartphone-Based Technology in Diabetes Management. Diabetes Therapy 2020;11(3):607
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  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
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  4. Bellei EA, Biduski D, Cechetti NP, De Marchi ACB. Diabetes Mellitus m-Health Applications: A Systematic Review of Features and Fundamentals. Telemedicine and e-Health 2018;24(11):839
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  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
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  7. 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 Technology & Therapeutics 2021;23(2):85
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  8. Archundia Herrera M, Chan C. Narrative Review of New Methods for Assessing Food and Energy Intake. Nutrients 2018;10(8):1064
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  10. . Hyperphosphatemia and karbamylation proteins — risk factors for cardiovascular disease. Shidnoevropejskij zurnal vnutrisnoi ta simejnoi medicini 2017;2017(1):39
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  11. Vasiloglou MF, 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
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  12. Hartz J, Yingling L, Powell-Wiley TM. Use of Mobile Health Technology in the Prevention and Management of Diabetes Mellitus. Current Cardiology Reports 2016;18(12)
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  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
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  14. Contreras I, Vehi J. Artificial Intelligence for Diabetes Management and Decision Support: Literature Review. Journal of Medical Internet Research 2018;20(5):e10775
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  16. Vasiloglou , Fletcher , Poulia . Challenges and Perspectives in Nutritional Counselling and Nursing: A Narrative Review. Journal of Clinical Medicine 2019;8(9):1489
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  17. Lu Y, Stathopoulou T, Vasiloglou MF, Pinault LF, Kiley C, Spanakis EK, Mougiakakou S. goFOODTM: An Artificial Intelligence System for Dietary Assessment. Sensors 2020;20(15):4283
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  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
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  19. Reber E, Gomes F, Vasiloglou MF, Schuetz P, Stanga Z. Nutritional Risk Screening and Assessment. Journal of Clinical Medicine 2019;8(7):1065
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  20. Bally L, Dehais J, Nakas CT, 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
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  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
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  22. Angelini S, Alicastro GM, Dionisi S, Di Muzio M. Structure and Characteristics of Diabetes Self-management Applications. CIN: Computers, Informatics, Nursing 2019;37(7):340
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  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
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  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
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  25. Vasiloglou MF, van der Horst K, Stathopoulou T, Jaeggi MP, Tedde GS, 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
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  26. Alfonsi JE, Choi EEY, Arshad T, Sammott SS, Pais V, Nguyen C, Maguire BR, Stinson JN, Palmert MR. Carbohydrate Counting App Using Image Recognition for Youth With Type 1 Diabetes: Pilot Randomized Control Trial. JMIR mHealth and uHealth 2020;8(10):e22074
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  27. Höchsmann C, Martin CK. Review of the validity and feasibility of image-assisted methods for dietary assessment. International Journal of Obesity 2020;44(12):2358
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  28. Herzig D, Nakas CT, Stalder J, Kosinski C, Laesser C, Dehais J, Jaeggi R, Leichtle AB, 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
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  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
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  30. Lu Y, Stathopoulou T, Vasiloglou MF, 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
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  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
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  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
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  33. Buck S, Krauss C, Waldenmaier D, Liebing C, Jendrike N, Högel J, Pfeiffer BM, 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
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  34. Tahir GA, Loo CK. A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment. Healthcare 2021;9(12):1676
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  35. Amugongo LM, 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
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  36. Thornton L, Osman B, Champion K, Green O, Wescott AB, Gardner LA, 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
