Published on in Vol 8, No 9 (2020): September

Preprints (earlier versions) of this paper are available at, first published .
Validity and Usability of a Smartphone Image-Based Dietary Assessment App Compared to 3-Day Food Diaries in Assessing Dietary Intake Among Canadian Adults: Randomized Controlled Trial

Validity and Usability of a Smartphone Image-Based Dietary Assessment App Compared to 3-Day Food Diaries in Assessing Dietary Intake Among Canadian Adults: Randomized Controlled Trial

Validity and Usability of a Smartphone Image-Based Dietary Assessment App Compared to 3-Day Food Diaries in Assessing Dietary Intake Among Canadian Adults: Randomized Controlled Trial


  1. Ho D, Chiu W, Lee Y, Su H, Chang C, Yao C, Hua K, Chu H, Hsu C, Chang J. Integration of an Image-Based Dietary Assessment Paradigm into Dietetic Training Improves Food Portion Estimates by Future Dietitians. Nutrients 2021;13(1):175 View
  2. Bouzo V, Plourde H, Beckenstein H, Cohen T, Cahill N. Evaluation of the Diet Tracking Smartphone Application Keenoa™: A Qualitative Analysis. Canadian Journal of Dietetic Practice and Research 2022;83(1):25 View
  3. Scott J, Vijayakumar A, Woodside J, Neville C. Feasibility of wearable camera use to improve the accuracy of dietary assessment among adults. Journal of Nutritional Science 2022;11 View
  4. Lucassen D, Brouwer-Brolsma E, Slotegraaf A, Kok E, Feskens E. DIetary ASSessment (DIASS) Study: Design of an Evaluation Study to Assess Validity, Usability and Perceived Burden of an Innovative Dietary Assessment Methodology. Nutrients 2022;14(6):1156 View
  5. Baum Martinez I, Peters B, Schwarz J, Schuppelius B, Steckhan N, Koppold-Liebscher D, Michalsen A, Pivovarova-Ramich O. Validation of a Smartphone Application for the Assessment of Dietary Compliance in an Intermittent Fasting Trial. Nutrients 2022;14(18):3697 View
  6. 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
  7. Ulker I, Ayyildiz F. Artificial Intelligence Applications in Nutrition and Dietetics. Journal of Intelligent Systems with Applications 2021:125 View
  8. Moyen A, Rappaport A, Fleurent-Grégoire C, Tessier A, Brazeau A, Chevalier S. Relative Validation of an Artificial Intelligence–Enhanced, Image-Assisted Mobile App for Dietary Assessment in Adults: Randomized Crossover Study. Journal of Medical Internet Research 2022;24(11):e40449 View
  9. 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
  10. Côté M, Lamarche B. Artificial intelligence in nutrition research: perspectives on current and future applications. Applied Physiology, Nutrition, and Metabolism 2022;47(1):1 View
  11. Saronga N, Mosha I, Stewart S, Bakar S, Sunguya B, Burrows T, Leyna G, Adam M, Collins C, Rollo M. A Mixed-Method Study Exploring Experiences and Perceptions of Nutritionists Regarding Use of an Image-Based Dietary Assessment System in Tanzania. Nutrients 2022;14(3):417 View
  12. Hattab S, Badrasawi M, Anabtawi O, Zidan S. Development and validation of a smartphone image-based app for dietary intake assessment among Palestinian undergraduates. Scientific Reports 2022;12(1) View
  13. Tricás-Vidal H, Vidal-Peracho M, Lucha-López M, Hidalgo-García C, Monti-Ballano S, Márquez-Gonzalvo S, Tricás-Moreno J. Association between Body Mass Index and the Use of Digital Platforms to Record Food Intake: Cross-Sectional Analysis. Applied Sciences 2022;12(23):12144 View
  14. Taylor J, Allman-Farinelli M, Chen J, Gauglitz J, Hamideh D, Jankowska M, Johnson A, Rangan A, Spruijt-Metz D, Yang J, Hekler E. Perspective: A Framework for Addressing Dynamic Food Consumption Processes. Advances in Nutrition 2022;13(4):992 View
