Dietary intake provides some of the most valuable insights for mounting intervention programs for prevention. With the growing concern about adolescent overweight and obesity, the need to accurately measure diet becomes imperative. Assessment among adolescents is problematic as this group has irregular eating patterns and they have less enthusiasm for recording food intake. Preliminary studies among adolescents suggest that innovative use of technology may improve the accuracy of diet information from young people. Recognition of immerging technology in mobile devices, e.g., higher resolution pictures, improved memory capacity, faster processors, allow these devices to process information not previously possible. This project addresses several objectives of PAR-06-104 by refining and testing methods for adolescents and using innovative methods to improve respondent self-report. Our goal is to develop, implement, and evaluate a mobile device food record that will translate to an accurate account of daily food and nutrient intake among adolescents. Our first steps include further development of our prototype mobile device to include digital photographs, a nutrient data base, and image processing for identification and quantification of food consumption. Mobile computing devices provide a unique vehicle for collecting dietary information that reduces burden on record keepers. Pictures of food can be marked with a variety of input methods that link the item for image processing and analysis that estimate the amount of food. Pictures before and after foods are eaten can estimate the amount of food consumed. We plan to recruit a sample of 100 informed and consenting sixth, seventh, eighth, and ninth-graders attending summer camps on the Purdue campus to consume meals of precisely known composition while using the mobile device food record under controlled conditions to aid with quantifying the error associated with the food and nutrient output. Refinements to the tool will reflect the feedback and correction of errors detected with use under the controlled environment. The final phase of the project will validate the mobile device food record among 103 informed and consenting free-living adolescents between 11 and 17 y by using urinary nitrogen and sucrose and fructose as biomarkers of protein intake and sugar intake, respectively. This tool could be used for population and clinical based studies to improve our understanding of dietary exposures among adolescents. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Research Project (R01)
Project #
1R01DK073711-01A1
Application #
7261155
Study Section
Special Emphasis Panel (ZRG1-HOP-D (50))
Program Officer
Miles, Carolyn
Project Start
2007-09-01
Project End
2010-08-31
Budget Start
2007-09-01
Budget End
2008-08-31
Support Year
1
Fiscal Year
2007
Total Cost
$305,000
Indirect Cost
Name
Purdue University
Department
Nutrition
Type
Other Domestic Higher Education
DUNS #
072051394
City
West Lafayette
State
IN
Country
United States
Zip Code
47907
Wang, Yu; He, Ye; Boushey, Carol J et al. (2018) Context Based Image Analysis With Application in Dietary Assessment and Evaluation. Multimed Tools Appl 77:19769-19794
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Panizza, Chloe E; Boushey, Carol J; Delp, Edward J et al. (2017) Characterizing Early Adolescent Plate Waste Using the Mobile Food Record. Nutrients 9:
Wang, Yu; Zhu, Fengqing; Boushey, Carol J et al. (2017) WEAKLY SUPERVISED FOOD IMAGE SEGMENTATION USING CLASS ACTIVATION MAPS. Proc Int Conf Image Proc 2017:1277-1281
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Fang, Shaobo; Zhu, Fengqing; Boushey, Carol J et al. (2017) THE USE OF CO-OCCURRENCE PATTERNS IN SINGLE IMAGE BASED FOOD PORTION ESTIMATION. IEEE Glob Conf Signal Inf Process 2017:462-466
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Bathgate, Katherine E; Sherriff, Jill L; Leonard, Helen et al. (2017) Feasibility of Assessing Diet with a Mobile Food  Record for Adolescents and Young Adults with  Down Syndrome. Nutrients 9:
Wang, Yu; Fang, Shaobo; Liu, Chang et al. (2016) FOOD IMAGE ANALYSIS: THE BIG DATA PROBLEM YOU CAN EAT! Conf Rec Asilomar Conf Signals Syst Comput 2016:1263-1267

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