Evidence map›Paper›PMID 33033394›Full record

ReviewInternational journal of obesity (2005)2020

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

Christoph Höchsmann, Corby K Martin

Abstract readReview
In one paragraph

Review in International journal of obesity (2005), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

46 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
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  13. Validity of Weighed and Digital Photography Diet Estimation Study in a Childcare Setting.Journal of human nutrition and dietetics : the official journal of the British Dietetic Association · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Christoph HöchsmannPennington Biomedical Research Center, Baton Rouge, LA, USA.ORCID http://orcid.org/0000-0003-2007-3007
Corby K MartinPennington Biomedical Research Center, Baton Rouge, LA, USA. Corby.Martin@pbrc.edu.ORCID http://orcid.org/0000-0002-8125-4015

Funding

Tracking & Evaluation CoreU54GM104940 · NIGMS · LSU PENNINGTON BIOMEDICAL RESEARCH CTR · PI Peter Todd Katzmarzyk · 2012 to 2026
$69.1M
Research BaseP30DK072476 · NIDDK · LSU PENNINGTON BIOMEDICAL RESEARCH CTR · PI ROBERT A KESTERSON · 2005 to 2026
$26.5M
Training in Obesity ResearchT32DK064584 · NIDDK · LSU PENNINGTON BIOMEDICAL RESEARCH CTR · PI CORBY K MARTIN, Christopher D Morrison · 2003 to 2026
$5.3M
NIDDK NIH HHS P30 DK072476NIDDK NIH HHS T32 DK064584NIGMS NIH HHS U54 GM104940
6 · The paper itself

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.

Indexed as

Nutrition AssessmentPhotographyPortion SizeDiet RecordsEatingEnergy IntakeFeasibility StudiesHumansMobile ApplicationsReproducibility of Results

Identifiers

PMID33033394
PMCPMC7686022

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.