  15. Susanto A, Winarto H, Fahira A, Abdurrohman H, Muharram A, Widitha U, Warman Efirianti G, Eduard George Y, Tjoa K. Building an artificial intelligence-powered medical image recognition smartphone application: What medical practitioners need to know. Informatics in Medicine Unlocked 2022;32:101017 View
  16. Iizuka K, Ishihara T, Watanabe M, Ito A, Sarai M, Miyahara R, Suzuki A, Saitoh E, Sasaki H. Nutritional Assessment of Hospital Meals by Food-Recording Applications. Nutrients 2022;14(18):3754 View
  17. Roberts C, Gill N, Baxter B, Sims S. Ecological Validation and Practical Challenges of Conducting Dietary Analysis in Athletic Individuals Using a Novel Remote Food Photography Method Mobile Phone Application. Journal of Science in Sport and Exercise 2023 View
  18. Azab S, de Souza R, Ly R, Teo K, Atkinson S, Morrison K, Anand S, Britz-McKibbin P. Non-esterified fatty acids as biomarkers of diet and glucose homeostasis in pregnancy: The impact of fatty acid reporting methods. Prostaglandins, Leukotrienes and Essential Fatty Acids 2022;176:102378 View
  19. Van Wymelbeke-Delannoy V, Juhel C, Bole H, Sow A, Guyot C, Belbaghdadi F, Brousse O, Paindavoine M. A Cross-Sectional Reproducibility Study of a Standard Camera Sensor Using Artificial Intelligence to Assess Food Items: The FoodIntech Project. Nutrients 2022;14(1):221 View
  20. Ben-Porat T, Alberga A, Audet M, Belleville S, Cohen T, Garneau P, Lavoie K, Marion P, Mellah S, Pescarus R, Rahme E, Santosa S, Studer A, Vuckovic D, Woods R, Yousefi R, Bacon S, Ben-Porat T, Cohen T. Understanding the impact of radical changes in diet and the gut microbiota on brain function and structure: rationale and design of the EMBRACE study. Surgery for Obesity and Related Diseases 2023;19(9):1000 View
  21. Surdilovic D, Abdelaal H, D'Souza J. Using artificial intelligence in preventive dentistry: A narrative review. Journal of Datta Meghe Institute of Medical Sciences University 2023;18(1):146 View
  22. Ducarmon Q, Grundler F, Le Maho Y, Wilhelmi de Toledo F, Zeller G, Habold C, Mesnage R. Remodelling of the intestinal ecosystem during caloric restriction and fasting. Trends in Microbiology 2023;31(8):832 View
  23. Ramírez-Contreras C, Farran-Codina A, Zerón-Rugerio M, Izquierdo-Pulido M. Relative Validity and Reliability of the Remind App as an Image-Based Method to Assess Dietary Intake and Meal Timing in Young Adults. Nutrients 2023;15(8):1824 View
  24. Whatnall M, Kolokotroni K, Fozard T, Evans T, Marwood J, Ells L, Burrows T. How is online self-reported weight compared with image-captured weight? A comparative study using data from an online longitudinal study of young adults. The American Journal of Clinical Nutrition 2023;118(2):452 View
  25. Ho D, Chiu W, Kao J, Tseng H, Yao C, Su H, Wei P, Le N, Nguyen H, Chang J. Mitigating errors in mobile-based dietary assessments: Effects of a data modification process on the validity of an image-assisted food and nutrition app. Nutrition 2023;116:112212 View
  26. Vitale M, Bruno V, D’Abbronzo G, Rivellese A, Bozzetto L, Scidà G, Annuzzi G. Evaluation of eating habits by 7-day food record: web-PC vs. traditional paper format. International Journal of Food Sciences and Nutrition 2023;74(4):580 View
  27. Sarkar C, Mohanty V, Balappanavar A, Rijhwani K, Chahar P. Development, validation, and usability testing of prototype mobile application for oral health promotion during pregnancy in India. Indian Journal of Public Health 2023;67(3):376 View
  28. Severinsen F, Andersen L, Paulsen M. The Use of a Decision Support System (MyFood) to Assess Dietary Intake Among Free-Living Older Adults in Norway: Evaluation Study. JMIR mHealth and uHealth 2023;11:e45079 View