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  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
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  38. Vasiloglou MF, Marcano I, Lizama S, Papathanail I, Spanakis EK, Mougiakakou S. Multimedia Data-Based Mobile Applications for Dietary Assessment. Journal of Diabetes Science and Technology 2023;17(4):1056
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  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
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  40. Sullivan VK, Rebholz CM. Nutritional Epidemiology and Dietary Assessment for Patients With Kidney Disease: A Primer. American Journal of Kidney Diseases 2023;81(6):717
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  41. Zuppinger C, Taffé P, Burger G, Badran-Amstutz W, Niemi T, Cornuz C, Belle FN, Chatelan A, Paclet Lafaille M, Bochud M, Gonseth Nusslé S. Performance of the Digital Dietary Assessment Tool MyFoodRepo. Nutrients 2022;14(3):635
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  42. Huang H, 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. Journal of Controlled Release 2022;343:31
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  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
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  44. Lee H, Ahn JS, Lee JE. 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
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  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
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  46. Schönenberger KA, Cossu L, Prendin F, Cappon G, Wu J, Fuchs KL, Mayer S, Herzig D, Facchinetti A, Bally L. Digital Solutions to Diagnose and Manage Postbariatric Hypoglycemia. Frontiers in Nutrition 2022;9
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  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
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  48. C Braga B, Nguyen PH, 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
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  49. Harmon SM, Heitkemper EM, Mamykina L, Hwang ML. 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
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  50. Dalakleidi KV, 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
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  51. Sultana J, Ahmed BM, Masud MM, Huq AKO, Ali ME, Naznin M. A Study on Food Value Estimation From Images: Taxonomies, Datasets, and Techniques. IEEE Access 2023;11:45910
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  52. Chen X, Kamavuako EN. Vision-Based Methods for Food and Fluid Intake Monitoring: A Literature Review. Sensors 2023;23(13):6137
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  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)
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  54. Bul K, Holliday N, Bhuiyan MRA, Clark CCT, Allen J, Wark PA. Usability and Preliminary Efficacy of an Artificial Intelligence–Driven Platform Supporting Dietary Management in Diabetes: Mixed Methods Study. JMIR Human Factors 2023;10:e43959
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  55. Shonkoff E, Cara KC, 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)
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  56. 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. CIN: Computers, Informatics, Nursing 2023;41(12):1026
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  57. Tosun FE, Teixeira AM, Abdalmoaty MR, 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
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  58. Schenk JM, Boynton A, Kulik P, Zyuzin A, Neuhouser ML, Kristal AR. 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
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According to Crossref, the following books are citing this article (DOI 10.2196/jmir.5567):

  1. Kowatsch T, Fischer-Taeschler D, Putzing F, Bürki P, Stettler C, Chiesa-Tanner G, Fleisch E. Digitale Transformation von Dienstleistungen im Gesundheitswesen VI. 2019. Chapter 12:205
    CrossRef
  2. Allegra D, Erba D, Farinella GM, Grazioso G, Maci PD, Stanco F, Tomaselli V. Image Analysis and Processing – ICIAP 2019. 2019. Chapter 57:629
    CrossRef
  3. Allegra D, Anthimopoulos M, Dehais J, Lu Y, Stanco F, Farinella GM, Mougiakakou S. New Trends in Image Analysis and Processing – ICIAP 2017. 2017. Chapter 46:471
    CrossRef
  4. Contreras I, Bertachi A, Biagi L, Oviedo S, Ramkissoon C, Vehi J. Artificial Intelligence in Precision Health. 2020. :329
    CrossRef
  5. Gambo I, Massenon R, Yange TS, Ikono R, Omodunbi T, Babatope K. Health Informatics: A Computational Perspective in Healthcare. 2021. Chapter 7:107
    CrossRef
  6. Mansour A, Amroun K, Habbas Z. Advances in Multidisciplinary Medical Technologies ─ Engineering, Modeling and Findings. 2021. Chapter 5:55
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  7. Dhanapal ACTA, Sylvia Subapriya M. , Subramaniam K, Appukutty M. Integrating AI in IoT Analytics on the Cloud for Healthcare Applications. 2022. chapter 1:1
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  8. Panagiotou M, Papathanail I, Abdur Rahman L, Brigato L, Bez NS, Vasiloglou MF, Stathopoulou T, de Galan BE, Pedersen-Bjergaard U, van der Horst K, Mougiakakou S. Computer Analysis of Images and Patterns. 2023. Chapter 8:77
    CrossRef