Related Experiment Video
Updated: May 8, 2026
Clinical Anthropometrics and Body Composition from 3-Dimensional Optical Imaging
Published on: June 7, 2024
Developing prediction equations and a mobile phone application to identify infants at risk of obesity
Gillian Santorelli1, Emily S Petherick, John Wright
1Bradford Institute for Health Research, Bradford Royal Infirmary, Bradford, United Kingdom. Gillian.Santorelli@bthft.nhs.uk
Insights
Researchers developed and validated equations to predict childhood obesity risk in infants using mobile technology. The user-friendly app provides risk assessments and advice, integrating technological advancements with health research.
Area of Science:
- Pediatrics
- Public Health
- Biostatistics
Background:
- Childhood obesity is a growing concern.
- Mobile technology offers new avenues for health interventions.
- Predictive tools can aid early intervention strategies.
Purpose of the Study:
- To develop and validate prediction equations for infant risk of childhood obesity.
- To integrate these equations into a mobile phone application (App).
Main Methods:
- Logistic regression used to develop prediction equations for childhood obesity risk at 6, 9, and 12 months.
- Equations incorporated infant sex, birth weight, and weight gain.
- Discrimination accuracy assessed using Area Under the Curve (AUC) and validated internally and externally.
- A user-friendly mobile App was developed to incorporate the final equations.
Main Results:
- Prediction equations demonstrated good discrimination (AUCs 86-91%).
- Inclusion of maternal BMI marginally improved prediction accuracy.
- Internal and external validation confirmed equation reliability.
- The App provides user-friendly risk assessments (low, medium, high) with actionable advice.
Conclusions:
- Novel prediction equations for childhood obesity risk have been successfully developed and integrated into a mobile App.
- This demonstrates the feasibility of merging obesity prediction research with mobile technology.
- The App serves as a proof of concept for technology-enhanced public health tools.
Background:
Advancements 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 Findings:
Anthropometry 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.
Conclusions:
Prediction 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.
