ReviewNature food2026
Integration of modern technologies to advance dietary assessment.
Review in Nature food, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Effects of Non-Nutritive Artificial Sweeteners on Gut Microbiota and Host Metabolism and Health-Related Outcomes: A Review.Nutrients · 2026Review
- FoodScribe: an open-source semantic framework for nutrient estimation from free-text dietary records.medRxiv : the preprint server for health sciences · 2026Article
- 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Diet is a key determinant of human and planetary health, but accurately measuring dietary intake remains challenging. Traditional self-reporting tools are imprecise, compromising our ability to accurately link diets with health outcomes. Modern technologies, including smartphone apps, image-based methods and biomarkers of food intake (BFIs), offer promise but bring their own caveats. App- and image-based methods reduce bias and reporting burden, but remain partly self-reported, and are thus prone to errors similar to those of traditional methods. Omics-based BFIs (that is, metabolites, food-related DNA or food proteins) are objective measures derived from biological samples; however, they mostly reflect recent intake, and require careful sampling alignment to estimate habitual diets. Here we discuss the drawbacks and opportunities for all dietary tools and propose strategies to integrate technologies along with multisampling for longitudinal measurements, for a new era in dietary assessment that can clarify the impact of diets, dietary components and dietary behaviour on human and planetary health.
Indexed as
Identifiers
41606178What Socratic 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 Socratic graph.