Literature DB >> 25441955

Image-assisted dietary assessment: a systematic review of the evidence.

Luke Gemming, Jennifer Utter, Cliona Ni Mhurchu.   

Abstract

Images captured during eating episodes provide objective information to assist in the assessment of dietary intake. Images are captured using handheld devices or wearable cameras, and can support traditional self-report or provide the primary record of dietary intake. A diverse range of image-assisted methods have been developed and evaluated but have not been previously examined together. Therefore, a review was undertaken to examine all studies that have evaluated or validated image-assisted methods of dietary assessment for assessing dietary energy intake. Identified image-assisted methods that employ similar methodologies were grouped for comparison. English-language full-text research articles published between January 1998 and November 2013 were searched using five electronic databases. A search of reference lists and associated websites was also conducted. Thirteen studies that evaluated 10 unique image-assisted methods among adults aged 18 to 70 years were included. Ten studies used handheld devices and three studies used wearable cameras. Eight studies evaluated image-based food records, two studies explored the use of images to enhance written food records, and three studies evaluated image-assisted 24-hour dietary recalls. Results indicate images enhance self-report by revealing unreported foods and identify misreporting errors not captured by traditional methods alone. Moreover, when used as the primary record of dietary intake, images can provide valid estimates of energy intake. However, image-assisted methods that rely on image analysis can be prone to underestimation if users do not capture images of satisfactory quality before all foods are consumed. Further validation studies using criterion measures are warranted. The validity among children, adolescents, and elderly persons as well as the feasibility of using image-assisted methods in large samples needs to be examined. Additional research is also needed to better understand the potential applications and pitfalls of wearable cameras.
Copyright © 2015 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Dietary assessment; Handheld computers; Image analysis; Nutrition assessment; Wearable cameras

Mesh:

Year:  2014        PMID: 25441955     DOI: 10.1016/j.jand.2014.09.015

Source DB:  PubMed          Journal:  J Acad Nutr Diet        ISSN: 2212-2672            Impact factor:   4.910


  82 in total

1.  Dietary under-reporting: what foods and which meals are typically under-reported?

Authors:  L Gemming; C Ni Mhurchu
Journal:  Eur J Clin Nutr       Date:  2015-12-16       Impact factor: 4.016

2.  Reliability and Validity of Digital Imagery Methodology for Measuring Starting Portions and Plate Waste from School Salad Bars.

Authors:  Melanie K Bean; Hollie A Raynor; Laura M Thornton; Alexandra Sova; Mary Dunne Stewart; Suzanne E Mazzeo
Journal:  J Acad Nutr Diet       Date:  2018-04-12       Impact factor: 4.910

Review 3.  Proceedings from the 2018 Association for Chemoreception Annual Meeting Symposium: Bariatric Surgery and Its Effects on Taste and Food Selection.

Authors:  Alan C Spector; Natasha Kapoor; Ruth K Price; M Yanina Pepino; M Barbara E Livingstone; Carel W Le Roux
Journal:  Chem Senses       Date:  2019-03-11       Impact factor: 3.160

Review 4.  Automatic, wearable-based, in-field eating detection approaches for public health research: a scoping review.

Authors:  Brooke M Bell; Ridwan Alam; Nabil Alshurafa; Edison Thomaz; Abu S Mondol; Kayla de la Haye; John A Stankovic; John Lach; Donna Spruijt-Metz
Journal:  NPJ Digit Med       Date:  2020-03-13

5.  Dietary Assessment with a Wearable Camera among Children: Feasibility and Intercoder Reliability.

Authors:  Alicia Beltran; Hafza Dadabhoy; Courtney Ryan; Ruchita Dholakia; Wenyan Jia; Janice Baranowski; Mingui Sun; Tom Baranowski
Journal:  J Acad Nutr Diet       Date:  2018-08-13       Impact factor: 4.910

Review 6.  Is a Picture Worth a Thousand Words? Few Evidence-Based Features of Dietary Interventions Included in Photo Diet Tracking Mobile Apps for Weight Loss.

Authors:  Sarah Hales; Caroline Dunn; Sara Wilcox; Gabrielle M Turner-McGrievy
Journal:  J Diabetes Sci Technol       Date:  2016-11-01

7.  Perspective: Randomized Controlled Trials Are Not a Panacea for Diet-Related Research.

Authors:  James R Hébert; Edward A Frongillo; Swann A Adams; Gabrielle M Turner-McGrievy; Thomas G Hurley; Donald R Miller; Ira S Ockene
Journal:  Adv Nutr       Date:  2016-05-16       Impact factor: 8.701

Review 8.  Technology Interventions to Manage Food Intake: Where Are We Now?

Authors:  Margaret Allman-Farinelli; Luke Gemming
Journal:  Curr Diab Rep       Date:  2017-09-23       Impact factor: 4.810

9.  The Healthy Cooking Index: Nutrition Optimizing Home Food Preparation Practices across Multiple Data Collection Methods.

Authors:  Margaret Raber; Tom Baranowski; Karla Crawford; Shreela V Sharma; Vanessa Schick; Christine Markham; Wenyan Jia; Mingui Sun; Emily Steinman; Joya Chandra
Journal:  J Acad Nutr Diet       Date:  2020-04-09       Impact factor: 4.910

10.  Development and Validation of an Objective, Passive Dietary Assessment Method for Estimating Food and Nutrient Intake in Households in Low- and Middle-Income Countries: A Study Protocol.

Authors:  Modou L Jobarteh; Megan A McCrory; Benny Lo; Mingui Sun; Edward Sazonov; Alex K Anderson; Wenyan Jia; Kathryn Maitland; Jianing Qiu; Matilda Steiner-Asiedu; Janine A Higgins; Tom Baranowski; Peter Olupot-Olupot; Gary Frost
Journal:  Curr Dev Nutr       Date:  2020-02-07
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