Integration of an Image-Based Dietary Assessment Paradigm into Dietetic Training Improves Food Portion Estimates by Future Dietitians

Abstract

The use of image-based dietary assessments (IBDAs) has rapidly increased; however, there is no formalized training program to enhance the digital viewing skills of dieticians. An IBDA was integrated into a nutritional practicum course in the School of Nutrition and Health Sciences, Taipei Medical University Taiwan. An online IBDA platform was created as an off-campus remedial teaching tool to reinforce the conceptualization of food portion sizes. Dietetic students' receptiveness and response to the IBDA, and their performance in food identification and quantification, were compared between the IBDA and real food visual estimations (RFVEs). No differences were found between the IBDA and RFVE in terms of food identification (67% vs. 71%) or quantification (±10% of estimated calories: 23% vs. 24%). A Spearman correlation analysis showed a moderate to high correlation for calorie estimates between the IBDA and RFVE (r ≥ 0.33~0.75, all p < 0.0001). Repeated IBDA training significantly improved students' image-viewing skills [food identification: first semester: 67%; pretest: 77%; second semester: 84%) and quantification [±10%: first semester: 23%; pretest: 28%; second semester: 32%; and ±20%: first semester: 38%; pretest: 48%; second semester: 59%] and reduced absolute estimated errors from 27% (first semester) to 16% (second semester). Training also greatly improved the identification of omitted foods (e.g., condiments, sugar, cooking oil, and batter coatings) and the accuracy of food portion size estimates. The integration of an IBDA into dietetic courses has the potential to help students develop knowledge and skills related to "e-dietetics".

Keywords: dietetic training; image-based dietary assessment; portion size estimation.

Figure 1

Flowchart and timeline of the integration of the image-based dietary assessment (IBDA) training protocol into the nutrition practicum course. (A) Study timeline; (B): Flowchart of IBDA training. PSMA: portion-size measurement aids.

Figure 2

Students’ overall performance of total calorie estimations (A); The absolute estimated error (B); data are presented as the median with the 95% confidence interval (CI); linear trend test using the median value of each semester data; *** p-trend < 0.0001 (C) median estimated error of total calories in the first semester (n = 84), the online pretest (n = 74), and the second semester (n = 81) of the nutrition practicum course.

Figure 3

Effects of repeated image-based dietary assessment (IBDA) training on the identification (A) and quantification (B) of condiments, cooking oils, and batter coatings, and poor performers (defined as ≥3 answers within >50% of the ground truth kcal on the first semester exam) (n = 14); linear trend test using the median value of each semester data; *** p-trend < 0.0001 (C). Spearman correlation analysis of self-ranking scores of usefulness of IBDA integration into the course and the median estimated error among poor performers (n = 14) (D).