Literature DB >> 33033394

Review of the validity and feasibility of image-assisted methods for dietary assessment.

Christoph Höchsmann1, Corby K Martin2.   

Abstract

Accurately quantifying dietary intake is essential to understanding the effect of diet on health and evaluating the efficacy of dietary interventions. Self-report methods (e.g., food records) are frequently utilized despite evident inaccuracy of these methods at assessing energy and nutrient intake. Methods that assess food intake via images of foods have overcome many of the limitations of traditional self-report. In cafeteria settings, digital photography has proven to be unobtrusive and accurate and is the method of choice for assessing food provision, plate waste, and food intake. In free-living conditions, image capture of food selection and plate waste via the user's smartphone, is promising and can produce accurate energy intake estimates, though accuracy is not guaranteed. These methods foster (near) real-time transfer of data and eliminate the need for portion size estimation by the user since the food images are analyzed by trained raters. A limitation that remains, similar to self-report methods where participants must truthfully record all consumed foods, is intentional and/or unintentional underreporting of foods due to social desirability or forgetfulness. Methods that rely on passive image capture via wearable cameras are promising and aim to reduce user burden; however, only pilot data with limited validity are currently available and these methods remain obtrusive and cumbersome. To reduce analysis-related staff burden and to allow real-time feedback to the user, recent approaches have aimed to automate the analysis of food images. The technology to support automatic food recognition and portion size estimation is, however, still in its infancy and fully automated food intake assessment with acceptable precision not yet a reality. This review further evaluates the benefits and challenges of current image-assisted methods of food intake assessment and concludes that less burdensome methods are less accurate and that no current method is adequate in all settings.

Entities:  

Year:  2020        PMID: 33033394      PMCID: PMC7686022          DOI: 10.1038/s41366-020-00693-2

Source DB:  PubMed          Journal:  Int J Obes (Lond)        ISSN: 0307-0565            Impact factor:   5.095


  58 in total

1.  Digital photography: a new method for estimating food intake in cafeteria settings.

Authors:  D A Williamson; H R Allen; P Davis Martin; A Alfonso; B Gerald; A Hunt
Journal:  Eat Weight Disord       Date:  2004-03       Impact factor: 4.652

2.  Validity and feasibility of a digital diet estimation method for use with preschool children: a pilot study.

Authors:  Theresa A Nicklas; Carol E O'Neil; Janice Stuff; Lora Suzanne Goodell; Yan Liu; Corby K Martin
Journal:  J Nutr Educ Behav       Date:  2012-06-23       Impact factor: 3.045

3.  The effect of keeping food records on eating patterns.

Authors:  S M Rebro; R E Patterson; A R Kristal; C L Cheney
Journal:  J Am Diet Assoc       Date:  1998-10

4.  "Snap-n-Eat": Food Recognition and Nutrition Estimation on a Smartphone.

Authors:  Weiyu Zhang; Qian Yu; Behjat Siddiquie; Ajay Divakaran; Harpreet Sawhney
Journal:  J Diabetes Sci Technol       Date:  2015-04-21

5.  Beyond Nutrient Intake: Use of Digital Food Photography Methodology to Examine Family Dinnertime.

Authors:  Morgan L McCloskey; Susan L Johnson; Traci A Bekelman; Corby K Martin; Laura L Bellows
Journal:  J Nutr Educ Behav       Date:  2019-02-28       Impact factor: 3.045

6.  Measurement of children's food intake with digital photography and the effects of second servings upon food intake.

Authors:  Corby K Martin; Robert L Newton; Stephen D Anton; H Raymond Allen; Anthony Alfonso; Hongmei Han; Tiffany Stewart; Melinda Sothern; Donald A Williamson
Journal:  Eat Behav       Date:  2006-04-27

7.  Assessment of the accuracy of portion size reports using computer-based food photographs aids in the development of an automated self-administered 24-hour recall.

Authors:  Amy F Subar; Jennifer Crafts; Thea Palmer Zimmerman; Michael Wilson; Beth Mittl; Noemi G Islam; Suzanne McNutt; Nancy Potischman; Richard Buday; Stephen G Hull; Tom Baranowski; Patricia M Guenther; Gordon Willis; Ramsey Tapia; Frances E Thompson
Journal:  J Am Diet Assoc       Date:  2010-01

8.  Preliminary Feasibility and Acceptability of the Remote Food Photography Method for Assessing Nutrition in Young Children with Type 1 Diabetes.

Authors:  Meredith H Rose; Randi Streisand; Laura Aronow; Carrie Tully; Corby K Martin; Eleanor Mackey
Journal:  Clin Pract Pediatr Psychol       Date:  2018-05-24

9.  Reported Energy Intake Accuracy Compared to Doubly Labeled Water and Usability of the Mobile Food Record among Community Dwelling Adults.

Authors:  Carol J Boushey; Melissa Spoden; Edward J Delp; Fengqing Zhu; Marc Bosch; Ziad Ahmad; Yurii B Shvetsov; James P DeLany; Deborah A Kerr
Journal:  Nutrients       Date:  2017-03-22       Impact factor: 5.717

10.  Efficacy of a school-based obesity prevention intervention at reducing added sugar and sodium in children's school lunches: the LA Health randomized controlled trial.

Authors:  Keely R Hawkins; Jeffrey H Burton; John W Apolzan; Jessi L Thomson; Donald A Williamson; Corby K Martin
Journal:  Int J Obes (Lond)       Date:  2018-09-25       Impact factor: 5.095

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  4 in total

1.  Improvement of Methodology for Manual Energy Intake Estimation From Passive Capture Devices.

Authors:  Zhaoxing Pan; Dan Forjan; Tyson Marden; Jonathan Padia; Tonmoy Ghosh; Delwar Hossain; J Graham Thomas; Megan A McCrory; Edward Sazonov; Janine A Higgins
Journal:  Front Nutr       Date:  2022-06-22

2.  A Novel Approach to Dining Bowl Reconstruction for Image-Based Food Volume Estimation.

Authors:  Wenyan Jia; Yiqiu Ren; Boyang Li; Britney Beatrice; Jingda Que; Shunxin Cao; Zekun Wu; Zhi-Hong Mao; Benny Lo; Alex K Anderson; Gary Frost; Megan A McCrory; Edward Sazonov; Matilda Steiner-Asiedu; Tom Baranowski; Lora E Burke; Mingui Sun
Journal:  Sensors (Basel)       Date:  2022-02-15       Impact factor: 3.576

3.  COVID-19 and Virtual Nutrition: A Pilot Study of Integrating Digital Food Models for Interactive Portion Size Education.

Authors:  Dang Khanh Ngan Ho; Yu-Chieh Lee; Wan-Chun Chiu; Yi-Ta Shen; Chih-Yuan Yao; Hung-Kuo Chu; Wei-Ta Chu; Nguyen Quoc Khanh Le; Hung Trong Nguyen; Hsiu-Yueh Su; Jung-Su Chang
Journal:  Nutrients       Date:  2022-08-12       Impact factor: 6.706

4.  Performance of the Digital Dietary Assessment Tool MyFoodRepo.

Authors:  Claire Zuppinger; Patrick Taffé; Gerrit Burger; Wafa Badran-Amstutz; Tapio Niemi; Clémence Cornuz; Fabiën N Belle; Angeline Chatelan; Muriel Paclet Lafaille; Murielle Bochud; Semira Gonseth Nusslé
Journal:  Nutrients       Date:  2022-02-01       Impact factor: 5.717

  4 in total

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