title: The use of crowdsourcing for dietary self-monitoring: crowdsourced ratings of food pictures are comparable to ratings by trained observers.
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.
  
  
  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<0.001). Using a multivariate linear model (R(2) = 0.73) to examine the association of peer healthiness scores with foods and beverages present in photos, peer ratings were in the hypothesized direction for both foods/beverages to increase and ones to limit. Photos with fruit, vegetables, whole grains, and legumes, nuts, and seeds (borderline at p = 0.06) were all associated with higher peer healthiness scores, and processed foods (borderline at p = 0.06), food from fast food restaurants, refined grains, red meat, cheese, savory snacks, sweets/desserts, and sugar-sweetened beverages were associated with lower peer healthiness scores.
  
  
  CONCLUSIONS
  The findings suggest that crowdsourcing holds potential to provide basic feedback on overall diet quality to users utilizing a low burden approach.
authors.affiliation: Health Promotion, Education, and Behavior, University of South Carolina, Arnold School of Public Health, Columbia, South Carolina, USA.
authors.forename: Gabrielle M
authors.initials: GM
authors.lastname: Turner-McGrievy
authors.affiliation: Department of Signal Processing, Tampere University of Technology, Tampere, Finland.
authors.forename: Elina E
authors.initials: EE
authors.lastname: Helander
authors.affiliation: VTT Technical Research Centre of Finland, Tampere, Finland.
authors.forename: Kirsikka
authors.initials: K
authors.lastname: Kaipainen
authors.affiliation: Department of Signal Processing, Tampere University of Technology, Tampere, Finland.
authors.forename: Jose Maria
authors.initials: JM
authors.lastname: Perez-Macias
authors.affiliation: Department of Signal Processing, Tampere University of Technology, Tampere, Finland VTT Technical Research Centre of Finland, Tampere, Finland.
authors.forename: Ilkka
authors.initials: I
authors.lastname: Korhonen
mesh_terms: D001628:Beverages; D063045:Crowdsourcing; D004032:Diet; D005502:Food; D006801:Humans; D016014:Linear Models; D063731:Mobile Applications; D010781:Photography; D012648:Self Care
keywords: crowdsourcing; diet; mobile health; self-monitoring; technology
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