scholarly journals 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 (Preprint)

Author(s):  
Salima Taylor ◽  
Mandy Korpuski ◽  
Sai Das ◽  
Cheryl Gilhooly ◽  
Ryan Simpson ◽  
...  
2021 ◽  
Author(s):  
Salima Taylor ◽  
Mandy Korpuski ◽  
Sai Das ◽  
Cheryl Gilhooly ◽  
Ryan Simpson ◽  
...  

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. CLINICALTRIAL N/A


2012 ◽  
Vol 82 (3) ◽  
pp. 209-215 ◽  
Author(s):  
Simone Bell ◽  
Heikki Pakkala ◽  
Michael P. Finglas

Food composition data (FCD) comprises the description and identification of foods, as well as their nutrient content, other constituents, and food properties. FCD are required for a range of purposes including food labeling, supporting health claims, nutritional and clinical management, consumer information, and research. There have been differences within and beyond Europe in the way FCD are expressed with respect to food description, definition of nutrients and other food properties, and the methods used to generate data. One of the major goals of the EuroFIR NoE project (2005 - 10) was to provide tools to overcome existing differences among member states and parties with respect to documentation and interchange of FCD. The establishment of the CEN’s (European Committee for Standardisation) TC 387 project committee on Food Composition Data, led by the Swedish Standards Institute, and the preparation of the draft Food Data Standard, has addressed these deficiencies by enabling unambiguous identification and description of FCD and their quality, for dissemination and data interchange. Another major achievement of the EuroFIR NoE project was the development and dissemination of a single, authoritative source of FCD in Europe enabling the interchange and update of data between countries, and also giving access to users of FCD.


2004 ◽  
Vol 23 (6) ◽  
pp. 669-682 ◽  
Author(s):  
Donald R. Davis ◽  
Melvin D. Epp ◽  
Hugh D. Riordan

2010 ◽  
Vol 23 (7) ◽  
pp. 749-752 ◽  
Author(s):  
C. Hodgkins ◽  
M.M. Raats ◽  
M.B. Egan ◽  
A. Fragodt ◽  
J. Buttriss ◽  
...  

Nutrients ◽  
2018 ◽  
Vol 10 (4) ◽  
pp. 433 ◽  
Author(s):  
Barbara Koroušić Seljak ◽  
Peter Korošec ◽  
Tome Eftimov ◽  
Marga Ocke ◽  
Jan van der Laan ◽  
...  

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