%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 23
%N 12
%P e26988
%T 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
%A Taylor,Salima
%A Korpusik,Mandy
%A Das,Sai
%A Gilhooly,Cheryl
%A Simpson,Ryan
%A Glass,James
%A Roberts,Susan
%+ Jean Mayer United States Department of Agriculture Human Nutrition Research Center on Aging, Tufts University, 711 Washington Street, Boston, MA, 02111, United States, 1 617 556 3238, susan.roberts@tufts.edu
%K energy intake
%K macronutrient intakes
%K 24-hour recall
%K machine learning
%K convolutional neural networks
%K nutrition
%K diet
%K app
%K natural language processing
%D 2021
%7 6.12.2021
%9 Original Paper
%J J Med Internet Res
%G English
%X 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. 
%M 34874885
%R 10.2196/26988
%U https://www.jmir.org/2021/12/e26988
%U https://doi.org/10.2196/26988
%U http://www.ncbi.nlm.nih.gov/pubmed/34874885