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PLoS One. 2013 Aug 7;8(8):e71183. doi: 10.1371/journal.pone.0071183. Print 2013.

Developing prediction equations and a mobile phone application to identify infants at risk of obesity.

Author information

1
Bradford Institute for Health Research, Bradford Royal Infirmary, Bradford, United Kingdom. Gillian.Santorelli@bthft.nhs.uk

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 >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.

PMID:
23940713
PMCID:
PMC3737139
DOI:
10.1371/journal.pone.0071183
[Indexed for MEDLINE]
Free PMC Article

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