  29. Leino A, Magee J, Kershaw D, Pai M, Park J. A Comprehensive Mixed‐Method Approach to Characterize the Source of Diurnal Tacrolimus Exposure Variability in Children: Systematic Review, Meta‐analysis, and Application to an Existing Data Set. The Journal of Clinical Pharmacology 2023 View
  30. Salinari A, Machì M, Armas Diaz Y, Cianciosi D, Qi Z, Yang B, Ferreiro Cotorruelo M, Villar S, Dzul Lopez L, Battino M, Giampieri F. The Application of Digital Technologies and Artificial Intelligence in Healthcare: An Overview on Nutrition Assessment. Diseases 2023;11(3):97 View
  31. Larke J, Chin E, Bouzid Y, Nguyen T, Vainberg Y, Lee D, Pirsiavash H, Smilowitz J, Lemay D. Surveying Nutrient Assessment with Photographs of Meals (SNAPMe): A Benchmark Dataset of Food Photos for Dietary Assessment. Nutrients 2023;15(23):4972 View
  32. Pala D, Petrini G, Bosoni P, Larizza C, Quaglini S, Lanzola G. Smartphone applications for nutrition Support: A systematic review of the target outcomes and main functionalities. International Journal of Medical Informatics 2024;184:105351 View
  33. Santarossa S, Redding A, Connell M, Kao K, Susick L, Kerver J. Exploring preliminary dietary intake results using a novel dietary assessment tool with pregnant participants enrolled in a birth cohort. BMC Research Notes 2024;17(1) View
  34. Iglesies-Grau J, Dionne V, Latour É, Gayda M, Besnier F, Gagnon D, Debray A, Gagnon C, Pelletier V, Nigam A, L’Allier P, Juneau M, Bouabdallaoui N, Bherer L. Mediterranean diet and time-restricted eating as a cardiac rehabilitation approach for patients with coronary heart disease and pre-diabetes: the DIABEPIC-1 protocol of a feasibility trial. BMJ Open 2023;13(10):e073763 View
  35. Scanlon S, Norton C. Investigating Nutrition and Hydration Knowledge and Practice among a Cohort of Age-Grade Rugby Union Players. Nutrients 2024;16(4):533 View
  36. Guo Z, Wu Q, Wang X, Dai Y, Ma Y, Qiu Y, Zhang Y, Wang X, Jin J. Effects of message framing and risk perception on health communication for optimum cardiovascular disease primary prevention: a protocol for a multicenter randomized controlled study. Frontiers in Public Health 2024;12 View
  37. Sharif I, Badrasawi M, Zidan S, Zghier H, Abu Sunaina R, Abu Mazer T. Creation, Validation, and Use of Photo-Based Smartphone Application for Dietary Fiber Counting Among University Students. Topics in Clinical Nutrition 2024;39(2):157 View
  38. Veena N, Prasad M, Aruna Deepthi S, Swaroopa Rani B, Nayak M, Someshwar S. An Optimized Recurrent Neural Network for re-modernize food dining bowls and estimating food capacity from images. Entertainment Computing 2024;50:100664 View
  39. Muñoz J, Groskreutz M, Compher C, Andy U. Diet Intake After Diet Modification Intervention in Women With Fecal Incontinence. Urogynecology 2024 View
  40. Mavar M, Sorić T, Bagarić E, Sarić A, Matek Sarić M. The Power of Vitamin D: Is the Future in Precision Nutrition through Personalized Supplementation Plans?. Nutrients 2024;16(8):1176 View
  41. 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
  42. Sak J, Suchodolska M. Artificial Intelligence in Nutrients Science Research: A Review. Nutrients 2021;13(2):322 View

Books/Policy Documents

  1. Kadam P, Petkar N, Phansalkar S. Information and Communication Technology for Competitive Strategies (ICTCS 2020). View
  2. Shaikh K, Vivek Bekal S, Marei H, Elsayed W, Surdilovic D, Jawad L. Artificial Intelligence in Dentistry. View
  3. Côté M, Lamarche B. Artificial Intelligence in Clinical Practice. View