Literature DB >> 29962361

Food identification by barcode scanning in the Netherlands: a quality assessment of labelled food product databases underlying popular nutrition applications.

Marcus Maringer1, Nancy Wisse-Voorwinden1, Pieter van 't Veer1, Anouk Geelen1.   

Abstract

OBJECTIVE: The quality of labelled food product databases underlying popular diet applications (apps) with barcode scanners was investigated.
DESIGN: Product identification rates for the scanned products and the availability and accuracy of nutrient values were calculated.
SETTING: One hundred food products were selected from the two largest supermarket chains in the Netherlands. Using the barcode scanners of the selected apps, the products were scanned and the results recorded as food diary entries. The collected data were exported.
SUBJECTS: Seven diet apps with barcode scanner and food recording feature were selected from the Google Play and Apple app stores.
RESULTS: Energy values were available for 99 % of the scanned products, of which on average 79 % deviated not more than 5 % from the true value. MyFitnessPal provided values for sixteen nutrients, while Virtuagym Food and Yazio provided values for only four nutrients. MyFitnessPal also showed the largest percentage of correctly identified products (i.e. 96 %) and SparkPeople the smallest (i.e. 5 %). The accuracy of the provided nutrient values varied greatly between apps and nutrients.
CONCLUSIONS: While energy was the most consistently and accurately reported value, the availability and accuracy of other values varied greatly between apps. Whereas popular diet apps with barcode scanners might be valuable tools for dietary assessments on the product and energy level, they appear less suitable for assessments on the nutrient level. The presence of user-generated database entries implies that the availability of food products might vary depending on the size and diversity of an app's user base.

Entities:  

Keywords:  Barcode scanning; Barcodes; Diet apps; Dietary intake assessment; Food database; Food identification; Labelled food products; Technological innovations

Mesh:

Year:  2018        PMID: 29962361     DOI: 10.1017/S136898001800157X

Source DB:  PubMed          Journal:  Public Health Nutr        ISSN: 1368-9800            Impact factor:   4.022


  8 in total

1.  Valuing the Diversity of Research Methods to Advance Nutrition Science.

Authors:  Richard D Mattes; Sylvia B Rowe; Sarah D Ohlhorst; Andrew W Brown; Daniel J Hoffman; DeAnn J Liska; Edith J M Feskens; Jaapna Dhillon; Katherine L Tucker; Leonard H Epstein; Lynnette M Neufeld; Michael Kelley; Naomi K Fukagawa; Roger A Sunde; Steven H Zeisel; Anthony J Basile; Laura E Borth; Emahlea Jackson
Journal:  Adv Nutr       Date:  2022-08-01       Impact factor: 11.567

2.  Rethinking the Use of Mobile Apps for Dietary Assessment in Medical Research.

Authors:  Wael Khazen; Florent Schäfer; Guy Fagherazzi; Jean-François Jeanne; Laëtitia Demaretz
Journal:  J Med Internet Res       Date:  2020-06-18       Impact factor: 5.428

3.  A Tool to Measure Young Adults' Food Intake: Design and Development of an Australian Database of Foods for the Eat and Track Smartphone App.

Authors:  Lyndal Wellard-Cole; Melisa Potter; Jisu Joseph Jung; Juliana Chen; Judy Kay; Margaret Allman-Farinelli
Journal:  JMIR Mhealth Uhealth       Date:  2018-11-07       Impact factor: 4.773

4.  Man or machine? Will the digital transition be able to automatize dietary intake data collection?

Authors:  Bent Egberg Mikkelsen
Journal:  Public Health Nutr       Date:  2019-05       Impact factor: 4.022

5.  Evaluation of the Ability of Diet-Tracking Mobile Applications to Estimate Energy and Nutrient Intake in Japan.

Authors:  Nana Shinozaki; Kentaro Murakami
Journal:  Nutrients       Date:  2020-10-29       Impact factor: 5.717

Review 6.  Nutrition-Related Mobile Apps in the French App Stores: Assessment of Functionality and Quality.

Authors:  Prescilla Martinon; Ina Saliasi; Laurie Fraticelli; Florence Carrouel; Denis Bourgeois; Colette Smentek; Claude Dussart
Journal:  JMIR Mhealth Uhealth       Date:  2022-03-14       Impact factor: 4.947

Review 7.  Optimal Design of Clinical Trials of Dietary Interventions in Disorders of Gut-Brain Interaction.

Authors:  Heidi M Staudacher; Chu Kion Yao; William D Chey; Kevin Whelan
Journal:  Am J Gastroenterol       Date:  2022-03-16       Impact factor: 12.045

8.  Raman Molecular Fingerprints of Rice Nutritional Quality and the Concept of Raman Barcode.

Authors:  Giuseppe Pezzotti; Wenliang Zhu; Haruna Chikaguchi; Elia Marin; Francesco Boschetto; Takehiro Masumura; Yo-Ichiro Sato; Tetsuya Nakazaki
Journal:  Front Nutr       Date:  2021-06-23
  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.