# Long Term Predictive Models of Childhood Obesity

> **NIH NIH P20** · UNIVERSITY OF DELAWARE · 2020 · $136,006

## Abstract

ABSTRACT
Childhood obesity is a major public health problem across the globe as well as in the US. In 2019, the prevalence
of obesity was 18.5% affecting almost 13.7 million US children and adolescents aged 18 or less, with children of
lower socioeconomic status and from rural areas affected in a disparate manner. Childhood obesity often 
continues into adulthood and is known to be a major risk factor for chronic diseases such as diabetes, cancer, and
cardiovascular diseases. Decades of rigorous research have shown that prevention and management of obesity
are not easy. This is partly due to our limited understating of obesity and the complex interactions among a
myriad of various factors, including biological and environmental ones, that are known to contribute to obesity. In
such a complex domain, advanced predictive models are effective in informing decision-makers and providers
in designing and delivering more effective interventions. While various predictive models have been developed
for childhood obesity, existing models have been created using small populations, are only good for a specific
age prediction, and most importantly, do not use the temporal data patterns of body weight changes across time.
To address this important gap in the field, a set of predictive models of childhood obesity using a longitudinal
dataset of children derived from the electronic health records (EHR) of a large pediatric healthcare system
(Nemours) are being developed by the investigators of this project. Building on our progress in developing 
preliminary models, the current project pursues two main aims to expand these models. The first is improving the
performance of these models by including additional variables related to family SES (socioeconomic status).
While the current models include several SES factors such as the type of insurance, additional SES factors,
including education and income levels and rurality, will be collected to be incorporated in the model and to inform
applicability of the model to the underserved populations targeted by the IDeA States Pediatric Clinical Trials
Network. The second aim is to extend the prediction duration of the models from current short-term (one to three
years) windows of prediction to long-term (up to 20 years) prediction windows. Including additional SES factors
is expected to improve the performance of the models and lengthening the prediction windows will make them
more practical and useful in real settings, including identification of children who would most benefit from future
clinical trials.
Successful expansion of these predictive models will offer powerful tools that can inform both prevention and
treatment interventions. Specifically, by identifying children at a higher risk of developing obesity, these 
predictions can facilitate engaging in appropriate interventions at earlier ages. Additionally, by offering an estimate of
the disease severity for a current patient and relevant risk factors, the mode...

## Key facts

- **NIH application ID:** 10154077
- **Project number:** 3P20GM103446-20S1
- **Recipient organization:** UNIVERSITY OF DELAWARE
- **Principal Investigator:** Steven J. Stanhope
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $136,006
- **Award type:** 3
- **Project period:** 2001-09-30 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10154077

## Citation

> US National Institutes of Health, RePORTER application 10154077, Long Term Predictive Models of Childhood Obesity (3P20GM103446-20S1). Retrieved via AI Analytics 2026-06-10 from https://api.ai-analytics.org/grant/nih/10154077. Licensed CC0.

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