Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Jan 27, 2021
Date Accepted: Nov 10, 2021
Date Submitted to PubMed: Dec 7, 2021
Short Paper: Use of Natural Spoken Language with Automated Mapping of Self-Reported Food Intake to Food Composition Data for Low-Burden Real-Time Dietary Assessment
ABSTRACT
Background:
Self-monitoring food intake is a cornerstone of national recommendations for health, but existing applications are burdensome, 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 24h-recall method for assessment of energy intake. COCO was used for five consecutive days, and 24-h 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 Body Mass Index of 24 (range 17-48) kg/m2.
Results:
There was no significant difference in energy intake between values obtained by COCO and 24-h recall for days when both methods were used (2092 +/- 1044 [SD] versus 2030 +/- 687 [SD], P=0.70). There was also no differences between the methods in the percent of energy from protein, carbohydrate and fat (P=0.27-0.89), and no trend in energy intake obtained with COCO over the entire 5-day study period (p=0.186).
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. Clinical Trial: N/A
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