Trial reportThe Journal of nutrition2026
Use of Machine Learning to Identify Determinants of Habitual-Preformed Water Intake.
Trial report in The Journal of nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00427193 (Comprehensive Assessment of Long-Term Effects of Reducing Intake of Energy), which is not on this map. Not yet cited in PubMed.
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.
Comprehensive Assessment of Long-Term Effects of Reducing Intake of Energy (CALERIE)
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
backgroundWater intake is vital for health, yet the determinants of preformed water consumption in adults are poorly understood.
objectivesThis study aimed to apply machine learning (ML) models to identify factors associated with preformed water intake, defined as water ingestion from plain water, other beverages, and food.
methodsThis secondary analysis used baseline data from 219 participants in the Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy 2 trial, a randomized controlled trial with extensive measures of body composition, energy expenditure, and dietary, physiological, psychological, and biomarker variables in healthy adults without obesity. Habitual intake of preformed water was quantified using deuterium and oxygen-18 isotope data obtained during 2 consecutive 14-d doubly-labeled water measurement periods of weight stability. We developed models using linear regression, tree-based models (random forest, gradient boosting, and extreme gradient boosting), and penalized regression models (ridge, lasso, and elastic net) to identify factors associated with preformed water intake.
resultsOn the basis of root mean squared error, the ridge regression model using 25 variables was the best and explained 38% of the variance in preformed water intake. Higher preformed water intake was associated with higher intake of dietary fiber, protein, alcohol, total weight of food ingested, and lower intake of carbohydrate and sodium. Higher preformed water intake was also associated with lower percent body fat and higher fat-free mass and total energy expenditure. Notably, ML models identified alcohol and potassium intake as important predictors that were not selected by traditional linear regression, underscoring their ability to capture nuanced relationships.
conclusionsThese results demonstrate that data-driven ML models using a complex dataset can identify features and patterns associated with an important nutrient that might be missed using traditional statistical approaches and could be used to identify individuals at risk of inadequate hydration. This trial was registered as clinicaltrials.gov at NCT00427193.
Indexed as
Identifiers
What 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.