ReviewInternational journal of obesity (2005)2020
Review of the validity and feasibility of image-assisted methods for dietary assessment.
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.
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.
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.
Who cites it
46 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Effectiveness of Digital Health Interventions on Body Weight and Dietary Intake Outcomes Among Culturally and Linguistically Diverse (CALD) and Indigenous Populations: A Systematic Review.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2026Pooled it
- Best Time of Day for Strength and Endurance Training to Improve Health and Performance? A Systematic Review with Meta-analysis.Sports medicine - open · 2023Pooled it
- AI-based digital image dietary assessment methods compared to humans and ground truth: a systematic review.Annals of medicine · 2023Pooled it
- The validity of measuring energy and nutrient intake with the Remote Food Photography Method (RFPM) in adolescents.The American journal of clinical nutrition · 2026Trial
- Digital and Technology-Enabled Approaches in Dietary Assessment: Addressing Bias, Error, and Feasibility in Population- and Community-Based Research.Advances in nutrition (Bethesda, Md.) · 2026Review
- Development and Construct Validation of a Food-Based Diet Quality Score Using Image-Based Food Records.Nutrients · 2026Article
- Performance of continuous glucose monitoring-based meal detection algorithms in young healthy adults.Scientific reports · 2026Article
- The Feasibility of Smartwatch Micro-Ecological Momentary Assessment for Tracking Eating Patterns of Malaysian Children and Adolescents in the South-East Asian Community Observatory Child Health Update 2020: Cross-Sectional Study.Journal of medical Internet research · 2026Article
- Development of an AI-Driven Computational Framework for Integrated Dietary Pattern Assessment: A Simulation-Based Proof-of-Concept Study.Nutrients · 2026Article
- Reducing bias and enhancing equity in AI-enabled precision nutrition: addressing measurement error across wearables, multiomics, and dietary data.Frontiers in digital health · 2026Review
- Dietary ecological momentary assessments: validation against food diaries and associations with health biomarkers.Frontiers in nutrition · 2026Article
- User Preferences for an Image-Assisted Dietary Recall: Qualitative Study Comparing 3 Dietary Assessment Methods.JMIR human factors · 2025Article
- 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 · 2025Article
- Application of an Ecological Approach to Assessment of Nutrition in School-Aged Children-A Report from the "Biomarkers of Nutrition for Development: Knowledge Indicating Dietary Sufficiency (BOND-KIDS)" Project Working Group 3.The Journal of nutrition · 2025Review
- Performance Evaluation of 3 Large Language Models for Nutritional Content Estimation from Food Images.Current developments in nutrition · 2025Article
- An Image-Voice Dietary Assessment System for Estimating Individual Nutrient Intakes in Cambodian Women and Children: Relative Validity, Reliability, and Acceptability Study.Journal of medical Internet research · 2025Article
- Adaptation of the Remote Food Photography Method to Assess Infant Intake During Bottle-Feeding of Ready-to-Feed Formula.Maternal & child nutrition · 2025Article
- Relative Validity of the Food Recording Smartphone App Libro in Young People Vulnerable to Eating Disorder: A Preliminary Cross-Over Study.Nutrients · 2025Article
- From bytes to bites: application of large language models to enhance nutritional recommendations.Clinical kidney journal · 2025Review
- Identifying Food Preferences and Malnutrition in Older Adults in Care Homes: Co-Design Study of a Digital Nutrition Assessment Tool.JMIR aging · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
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.
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What OpenQuestion holds
Registered trials
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.