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Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study

机译:基于手机的系统中的碳水化合物估计与1型糖尿病患者的自我估计的比较研究

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摘要

Background: 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.ududObjective: 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.ududMethods: 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.ududResults: 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.ududConclusions: 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.
机译:背景:糖尿病在世界范围内蔓延,糖尿病患者经常被错误地评估其食物摄入量。因此,迫切需要开发自动化饮食评估工具。具有增强功能的移动电话的最新可用性以及计算机视觉的进步,已经允许开发用于自动评估餐食的图像分析应用程序。 GoCARB是一种基于手机的系统,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。在典型情况下,用户将参考卡放在盘子旁边,并使用移动电话获取两个图像。一系列计算机视觉模块可检测盘子并自动分割并识别不同的食物,同时重建其3D形状。最后,通过将每种食品的体积与美国农业部营养数据库提供的营养信息(标准参考)相结合来计算碳水化合物含量。 ud ud目的:本研究的主要目的是在以下情况下评估GoCARB原型的准确性:由1型糖尿病患者使用,并将其与自己在碳水化合物计数方面的表现进行比较。此外,该系统的用户体验和可用性还通过问卷进行了评估。 ud ud方法:该研究是在瑞士伯尔尼大学医院(Inselspital)(瑞士伯尔尼)进行的,涉及19位1型糖尿病成人志愿者。参加一次。每个学习日,从医院的餐厅中取六道菜,种类繁多,呈现给参与者。用标准天平称重食品,并从USDA营养数据库中计算出碳水化合物的真实含量。要求参与者分别计算每餐的碳水化合物含量,然后使用GoCARB。在每个会话的末尾,将完成一份问卷以评估用户对GoCARB的使用体验。 ud ud结果:用于评估参与者的碳水化合物的平均绝对误差为27.89(SD 38.20)克,而GoCARB的相应值为系统中含有12.28(SD 9.56)克的碳水化合物,其性能明显更好(P = .001)。在75.4%(86/114)的餐点中,GoCARB自动分割成功,并且成功识别了85.1%(291/342)的单个食品。大多数参与者发现GoCARB易于使用。 ud ud结论:这项研究表明,该系统能够平均估计餐点中的碳水化合物含量,其准确性高于1型糖尿病患者。参加者认为该应用程序有用且易于使用。 GoCARB似乎是一种公认​​的支持mHealth的工具,用于评估餐后用餐。

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