ArticleScientific reports2021
A prediction model for childhood obesity in New Zealand.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 17 citations in OpenAlex.
- Predicting childhood overweight and obesity at school entrance using healthcare, demographic, and socioeconomic data in Wales, UK.European journal of public health · 2026Article
- Evidence for a perinatal origin of childhood obesity: a systematic review and meta-analysis of risk and protective factors.Osong public health and research perspectives · 2026Article
- 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
- A prediction model for childhood obesity risk using the machine learning method: a panel study on Korean children.Scientific reports · 2023Article
- Longitudinal body mass index trajectories at preschool age: children with rapid growth have differential composition of the gut microbiota in the first year of life.International journal of obesity (2005) · 2022Article
- A Precision Health Service for Chronic Diseases: Development and Cohort Study Using Wearable Device, Machine Learning, and Deep Learning.IEEE journal of translational engineering in health and medicine · 2022Article
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Authors and funding
12 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Several early childhood obesity prediction models have been developed, but none for New Zealand's diverse population. We aimed to develop and validate a model for predicting obesity in 4-5-year-old New Zealand children, using parental and infant data from the Growing Up in New Zealand (GUiNZ) cohort. Obesity was defined as body mass index (BMI) for age and sex ≥ 95th percentile. Data on GUiNZ children were used for derivation (n = 1731) and internal validation (n = 713). External validation was performed using data from the Prevention of Overweight in Infancy Study (POI, n = 383) and Pacific Islands Families Study (PIF, n = 135) cohorts. The final model included: birth weight, maternal smoking during pregnancy, maternal pre-pregnancy BMI, paternal BMI, and infant weight gain. Discrimination accuracy was adequate [AUROC = 0.74 (0.71-0.77)], remained so when validated internally [AUROC = 0.73 (0.68-0.78)] and externally on PIF [AUROC = 0.74 [0.66-0.82)] and POI [AUROC = 0.80 (0.71-0.90)]. Positive predictive values were variable but low across the risk threshold range (GUiNZ derivation 19-54%; GUiNZ validation 19-48%; and POI 8-24%), although more consistent in the PIF cohort (52-61%), all indicating high rates of false positives. Although this early childhood obesity prediction model could inform early obesity prevention, high rates of false positives might create unwarranted anxiety for families.
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