ArticlePloS one2013
Developing prediction equations and a mobile phone application to identify infants at risk of obesity.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 3 of them syntheses that pooled it.
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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.
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Who cites it
25 citing papers in PubMed, 3 syntheses or guidelines pooled it, 50 citations in OpenAlex.
- Clinical relevance and validity of tools to predict infant, childhood and adulthood obesity: a systematic review.Public health nutrition · 2018Pooled it
- Predicting childhood overweight and obesity using maternal and early life risk factors: a systematic review.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2018Pooled it
- Systematic review of randomised controlled trials of interventions that aim to reduce the risk, either directly or indirectly, of overweight and obesity in infancy and early childhood.Maternal & child nutrition · 2016Pooled it
- A prediction model for childhood obesity risk based on maternal thyroid status and related parameters using machine learning: a mother-newborn-offspring study in a mild-to-moderate iodine deficiency area.International journal of obesity (2005) · 2026Article
- Support and concerns: Dutch health care professionals' views on the use of a childhood obesity prediction toolbox.BMC pediatrics · 2026Article
- Risk factors and prediction for pediatric obesity: current status and future perspectives.Endocrine journal · 2025Review
- Development of Machine Learning-Based Risk Prediction Models to Predict Rapid Weight Gain in Infants: Analysis of Seven Cohorts.JMIR public health and surveillance · 2025Article
- Predicting obesity at adolescence from an early age in a Dutch observational cohort study: the development and internal validation of a multivariable prediction model.BMC pediatrics · 2025Observational
- Validation of Variables for Use in Pediatric Obesity Risk Score Development in Demographically and Racially Diverse United States Cohorts.The Journal of pediatrics · 2024Article
- Modelling individual infancy growth trajectories to predict excessive gain in BMI z-score: a comparison of growth measures in the ABCD and GECKO Drenthe cohorts.BMC public health · 2023Article
- Obesity Prediction with EHR Data: A deep learning approach with interpretable elements.ACM transactions on computing for healthcare · 2022Article
- Predicting Childhood Obesity Using Machine Learning: Practical Considerations.BioMedInformatics · 2022Article
- Childhood overweight and obesity at the start of primary school: External validation of pregnancy and early-life prediction models.PLOS global public health · 2022Article
- European Childhood Obesity Risk Evaluation (CORE) index based on perinatal factors and maternal sociodemographic characteristics: the Feel4Diabetes-study.European journal of pediatrics · 2021Article
- Association between mode of delivery and body mass index at 4-5 years in White British and Pakistani children: the Born in Bradford birth cohort.BMC public health · 2021Article
- Dynamic prediction model to identify young children at high risk of future overweight: Development and internal validation in a cohort study.Pediatric obesity · 2020Article
- Review
- Predicting the risk of childhood overweight and obesity at 4-5 years using population-level pregnancy and early-life healthcare data.BMC medicine · 2020Article
- Identifying a risk score for childhood obesity based on predictors identified in pregnant women and 1-year-old infants: An analysis of the data of the Hokkaido Study on Environment and Children's Health.Clinical pediatric endocrinology : case reports and clinical investigations : official journal of the Japanese Society for Pediatric Endocrinology · 2019Article
- Predicting childhood obesity using electronic health records and publicly available data.PloS one · 2019Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundAdvancements in knowledge of obesity aetiology and mobile phone technology have created the opportunity to develop an electronic tool to predict an infant's risk of childhood obesity. The study aims were to develop and validate equations for the prediction of childhood obesity and integrate them into a mobile phone application (App). METHODS AND
findingsAnthropometry and childhood obesity risk data were obtained for 1868 UK-born White or South Asian infants in the Born in Bradford cohort. Logistic regression was used to develop prediction equations (at 6 ± 1.5, 9 ± 1.5 and 12 ± 1.5 months) for risk of childhood obesity (BMI at 2 years >91(st) centile and weight gain from 0-2 years >1 centile band) incorporating sex, birth weight, and weight gain as predictors. The discrimination accuracy of the equations was assessed by the area under the curve (AUC); internal validity by comparing area under the curve to those obtained in bootstrapped samples; and external validity by applying the equations to an external sample. An App was built to incorporate six final equations (two at each age, one of which included maternal BMI). The equations had good discrimination (AUCs 86-91%), with the addition of maternal BMI marginally improving prediction. The AUCs in the bootstrapped and external validation samples were similar to those obtained in the development sample. The App is user-friendly, requires a minimum amount of information, and provides a risk assessment of low, medium, or high accompanied by advice and website links to government recommendations.
conclusionsPrediction equations for risk of childhood obesity have been developed and incorporated into a novel App, thereby providing proof of concept that childhood obesity prediction research can be integrated with advancements in technology.
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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.