Evaluation of the validity of the PortionSize app in controlled and free-living conditions: Tests of an app that calculates food intake and provides immediate feedback to users

项目摘要/摘要 准确量化食物摄入量对于促进健康和降低慢性病风险至关重要。食物 摄入量包括能量摄入量、营养素摄入量和各种食物类别(如水果、蔬菜)的摄入量， 从而反映个体的营养状况。营养影响疾病风险，包括发病风险 肥胖、糖尿病和癌症，所有这些都对美国(美国)产生了负面影响。尽管它很重要， 几十年来，准确量化食物摄入量一直是研究人员和临床医生面临的挑战。自我报告法 (例如，食物记录和饮食召回)是营养流行病学研究的支柱，但它们的准确性 被质疑，部分原因是数据缺失和人们不准确地估计部分大小和召回 他们吃了什么。过去15年在评估食物摄入量方面的进展包括技术辅助 方法，包括那些依赖食物摄影的方法。我们的团队之前开发了远程食物 照相法(RFPM)和SmartIntake应用程序，它基于食物图像来量化食物摄入量 用户在进食前和进食后都会抓拍。用这种方法可以准确估计食物的摄入量。 大多数研究人群和环境，然而图像分析需要时间和资源，需要人类 而且用户不会收到关于他们食物摄入量的即时反馈。我们开发了PortionSize 智能手机应用程序来克服这些限制。PortionSize应用程序依赖于用户捕获其 食物选择和浪费，但它立即向用户提供食物摄入量数据。PortionSize应用程序 包括创新技术，以最大限度地减少丢失的数据，并帮助用户准确估计部分大小。 初步数据支持PortionSize应用程序的有效性，并在拟议的研究中验证了可靠性 并将测试PortionSize和MyFitnessPal的有效性，这是一种常用的基于智能手机的食物记录 反对“黄金标准”的标准措施。具体地说，这些应用程序将在健康成年人身上进行测试， 以下三个条件：1)基于实验室的试餐(研究1)，2)自由生活条件，其中 参与者将从冷却器中摄入预先称重的食物，这可以测试能量和营养的摄入量 在自由生活条件下(研究2)，以及3)自由生活条件下，能量摄入量也通过双重评估 标记水(研究3)。如果发现有效，则PortionSize应用程序将通过提供 可广泛和可负担得起地传播的方法，以评估食物摄入量并促进/跟踪遵守 实时个性化饮食。

Project Summary / Abstract Accurately quantifying food intake is vital to promoting health and reducing chronic disease risk. Food intake encompasses energy intake, nutrient intake, and intake of various food groups (e.g., fruits, vegetables), and thus reflects the nutritional status of individuals. Nutrition affects disease risk, including risk of developing obesity, diabetes, and cancer, all of which negatively affect the United States (U.S). Despite its importance, accurately quantifying food intake has challenged researchers and clinicians for decades. Self-report methods (e.g., food records and diet recall) are a mainstay of nutritional epidemiology research, but their accuracy has been questioned, due, in part, to missing data and people inaccurately estimating portion size and recalling what they ate. Advances in assessing food intake over the past 15 years include technology-assisted approaches, including those that rely on food photography. Our group previously developed the Remote Food Photography Method (RFPM) and SmartIntake app, which quantifies food intake based on food images that users capture before and after they eat. Accurate estimates of food intake are obtained with this method in most study populations and settings, yet analysis of the images takes time and resources, requires a human rater, and users do not receive immediate feedback about their food intake. We developed the PortionSize smartphone app to overcome these limitations. The PortionSize app relies on users capturing images of their food selection and waste, but it immediately provides users with food intake data. The PortionSize app includes innovative technology to minimize missing data and to help users accurately estimate portion size. Preliminary data supports the validity of the PortionSize app, and during the proposed research the reliability and validity of PortionSize and MyFitnessPal, a commonly used smartphone-based food record, will be tested against `gold-standard' criterion measures. Specifically, the apps will be tested in healthy adults under the following three conditions: 1) laboratory-based test meals (Study 1), 2) free-living conditions, where participants will consume pre-weighed food from a cooler, which provides a test of energy and nutrient intake in free-living conditions (Study 2), and 3) free-living conditions, where energy intake is also assessed by doubly labeled water (Study 3). If found to be valid, the PortionSize app will move the field forward by providing a method that could widely and affordably be disseminated to assess food intake and foster/track adherence to personalized diets in real time.