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

Research Instruments, Questionnaires, and Tools (576) Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (832)

Published on in Vol 17, No 1 (2015): January

Validity of Electronic Diet Recording Nutrient Estimates Compared to Dietitian Analysis of Diet Records: Randomized Controlled Trial

Validity of Electronic Diet Recording Nutrient Estimates Compared to Dietitian Analysis of Diet Records: Randomized Controlled Trial

Validity of Electronic Diet Recording Nutrient Estimates Compared to Dietitian Analysis of Diet Records: Randomized Controlled Trial

Authors of this article:

Susan K Raatz1, 2 Author Orcid Image ;   Angela J Scheett1, 3 Author Orcid Image ;   LuAnn K Johnson1, 3 Author Orcid Image ;   Lisa Jahns1 Author Orcid Image
Article Authors Cited by (20) Tweetations (12) Metrics

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  8. Lancaster R, Radd‐Vagenas S, Fiatarone Singh M, Noble Y, Daniel K, Mavros Y, Sachdev P, Lautenschlager N, Cox K, Brodaty H, O'Leary F, Flood V. Electronic food records among middle‐aged and older people: A comparison of self‐reported and dietitian‐assisted information. Nutrition & Dietetics 2021;78(2):145 View
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  10. Cho J, Kim S. Personal and social predictors of use and non-use of fitness/diet app: Application of Random Forest algorithm. Telematics and Informatics 2020;55:101301 View
  11. Raatz S, Jahns L, Johnson L, Scheett A, Carriquiry A, Lemieux A, Nakajima M, al'Absi M. Smokers report lower intake of key nutrients than nonsmokers, yet both fall short of meeting recommended intakes. Nutrition Research 2017;45:30 View
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. Abeltino A, Riente A, Bianchetti G, Serantoni C, De Spirito M, Capezzone S, Esposito R, Maulucci G. Digital applications for diet monitoring, planning, and precision nutrition for citizens and professionals: a state of the art. Nutrition Reviews 2024 View
  18. Appleton K. Liking for Sweet Taste, Sweet Food Intakes, and Sugar Intakes. Nutrients 2024;16(21):3672 View

Books/Policy Documents

  1. Xu X, Hou L, Guo Z, Wang J, Li J. Big Data – BigData 2018. View