ReviewJournal of biomedical physics & engineering2019
A Systematic Review of Nutrition Recommendation Systems: With Focus on Technical Aspects.
Review in Journal of biomedical physics & engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Pooled it
- Nutritional management recommendation systems in polycystic ovary syndrome: a systematic review.BMC women's health · 2024Pooled it
- A Systematic Review on Food Recommender Systems for Diabetic Patients.International journal of environmental research and public health · 2023Pooled it
- Mapping Digital Nudges and Recommender Systems for Obesity Prevention: Scoping Review.Interactive journal of medical research · 2026Review
- Diet-Related Health Recommender Systems for Patients With Chronic Health Conditions: Scoping Review.Journal of medical Internet research · 2026Article
- Functionality and quality assessment of Iranian nutrition-related apps using the mobile app rating scale.Journal of education and health promotion · 2026Article
- Development of an Evaluation Index System for Health Recommender Systems Based on the Health Technology Assessment Framework: Cross-Sectional Delphi Study.JMIR formative research · 2025Article
- Artificial Intelligence-Driven Recommendations and Functional Food Purchases: Understanding Consumer Decision-Making.Foods (Basel, Switzerland) · 2025Article
- Recommender systems for obesity prevention: Scoping review of reviews.SAGE open medicine · 2025Review
- Impact of machine learning on dietary and exercise behaviors in type 2 diabetes self-management: a systematic literature review.PeerJ. Computer science · 2025Article
- Designing and Evaluating a Nutrition Recommender System for Improving Food Security in a Developing Country.Archives of Iranian medicine · 2023Article
- A fuzzy based dietary clinical decision support system for patients with multiple chronic conditions (MCCs).Scientific reports · 2023Article
- Recommender System Based on Collaborative Filtering for Personalized Dietary Advice: A Cross-Sectional Analysis of the ELSA-Brasil Study.International journal of environmental research and public health · 2022Article
- A Nudge-Inspired AI-Driven Health Platform for Self-Management of Diabetes.Sensors (Basel, Switzerland) · 2022Article
- Something Similar: Exploring the Usefulness of an On-the-Spot Meal Recommendation System for Health Goal Attainment.Extended abstracts on Human factors in computing systems. CHI ConferenceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNutrition informatics has become a novel approach for registered dietitians to practice in this field and make a profit for health care. Recommendation systems considered as an effective technology into aid users to adjust their eating behavior and achieve the goal of healthier food and diet. The purpose of this study is to review nutrition recommendation systems (NRS) and their characteristics for the first time. MATERIAL AND
methodsThe systematic review was conducted using a comprehensive selection of scientific databases as reference sources, allowing access to diverse publications in the field. The process of articles selection was based on the PRISMA strategy. We identified keywords from our initial research, MeSH database and expert's opinion. Databases of PubMed, Web of Sciences, Scopus, Embase, and IEEE were searched. After evaluating, they obtained records from databases by two independent reviewers and inclusion and exclusion criteria were applied to each retrieved work to select those of interest. Finally, 25 studies were included.
resultsHybrid recommender systems and knowledge-based recommender systems with 40% and 32%, respectively, were the mostly recommender types used in NRS. In NRS, rule-based and ontology techniques were used frequently. The frequented platform that applied in NRS was a mobile application with 28%.
conclusionIf NRS was properly designed, implemented and finally evaluated, it could be used as an effective tool to improve nutrition and promote a healthy lifestyle. This study can help to inform specialists in the nutrition informatics domain, which was necessary to design and develop NRS.
Indexed as
Identifiers
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.