Rhyner Daniel, Loher Hannah, Dehais Joachim, Anthimopoulos Marios, Shevchik Sergey, Botwey Ransford Henry, Duke David, Stettler Christoph, Diem Peter, Mougiakakou Stavroula
ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
J Med Internet Res. 2016 May 11;18(5):e101. doi: 10.2196/jmir.5567.
Diabetes mellitus is spreading throughout the world and diabetic individuals have been shown to often assess their food intake inaccurately; therefore, it is a matter of urgency to develop automated diet assessment tools. The recent availability of mobile phones with enhanced capabilities, together with the advances in computer vision, have permitted the development of image analysis apps for the automated assessment of meals. GoCARB is a mobile phone-based system designed to support individuals with type 1 diabetes during daily carbohydrate estimation. In a typical scenario, the user places a reference card next to the dish and acquires two images using a mobile phone. A series of computer vision modules detect the plate and automatically segment and recognize the different food items, while their 3D shape is reconstructed. Finally, the carbohydrate content is calculated by combining the volume of each food item with the nutritional information provided by the USDA Nutrient Database for Standard Reference.
The main objective of this study is to assess the accuracy of the GoCARB prototype when used by individuals with type 1 diabetes and to compare it to their own performance in carbohydrate counting. In addition, the user experience and usability of the system is evaluated by questionnaires.
The study was conducted at the Bern University Hospital, "Inselspital" (Bern, Switzerland) and involved 19 adult volunteers with type 1 diabetes, each participating once. Each study day, a total of six meals of broad diversity were taken from the hospital's restaurant and presented to the participants. The food items were weighed on a standard balance and the true amount of carbohydrate was calculated from the USDA nutrient database. Participants were asked to count the carbohydrate content of each meal independently and then by using GoCARB. At the end of each session, a questionnaire was completed to assess the user's experience with GoCARB.
The mean absolute error was 27.89 (SD 38.20) grams of carbohydrate for the estimation of participants, whereas the corresponding value for the GoCARB system was 12.28 (SD 9.56) grams of carbohydrate, which was a significantly better performance ( P=.001). In 75.4% (86/114) of the meals, the GoCARB automatic segmentation was successful and 85.1% (291/342) of individual food items were successfully recognized. Most participants found GoCARB easy to use.
This study indicates that the system is able to estimate, on average, the carbohydrate content of meals with higher accuracy than individuals with type 1 diabetes can. The participants thought the app was useful and easy to use. GoCARB seems to be a well-accepted supportive mHealth tool for the assessment of served-on-a-plate meals.
糖尿病正在全球蔓延，且已表明糖尿病患者常常无法准确评估自己的食物摄入量；因此，开发自动化饮食评估工具迫在眉睫。近期功能增强的手机的普及，再加上计算机视觉技术的进步，使得开发用于自动评估膳食的图像分析应用程序成为可能。GoCARB是一个基于手机的系统，旨在在日常碳水化合物估算过程中为1型糖尿病患者提供支持。在典型场景中，用户将一张参考卡放在餐盘旁边，并用手机拍摄两张照片。一系列计算机视觉模块会检测餐盘，并自动分割和识别不同的食物项目，同时重建它们的三维形状。最后，通过将每个食物项目的体积与美国农业部标准参考营养数据库提供的营养信息相结合来计算碳水化合物含量。
本研究的主要目的是评估1型糖尿病患者使用GoCARB原型时的准确性，并将其与他们自己进行碳水化合物计数的表现进行比较。此外，通过问卷调查评估该系统的用户体验和可用性。
该研究在瑞士伯尔尼大学医院“因塞尔医院”（伯尔尼）进行，纳入了19名1型糖尿病成年志愿者，每人参与一次。每个研究日，从医院餐厅选取总共六顿种类丰富的膳食提供给参与者。食物项目用标准天平称重，并根据美国农业部营养数据库计算碳水化合物的实际含量。要求参与者先独立计算每顿膳食的碳水化合物含量，然后使用GoCARB进行计算。在每个环节结束时，填写一份问卷以评估用户对GoCARB的体验。
参与者估算的碳水化合物平均绝对误差为27.89（标准差38.20）克，而GoCARB系统的相应值为12.28（标准差9.56）克，GoCARB的表现明显更好（P = 0.001）。在75.4%（86/114）的膳食中，GoCARB自动分割成功，85.1%（291/342）的单个食物项目被成功识别。大多数参与者认为GoCARB易于使用。
本研究表明，该系统平均能够比1型糖尿病患者更准确地估算膳食中的碳水化合物含量。参与者认为该应用程序有用且易于使用。GoCARB似乎是一种广受认可的用于评估盘中膳食的移动健康支持工具。
