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ARS Home » Northeast Area » Boston, Massachusetts » Jean Mayer Human Nutrition Research Center On Aging » Research » Publications at this Location » Publication #390454

Research Project: Energy Met.: Novel Approaches to Facilitating Successful Energy Regulation in Aging--Obesity & Met.: Role of Adipocyte Metabolism in the Development of Obesity and Associated Metabolic Complications

Location: Jean Mayer Human Nutrition Research Center On Aging

Title: 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
item TAYLOR, SALIMA - Jean Mayer Human Nutrition Research Center On Aging At Tufts University
item KORPUSIK, MANDY - Massachusetts Institute Of Technology
item DAS, SAI KRUPA - Jean Mayer Human Nutrition Research Center On Aging At Tufts University
item GILHOOLY, CHERYL - Jean Mayer Human Nutrition Research Center On Aging At Tufts University
item SIMPSON, RYAN - Tufts University
item GLASS, JAMES - Massachusetts Institute Of Technology
item ROBERTS, SUSAN - Jean Mayer Human Nutrition Research Center On Aging At Tufts University

Submitted to: Journal of Medical Internet Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/10/2021
Publication Date: 12/6/2021
Citation: Taylor, S., Korpusik, M., Das, S., Gilhooly, C., Simpson, R., Glass, J., Roberts, S. 2021. 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. Journal of Medical Internet Research. 23(12):e26988. https://doi.org/10.2196/26988.
DOI: https://doi.org/10.2196/26988

Interpretive Summary: Self-monitoring of food intake is a cornerstone of national recommendations for health, but existing applications for this purpose are burdensome, which limits use. We developed and pilot tested a new app that combines speech understanding technology with technologies for mapping foods to appropriate food composition codes, for lower-burden and automated nutritional analysis of dietary intake. In a pilot study, there was no significant difference in energy intake between values obtained by the new method and the gold standard 24-h recall. This first demonstration of using natural spoken language to map reported foods to food composition codes demonstrates a promising new approach to automate assessments of dietary intake.

Technical Abstract: 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.