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

项目概要/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。食品 摄入量包括能量摄入量、营养素摄入量和各种食物组（例如水果、蔬菜）的摄入量， 从而反映个体的营养状况。营养会影响疾病风险，包括罹患疾病的风险 肥胖、糖尿病和癌症，所有这些都对美国产生负面影响。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。自我报告方法 （例如食物记录和饮食回忆）是营养流行病学研究的支柱，但其准确性 受到质疑，部分原因是数据缺失以及人们不准确地估计份量大小和回忆 他们吃什么。过去 15 年在评估食物摄入量方面取得的进展包括技术辅助 方法，包括那些依赖食物摄影的方法。我们小组之前开发了远程食品 摄影法 (RFPM) 和 SmartIntake 应用程序，根据食物图像量化食物摄入量 用户在进食前和进食后进行拍摄。通过这种方法可以准确估计食物摄入量 大多数研究人群和环境，但图像分析需要时间和资源，需要人工 评分者和用户不会立即收到有关其食物摄入量的反馈。我们开发了 PartionSize 智能手机应用程序可以克服这些限制。 PortionSize 应用程序依赖于用户捕获其自己的图像 食物选择和浪费，但它立即为用户提供食物摄入数据。部分大小应用程序 包括创新技术，可最大限度地减少丢失数据并帮助用户准确估计份量大小。 初步数据支持 PortionSize 应用程序的有效性，并且在拟议的研究期间验证了可靠性 PortionSize 和 MyFitnessPal（一种常用的基于智能手机的食物记录）的有效性将得到测试 反对“黄金标准”标准措施。具体来说，这些应用程序将在健康成年人中进行测试 以下三个条件：1）基于实验室的测试膳食（研究1），2）自由生活条件，其中 参与者将从冷藏箱中食用预先称重的食物，以测试能量和营养摄入量 在自由生活条件下（研究 2）和 3）自由生活条件下，能量摄入也通过双重评估 标记水（研究 3）。如果发现有效，PortionSize 应用程序将通过提供 可以广泛且经济地传播的方法，以评估食物摄入量并促进/跟踪遵守情况 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.