UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Deposit your research

  Please note!

UCL Discovery download statistics are currently being regenerated.

We estimate that this process will complete on or before Mon 06-Jul-2020. Until then, reported statistics will be incomplete.

Developing Prediction Equations and a Mobile Phone Application to Identify Infants at Risk of Obesity

Santorelli, G; Petherick, ES; Wright, J; Wilson, B; Samiei, H; Cameron, N; Johnson, W; (2013) Developing Prediction Equations and a Mobile Phone Application to Identify Infants at Risk of Obesity. PLoS ONE , 8 (8) , Article e71183 . 10.1371/journal.pone.0071183. Green open access

[img] PDF
journal.pone.0071183-2.pdf

Download (277kB)

Abstract

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 >91st 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.

Type: Article
Title: Developing Prediction Equations and a Mobile Phone Application to Identify Infants at Risk of Obesity
Open access status: An open access version is available from UCL Discovery
DOI: 10.1371/journal.pone.0071183
Publisher version: http://dx.doi.org/10.1371/journal.pone.0071183
Language: English
Additional information: © 2013 Santorelli et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science
URI: https://discovery.ucl.ac.uk/id/eprint/1425203
Downloads since deposit
40Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item