WW International, Inc., New York, NY 10100, USA.
Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32610, USA.
Nutrients. 2023 Feb 4;15(4):803. doi: 10.3390/nu15040803.
Obesity and diabetes have emerged as an increasing threat to public health, and the consumption of added sugar can contribute to their development. Though nutritional content information can positively influence consumption behavior, added sugar is not currently required to be disclosed in all countries. However, a growing proportion of the world's population has access to mobile devices, which allow for the development of digital solutions to support health-related decisions and behaviors. To test whether advances in computational science can be leveraged to develop an accurate and scalable model to estimate the added sugar content of foods based on their nutrient profile, we collected comprehensive nutritional information, including information on added sugar content, for 69,769 foods. Eighty percent of this data was used to train a gradient boosted tree model to estimate added sugar content, while 20% of it was held out to assess the predictive accuracy of the model. The performance of the resulting model showed 93.25% explained variance per default portion size (84.32% per 100 kcal). The mean absolute error of the estimate was 0.84 g per default portion size (0.81 g per 100 kcal). This model can therefore be used to deliver accurate estimates of added sugar through digital devices in countries where the information is not disclosed on packaged foods, thus enabling consumers to be aware of the added sugar content of a wide variety of foods.
肥胖和糖尿病已成为公众健康日益严重的威胁，而添加糖的摄入可能导致肥胖和糖尿病的发生。尽管营养成分信息可以积极影响消费行为，但目前并非所有国家都要求披露添加糖。然而，越来越多的世界人口可以使用移动设备，这为开发数字解决方案以支持与健康相关的决策和行为提供了可能。为了检验计算科学的进步是否可以被利用来开发一种准确且可扩展的模型，根据食物的营养成分来估算添加糖的含量，我们收集了 69769 种食物的综合营养信息，包括添加糖含量的信息。该数据的 80%用于训练梯度提升树模型来估算添加糖的含量，而 20%的数据则用于评估模型的预测准确性。由此产生的模型的性能显示，默认份量（每份 84.32 卡路里）下的解释方差为 93.25%，默认份量（每份 100 卡路里）下的解释方差为 84.32%。该模型可以通过数字设备在没有披露包装食品添加糖信息的国家提供准确的添加糖估算值，从而使消费者能够了解各种食品的添加糖含量。
