Feasibility of Reviewing Digital Food Images for Dietary Assessment among Nutrition Professionals
1
Nutritional Sciences Programme & Center for Community Health, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur 50300, Malaysia
2
Dietetics Programme & Center for Community Health, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur 50300, Malaysia
*
Author to whom correspondence should be addressed.
Nutrients 2018, 10(8), 984; https://doi.org/10.3390/nu10080984
Received: 12 June 2018 / Revised: 23 July 2018 / Accepted: 25 July 2018 / Published: 27 July 2018
(This article belongs to the Special Issue Food Portion Size in Relation to Diet and Health)
Validity of image-assisted and image-based dietary assessment methods relies on the accuracy of portion size estimation based on food images. However, little is known on the ability of nutrition professionals in assessing dietary intake based on digital food images. This study aims to examine the ability of nutrition professionals in reviewing food images with regard to food item identification and portion size estimation. Thirty-eight nutritionists, dietitians, and nutrition researchers participated in this study. Through an online questionnaire, participants’ accuracy in identifying food items and estimating portion sizes of two sets of digital food images presenting a meal on a plate (Image PL) and in a bowl (Image BW) were tested. Participants reported higher accuracy in interpreting Image BW compared to Image PL, both in terms of accuracy in food identification (75.3 ± 17.6 vs. 68.9 ± 17.1%) and percentage difference in portion size estimation (44.3 ± 16.6 vs. 47.6 ± 21.2%). Weight of raw vegetables was significantly underestimated (−45.1 ± 22.8% vs. −21.2 ± 37.4%), while drink was significantly overestimated (40.1 ± 45.8% vs. 26.1 ± 32.2) in both images. Less than one-third of the participants estimated portion size within 10% of actual weight for Image PL (23.7%) and Image BW (32.3%). Accuracy of nutrition professionals in reviewing food images could be further improved with training on better perception of portion sizes from images.
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MDPI and ACS Style
Ainaa Fatehah, A.; Poh, B.K.; Nik Shanita, S.; Wong, J.E. Feasibility of Reviewing Digital Food Images for Dietary Assessment among Nutrition Professionals. Nutrients 2018, 10, 984.
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