Published on in Vol 7, No 2 (2019): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/9838, first published .
Popular Nutrition-Related Mobile Apps: An Agreement Assessment Against a UK Reference Method

Popular Nutrition-Related Mobile Apps: An Agreement Assessment Against a UK Reference Method

Popular Nutrition-Related Mobile Apps: An Agreement Assessment Against a UK Reference Method

Journals

  1. Koepp J, Baron M, Hernandes Martins P, Brandenburg C, Kira A, Trindade V, Ley Dominguez L, Carneiro M, Frozza R, Possuelo L, De Mello Pinto M, Mahlmann Kipper L, Pinheiro da Costa B. The Quality of Mobile Apps Used for the Identification of Pressure Ulcers in Adults: Systematic Survey and Review of Apps in App Stores. JMIR mHealth and uHealth 2020;8(6):e14266 View
  2. Connor S. Underreporting of Dietary Intake: Key Issues for Weight Management Clinicians. Current Cardiovascular Risk Reports 2020;14(10) View
  3. 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
  4. Siebra C, Souto E. Mobile health support for motor disability individuals: A review focused on holistic assessment and interventions. Technology and Disability 2020;32(1):51 View
  5. Banskota S, Healy M, Goldberg E. 15 Smartphone Apps for Older Adults to Use While in Isolation During the COVID-19 Pandemic. Western Journal of Emergency Medicine 2020;21(3) View
  6. Shinozaki N, Murakami K. Evaluation of the Ability of Diet-Tracking Mobile Applications to Estimate Energy and Nutrient Intake in Japan. Nutrients 2020;12(11):3327 View
  7. 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
  8. 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
  9. Zečević M, Mijatović D, Kos Koklič M, Žabkar V, Gidaković P. User Perspectives of Diet-Tracking Apps: Reviews Content Analysis and Topic Modeling. Journal of Medical Internet Research 2021;23(4):e25160 View
  10. Martinon P, Saliasi I, Bourgeois D, Smentek C, Dussart C, Fraticelli L, Carrouel F. Nutrition-Related Mobile Apps in the French App Stores: Assessment of Functionality and Quality. JMIR mHealth and uHealth 2022;10(3):e35879 View
  11. Yang Y, Yang H, Kusuma J, Shiao S. Validating Accuracy of an Internet-Based Application against USDA Computerized Nutrition Data System for Research on Essential Nutrients among Social-Ethnic Diets for the E-Health Era. Nutrients 2022;14(15):3168 View
  12. Hinojosa-Nogueira D, Ortiz-Viso B, Navajas-Porras B, Pérez-Burillo S, González-Vigil V, de la Cueva S, Rufián-Henares J. Stance4Health Nutritional APP: A Path to Personalized Smart Nutrition. Nutrients 2023;15(2):276 View
  13. Siebra C, Wac K. Engineering uncertain time for its practical integration in ontologies. Knowledge-Based Systems 2022;251:109152 View
  14. 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
  15. Tovar A, Richardson C, Keim N, Van Loan M, Davis B, Casazza G. Four Weeks of 16/8 Time Restrictive Feeding in Endurance Trained Male Runners Decreases Fat Mass, without Affecting Exercise Performance. Nutrients 2021;13(9):2941 View
  16. 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
  17. Hernández-Solano A, Pérez-Hernández V, Burrola-Méndez S, Aguirre A, Gallegos J, Teruel G. Using Household Expenditure Surveys for Comparable and Replicable Nutritional Analysis: Evidence from México. Nutrients 2022;14(17):3588 View
  18. De Santis K, Jahnel T, Sina E, Wienert J, Zeeb H. Digitization and Health in Germany: Cross-sectional Nationwide Survey. JMIR Public Health and Surveillance 2021;7(11):e32951 View
  19. Castela Forte J, Gannamani R, Folkertsma P, Kanthappu S, van Dam S, Wolffenbuttel B. A Pilot Study on the Prevalence of Micronutrient Imbalances in a Dutch General Population Cohort and the Effects of a Digital Lifestyle Program. Nutrients 2022;14(7):1426 View
