ArticleEuropean journal of investigation in health, psychology and education2025
Explanatory AI Predicts the Diet Adopted Based on Nutritional and Lifestyle Habits in the Spanish Population.
Article in European journal of investigation in health, psychology and education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 2 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
3 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine Learning for the Analysis of Healthy Lifestyle Data: Scoping Review and Guidelines.JMIR human factors · 2026Guideline
- Artificial Intelligence in the Management of Malnutrition in Cancer Patients: A Systematic Review.Advances in nutrition (Bethesda, Md.) · 2025Pooled it
- Methodological Review of Classification Trees for Risk Stratification: An Application Example in the Obesity Paradox.Nutrients · 2025Review
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
No grant is acknowledged in the PubMed record.
Abstract
This study used Explainable Artificial Intelligence (XAI) with SHapley Additive exPlanations (SHAP) to examine dietary and lifestyle habits in the Spanish population and identify key diet predictors. A cross-sectional design was used, employing the validated NutSo-HH scale to gather data on nutrition, lifestyle, and socio-demographic factors. The CatBoost method combined with SHAP was applied. The sample included 22,181 Spanish adults: 17,573 followed the Mediterranean diet, 1425 were vegetarians, 365 were vegans, and 1018 practiced intermittent fasting. Fish consumption was the strongest dietary indicator, with vegans abstaining and some vegetarians consuming it occasionally. Age influenced diet: younger individuals preferred vegan/vegetarian diets, while older adults adhered to the Mediterranean diet. Vegans and vegetarians consumed less junk food, and intermittent fasters were more physically active. The model effectively predicts the Mediterranean diet but struggles with others due to sample imbalance, highlighting the need for larger studies on plant-based and intermittent fasting diets.
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.