
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
Author(s) -
Salima Taylor,
Mandy Korpuski,
Sai Krupa Das,
Cheryl H. Gilhooly,
Ryan Simpson,
James Glass,
Susan B. Roberts
Publication year - 2021
Publication title -
jmir. journal of medical internet research/journal of medical internet research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.446
H-Index - 142
eISSN - 1439-4456
pISSN - 1438-8871
DOI - 10.2196/26988
Subject(s) - medicine , food composition data , coco , environmental health , composition (language) , food intake , food group , gerontology , food science , computer science , artificial intelligence , biology , linguistics , philosophy , orange (colour)
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.