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

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,

Using participant hedonic ratings of food images to construct data driven food groupings

Theorists posit that food reward is a powerful determinant of intake, yet little is known regarding how individuals’ hedonic ratings of a variety of foods interrelate and how hedonic ratings correspond to habitual dietary intake. Participant ratings of food appeal of 104 food images were collected while participants were in a fed state (n = 129). Self-reported frequency of intake of the food items, perceived hunger, body mass index (BMI), and dietary restraint were also assessed. Principal components analysis (PCA) was employed to analyze hedonic ratings of the foods, to identify component structures and to reduce the number of variables. The resulting component structures comprised 63 images loading on seven components including Energy-Dense Main Courses, Light Main Courses and Seafood as well as components more analogous to traditional food groups (e.g., Fruits, Grains, Desserts, Meats). However, vegetables were not represented in a unique, independent component. All components were positively correlated with reported intake of the food items (r’s = .26–.52, p < .05), except for the Light Main Course component (r = .10). BMI showed a small positive relation with aggregated food appeal ratings (r = .19; p < .05), which was largely driven by the relations between BMI and appeal ratings for Energy-Dense Main Courses (r = .24; p < .01) and Desserts (r = .27; p < .01). Dietary restraint showed a small significant negative relation to Energy-Dense Main Courses (r = −.21; p < .05), and Meats (r = −.18; p < .05). The present investigation provides novel evidence that how individuals’ hedonic ratings of foods aggregate into food components and how these component ratings relate to dietary intake. The notable absence of a vegetable component suggests that individuals’ liking for vegetables is highly variable and, from an empirical standpoint, not related to how they respond hedonically to other food categories