Accuracy of using weight and length in children under 24 months to screen for early childhood obesity: A systematic review

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cg.authorship.typesCGIAR and advanced research institute
cg.contributor.affiliationUniversity of South Carolina
cg.contributor.affiliationInternational Food Policy Research Institute
cg.contributor.donorGates Foundation
cg.creator.identifierJef L Leroy: 0000-0001-9371-3832
cg.creator.identifierRebecca Brander: 0000-0002-6156-0373
cg.creator.identifierMarie Ruel: 0000-0002-9506-348X
cg.howPublishedFormally Published
cg.identifier.doihttps://doi.org/10.1016/j.advnut.2025.100452
cg.identifier.projectIFPRI - Nutrition, Diets, and Health Unit
cg.identifier.publicationRankA Plus
cg.isijournalISI Journal
cg.issn2161-8313
cg.journalAdvances in Nutrition
cg.reviewStatusPeer Review
cg.subject.impactAreaNutrition, health and food security
dc.contributor.authorBoncyk, Morgan
dc.contributor.authorLeroy, Jef L.
dc.contributor.authorBrander, Rebecca L.
dc.contributor.authorLarson, Leila M.
dc.contributor.authorRuel, Marie T.
dc.contributor.authorFrongillo, Edward A.
dc.date.accessioned2025-05-30T20:26:01Z
dc.date.available2025-05-30T20:26:01Z
dc.identifier.urihttps://hdl.handle.net/10568/174888
dc.titleAccuracy of using weight and length in children under 24 months to screen for early childhood obesity: A systematic reviewen
dcterms.abstractThe global increase in early childhood overweight and obesity has prompted interest in early prediction of overweight and obesity to allow timely intervention and prevent lifelong consequences. A systematic review was conducted to assess the accuracy and feasibility of predicting overweight and obesity in individual three to seven-year-old children using data available in healthcare and community settings on children under 24 months of age. This review was registered in PROSPERO (CRD42024509603) and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. From 7,943 unique articles identified through PubMed, CINAHL, Scopus, and Google Scholar, 14 studies met the inclusion criteria, 13 from high-income countries and one from a middle-income country. These studies evaluated the accuracy of predicting childhood overweight or obesity in individual children using anthropometrics-alone or multiple-predictor models. Anthropometrics-alone models yielded areas under the curve (AUCs) ≥0.56 with expert guidance and ≥0.77 with machine learning. Multiple-predictor models yielded AUC ≥0.68 with expert guidance and ≥0.76 with machine learning. The inclusion of child, parental, and community predictors improved predictive accuracy but led to greater variation in performance across models. Models were more accurate when children were older at the initial assessment, multiple assessments were made, and the time between assessment and outcome prediction was shorter. Prediction models with an AUC ≥0.70 used machine learning to optimize variable selection, limiting their practicality for broad-scale implementation in healthcare or community settings. There is insufficient evidence on the accuracy of overweight and obesity prediction models for children in low- and middle-income countries. Existing prediction models are not well-suited for broad-scale screening of individual children for risk of early childhood overweight or obesity.en
dcterms.accessRightsOpen Access
dcterms.audienceScientists
dcterms.available2025-05-24
dcterms.bibliographicCitationBoncyk, Morgan; Leroy, Jef L.; Brander, Rebecca L.; Larson, Leila M.; Ruel, Marie T.; and Frongillo, Edward A. Accuracy of using weight and length in children under 24 months to screen for early childhood obesity: A systematic review. Advances in Nutrition. Article in press. First published online May 24, 2025. https://doi.org/10.1016/j.advnut.2025.100452
dcterms.issued2025
dcterms.languageen
dcterms.licenseCC-BY-4.0
dcterms.publisherElsevier
dcterms.relationhttps://doi.org/10.1016/j.advnut.2025.100367
dcterms.subjectanthropometry
dcterms.subjectchildren
dcterms.subjectinfants
dcterms.subjectlength
dcterms.subjectobesity
dcterms.subjectscreening
dcterms.subjectweight
dcterms.typeJournal Article

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