  20. Mistura L, Comendador Azcarraga F, D’Addezio L, Martone D, Turrini A. An Italian Case Study for Assessing Nutrient Intake through Nutrition-Related Mobile Apps. Nutrients 2021;13(9):3073 View
  21. Kim J, Kim H, Lee J, Ko H, Jung S, Kim H, Wie G, Kim Y. Comparison of Energy and Macronutrients Between a Mobile Application and a Conventional Dietary Assessment Method in Korea. Journal of the Academy of Nutrition and Dietetics 2022;122(11):2127 View
  22. Kaiser B, Stelzl T, Finglas P, Gedrich K. The Assessment of a Personalized Nutrition Tool (eNutri) in Germany: Pilot Study on Usability Metrics and Users’ Experiences. JMIR Formative Research 2022;6(8):e34497 View
  23. Granheim S, Løvhaug A, Terragni L, Torheim L, Thurston M. Mapping the digital food environment: A systematic scoping review. Obesity Reviews 2022;23(1) View
  24. Lin A, Morgan N, Ward D, Tangney C, Alshurafa N, Van Horn L, Spring B. Comparative Validity of Mostly Unprocessed and Minimally Processed Food Items Differs Among Popular Commercial Nutrition Apps Compared with a Research Food Database. Journal of the Academy of Nutrition and Dietetics 2022;122(4):825 View
  25. Kim H, Kim Y, Michaelides A, Park Y. Weight Loss Trajectories and Related Factors in a 16-Week Mobile Obesity Intervention Program: Retrospective Observational Study. Journal of Medical Internet Research 2022;24(4):e29380 View
  26. 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
  27. Siniarski A, Sobieraj P, Samel-Kowalik P, Sińska B, Milewska M, Bzikowska-Jura A. Nutrition-related mobile applications - Should they be used for dietary prevention and treatment of cardiovascular diseases?. Nutrition, Metabolism and Cardiovascular Diseases 2022;32(11):2505 View
  28. Samoilova Y, Matveeva M, Vachadze T, Tolmachev I, Zakharchuk P. Gamification as a method of preventing childhood obesity. Profilakticheskaya meditsina 2022;25(9):117 View
  29. Murai U, Tajima R, Matsumoto M, Sato Y, Horie S, Fujiwara A, Koshida E, Okada E, Sumikura T, Yokoyama T, Ishikawa M, Kurotani K, Takimoto H. Validation of Dietary Intake Estimated by Web-Based Dietary Assessment Methods and Usability Using Dietary Records or 24-h Dietary Recalls: A Scoping Review. Nutrients 2023;15(8):1816 View
  30. 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 2024;64(3):334 View
  31. Peeters W, Cook L, Page O. The effect of pre-exercise protein intake on substrate metabolism, energy expenditure, and energy intake: a dose–response study. Journal of the International Society of Sports Nutrition 2023;20(1) View
  32. Skovgaard L, Trénel P, Westergaard K, Knudsen A. Dietary Patterns and Their Associations with Symptom Levels Among People with Multiple Sclerosis: A Real-World Digital Study. Neurology and Therapy 2023;12(4):1335 View
  33. Newsome F, Cardel M, Chi X, Lee A, Miller D, Menon S, Janicke D, Gurka M, Butryn M, Manasse S. Wellness Achieved Through Changing Habits: A Randomized Controlled Trial of an Acceptance-Based Intervention for Adolescent Girls With Overweight or Obesity. Childhood Obesity 2023;19(8):525 View
  34. 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
  35. Banal M, Bongga D, Angbengco J, Amarra S, Panlasigui L. MyFitnessPal smartphone application: relative validity and intercoder reliability among dietitians in assessing energy and macronutrient intakes of selected Filipino adults with obesity. BMJ Nutrition, Prevention & Health 2024:e000770 View

Books/Policy Documents

  1. Kaput J, Monteiro J, Morine M, Kussmann M. Comprehensive Precision Medicine. View
  2. Amjath-Babu T, Riadura S, Krupnik T. Handbook of Computational Social Science for Policy. View
  3. Weech M, Fallaize R, Kelly E, Hwang F, Franco R, Lovegrove J. Smartphone Apps for Health and Wellness. View