Rx Food App: A Proof-of-Concept Study of an Image-Based Dietary Assessment Mobile Application. (7th June 2021)

• Record Type: Journal Article
• Authors: Jefferson, Katherine; Choi, Elizabeth; Lichti, Derrick; Alfonsi, Jeffrey; Patel, Barkha; Hamilton, Jill; Arcand, JoAnne
• Is Part Of: Current developments in nutrition. Volume 5(2021)Supplement 2
• Page Start: 1001
• Page End: 1001
• Publication Date: 2021-06-07
• Subjects: Nutrition -- Periodicals; Nutritional Physiological Phenomena; Nutrition; Periodicals; Periodicals; Fulltext; Internet Resources; Periodicals; 612.3
• DOI: 10.1093/cdn/nzab052_004
• Languages: English
• ISSNs: 2475-2991

Abstract

Abstract: Objectives: To determine if Rx Food, an image-based dietary assessment app powered by artificial intelligence, can derive comparable nutritional composition estimates compared to calculated methods. Sub-group analyses assessed differences between composite (i.e., multiple ingredients) and single item foods. Methods: Food items were selected for testing based on their frequency of consumption among patients attending a weight management clinic. Food photos were uploaded, and serving sizes entered, into the app which generated estimated nutrient data. The nutritional composition of foods was also analyzed with ESHA Food Processor software. Nutrient estimates between the methods were compared using paired t-tests, Pearson correlation coefficients, and Bland-Altman plots for energy, carbohydrates, protein, total fat, fibre, total sugar and sodium. Results: Thirty-nine food items were analyzed [ n = 10 (27%) composite items and n = 29 (73%) single item foods]. There were no statistically significant differences in the mean differences in estimates from Rx Food and calculated values for all nutrients: −4.3 ± 29.2 kcal for energy, −0.4 ± 2.6 g for carbohydrates, −0.1 ± 1.9 g for protein, −0.3 ± 1.7 g for fat, −0.2 ± 2.3 g for fibre, 0.01 ± 1.4 g for sugar, and −33 ± 135 mg for sodium. Among all food items, a strong, significant correlation (r > 0.80; P < 0.05) was observed for all nutrients except fibre (r = 0.552; P < 0.001). In the Bland-Altman plots for all foods, significant bias was found for fibre (r = 0.562; P < 0.001), fat (r = 0.562; P = 0.025), and sodium (r = 0.359; P = 0.025), suggesting that Rx Food may underestimate nutrient composition at higher levels. Subgroup analyses of composite items showed significant strong correlations for energy, carbohydrates, protein, and sugar (r > 0.80; P < 0.05), significant moderate correlations (r = 0.60–0.79; P < 0.05) for fat and fibre, but not for sodium (r = 0.591; P = 0.072). Single item analysis showed significant correlations for all nutrients (r > 0.80; P < 0.05). Conclusions: This preliminary data shows that Rx Food has the potential to be an accurate, image-based, low burden tool to calculate nutrient composition of foods. These findings justify further research to determine the validity of Rx Food in its ability to generate accurate nutrient intake data as a dietary assessment tool. Funding Sources: N/A.