Literature DB >> 21324224

Adolescents in the United States can identify familiar foods at the time of consumption and when prompted with an image 14 h postprandial, but poorly estimate portions.

TusaRebecca E Schap1, Bethany L Six, Edward J Delp, David S Ebert, Deborah A Kerr, Carol J Boushey.   

Abstract

OBJECTIVE: To evaluate adolescents' abilities to identify foods and estimate the portion size of foods consumed in order to inform development of the mobile telephone food record (mpFR).
DESIGN: Data were collected from two samples of adolescents (11-18 years). Adolescents in sample 1 participated in one lunch (n 63) and fifty-five of the sixty-three adolescents (87 %) returned for breakfast the next morning. Sample 2 volunteers received all meals and snacks for a 24 h period. At mealtime, sample 1 participants were asked to write down the names of the foods. Sample 2 participants identified foods in an image of their meal 10-14 h postprandial. Adolescents in sample 2 also estimated portion sizes of their breakfast foods and snacks.
RESULTS: Sample 1 identified thirty of the thirty-eight food items correctly, and of the misidentified foods all were identified within the correct major food group. For sample 2, eleven of the thirteen food items were identified correctly 100 % of the time. Half of the breakfast and snack foods had at least one portion size estimate within 10 % of the true amount using a variety of measurement descriptors.
CONCLUSIONS: The results provide evidence that adolescents can correctly identify familiar foods and they can look at an image of their meal and identify the foods in the image up to 14·5 h postprandial. The results of the present study not only inform the development of the mpFR but also provide strong evidence of the use of digital images of eating occasions in research and clinical settings.

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Year:  2011        PMID: 21324224      PMCID: PMC3114201          DOI: 10.1017/S1368980010003794

Source DB:  PubMed          Journal:  Public Health Nutr        ISSN: 1368-9800            Impact factor:   4.022


  17 in total

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2.  Development of a food frequency questionnaire to estimate calcium intake of Asian, Hispanic, and white youth.

Authors:  J Keith Jensen; Deborah Gustafson; Carol J Boushey; Garry Auld; Margaret Ann Bock; Christine M Bruhn; Kathe Gabel; Scottie Misner; Rachel Novotny; Louise Peck; Marsha Read
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3.  Technology-Assisted Dietary Assessment.

Authors:  Fengqing Zhu; Anand Mariappan; Carol J Boushey; Deb Kerr; Kyle D Lutes; David S Ebert; Edward J Delp
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4.  Personal Dietary Assessment Using Mobile Devices.

Authors:  Anand Mariappan; Marc Bosch; Fengqing Zhu; Carol J Boushey; Deborah A Kerr; David S Ebert; Edward J Delp
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2009-01-01

5.  How children remember what they have eaten.

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6.  Shortening the retention interval of 24-hour dietary recalls increases fourth-grade children's accuracy for reporting energy and macronutrient intake at school meals.

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7.  The food intake recording software system is valid among fourth-grade children.

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8.  Accuracy of fourth-graders' dietary recalls of school breakfast and school lunch validated with observations: in-person versus telephone interviews.

Authors:  Suzanne Domel Baxter; William O Thompson; Mark S Litaker; Caroline H Guinn; Francesca H A Frye; Michelle L Baglio; Nicole M Shaffer
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9.  Calcium intake of Asian, Hispanic and white youth.

Authors:  Rachel Novotny; Carol Boushey; Margaret A Bock; Louise Peck; Garry Auld; Christine M Bruhn; Deborah Gustafson; Kathe Gabel; James Keith Jensen; Scottie Misner; Marsha Read
Journal:  J Am Coll Nutr       Date:  2003-02       Impact factor: 3.169

10.  Evidence-based development of a mobile telephone food record.

Authors:  Bethany L Six; Tusarebecca E Schap; Fengqing M Zhu; Anand Mariappan; Marc Bosch; Edward J Delp; David S Ebert; Deborah A Kerr; Carol J Boushey
Journal:  J Am Diet Assoc       Date:  2010-01
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  15 in total

1.  Comparison of known food weights with image-based portion-size automated estimation and adolescents' self-reported portion size.

Authors:  Christina D Lee; Junghoon Chae; TusaRebecca E Schap; Deborah A Kerr; Edward J Delp; David S Ebert; Carol J Boushey
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2.  FOOD IMAGE ANALYSIS: THE BIG DATA PROBLEM YOU CAN EAT!

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3.  Mobile Image Based Color Correction Using Deblurring.

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4.  Image-Based Food Volume Estimation.

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5.  Image Enhancement and Quality Measures for Dietary Assessment Using Mobile Devices.

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Review 6.  Merging dietary assessment with the adolescent lifestyle.

Authors:  T E Schap; F Zhu; E J Delp; C J Boushey
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7.  Accuracy of food portion size estimation from digital pictures acquired by a chest-worn camera.

Authors:  Wenyan Jia; Hsin-Chen Chen; Yaofeng Yue; Zhaoxin Li; John Fernstrom; Yicheng Bai; Chengliu Li; Mingui Sun
Journal:  Public Health Nutr       Date:  2013-12-04       Impact factor: 4.022

8.  MODEL-BASED FOOD VOLUME ESTIMATION USING 3D POSE.

Authors:  Chang Xu; Ye He; Nitin Khanna; Carol J Boushey; Edward J Delp
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9.  Low Complexity Image Quality Measures for Dietary Assessment Using Mobile Devices.

Authors:  Chang Xu; Nitin Khanna; Carol J Boushey; Edward J Delp
Journal:  ISM       Date:  2012-01-09

10.  Novel technologies for assessing dietary intake: evaluating the usability of a mobile telephone food record among adults and adolescents.

Authors:  Bethany L Daugherty; TusaRebecca E Schap; Reynolette Ettienne-Gittens; Fengqing M Zhu; Marc Bosch; Edward J Delp; David S Ebert; Deborah A Kerr; Carol J Boushey
Journal:  J Med Internet Res       Date:  2012-04-13       Impact factor: 5.428

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