Bradford Institute for Health Research, Bradford Royal Infirmary, Bradford, United Kingdom.
PLoS One. 2013 Aug 7;8(8):e71183. doi: 10.1371/journal.pone.0071183. Print 2013.
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).
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.
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.
肥胖病因学和移动电话技术的进步为开发一种电子工具来预测婴儿儿童肥胖的风险提供了机会。本研究旨在开发和验证预测儿童肥胖的方程，并将其整合到一个移动电话应用程序（App）中。
在布拉德福德出生队列中，对 1868 名英国出生的白种人或南亚人婴儿的人体测量和儿童肥胖风险数据进行了研究。使用逻辑回归来开发预测方程（在 6±1.5、9±1.5 和 12±1.5 个月时），用于预测儿童肥胖的风险（BMI 在 2 岁时>91 百分位数，体重从 0 到 2 岁时>1 百分位带宽），将性别、出生体重和体重增长作为预测指标。通过曲线下面积（AUC）评估方程的判别准确性；通过将 AUC 与 bootstrap 样本中获得的 AUC 进行比较来评估内部有效性；通过将方程应用于外部样本来评估外部有效性。构建了一个 App，其中包含六个最终方程（每个年龄两个，其中一个包括母亲 BMI）。这些方程具有良好的判别能力（AUC 为 86-91%），添加母亲 BMI 可略微提高预测能力。在 bootstrap 和外部验证样本中的 AUC 与在开发样本中获得的 AUC 相似。该应用程序易于使用，需要的信息量最少，并提供低、中、高风险的风险评估，并附有建议和政府建议的网站链接。
已经开发出用于预测儿童肥胖风险的预测方程，并将其整合到一个新的 App 中，从而证明了儿童肥胖预测研究可以与技术进步相结合的概念。
