The use of crowdsourcing for dietary self-monitoring: Crowdsourced ratings of food pictures are comparable to ratings by trained observers

Gabrielle M. Turner-McGrievy (Corresponding Author), Elina E. Helander, Kirsikka Kaipainen, Jose Maria Perez-Macias, Ilkka Korhonen

Research output: Contribution to journalArticleScientificpeer-review

16 Citations (Scopus)

Abstract

Objective Crowdsourcing dietary ratings for food photographs, which uses the input of several users to provide feedback, has potential to assist with dietary self-monitoring. Materials and methods This study assessed how closely crowdsourced ratings of foods and beverages contained in 450 pictures from the Eatery mobile app as rated by peer users (fellow Eatery app users) (n=5006 peers, mean 18.4 peer ratings/photo) using a simple 'healthiness' scale were related to the ratings of the same pictures by trained observers (raters). In addition, the foods and beverages present in each picture were categorized and the impact on the peer rating scale by food/beverage category was examined. Raters were trained to provide a 'healthiness' score using criteria from the 2010 US Dietary Guidelines. Results The average of all three raters' scores was highly correlated with the peer healthiness score for all photos (r=0.88, p
Original languageEnglish
Pages (from-to)112-119
JournalJournal of the American Medical Informatics Association
Volume22
Issue numbere1
DOIs
Publication statusPublished - 2014
MoE publication typeA1 Journal article-refereed

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