TY - JOUR AU - Taylor, Salima AU - Korpusik, Mandy AU - Das, Sai AU - Gilhooly, Cheryl AU - Simpson, Ryan AU - Glass, James AU - Roberts, Susan PY - 2021 DA - 2021/12/6 TI - Use of Natural Spoken Language With Automated Mapping of Self-reported Food Intake to Food Composition Data for Low-Burden Real-time Dietary Assessment: Method Comparison Study JO - J Med Internet Res SP - e26988 VL - 23 IS - 12 KW - energy intake KW - macronutrient intakes KW - 24-hour recall KW - machine learning KW - convolutional neural networks KW - nutrition KW - diet KW - app KW - natural language processing AB - Background: Self-monitoring food intake is a cornerstone of national recommendations for health, but existing apps for this purpose are burdensome for users and researchers, which limits use. Objective: We developed and pilot tested a new app (COCO Nutritionist) that combines speech understanding technology with technologies for mapping foods to appropriate food composition codes in national databases, for lower-burden and automated nutritional analysis of self-reported dietary intake. Methods: COCO was compared with the multiple-pass, interviewer-administered 24-hour recall method for assessment of energy intake. COCO was used for 5 consecutive days, and 24-hour dietary recalls were obtained for two of the days. Participants were 35 women and men with a mean age of 28 (range 20-58) years and mean BMI of 24 (range 17-48) kg/m2. Results: There was no significant difference in energy intake between values obtained by COCO and 24-hour recall for days when both methods were used (mean 2092, SD 1044 kcal versus mean 2030, SD 687 kcal, P=.70). There were also no significant differences between the methods for percent of energy from protein, carbohydrate, and fat (P=.27-.89), and no trend in energy intake obtained with COCO over the entire 5-day study period (P=.19). Conclusions: This first demonstration of a dietary assessment method using natural spoken language to map reported foods to food composition codes demonstrates a promising new approach to automate assessments of dietary intake. SN - 1438-8871 UR - https://www.jmir.org/2021/12/e26988 UR - https://doi.org/10.2196/26988 UR - http://www.ncbi.nlm.nih.gov/pubmed/34874885 DO - 10.2196/26988 ID - info:doi/10.2196/26988 ER -