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疾病风险至关重要。食品 摄入包括能量摄入、营养摄入和各种食物组的摄入（例如，水果、蔬菜）， 从而反映个体的营养状况。营养影响疾病风险，包括发展 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， 数十年来，精确地量化食物摄入一直是研究人员和临床医生的挑战。自我报告方法 (e.g.,食物记录和饮食回忆）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回忆 他们吃什么。过去15年来，评估食物摄入量的进展包括技术辅助 方法，包括那些依赖于食物摄影。我们小组以前开发了远程食物 摄影方法（RFPM）和SmartIntake应用程序，该应用程序根据食物图像量化食物摄入量， 使用者在进食前和进食后都能捕捉到。用这种方法可以准确估计食物摄入量， 大多数研究人群和设置，但图像的分析需要时间和资源， 评分员，并且用户不会立即收到有关其食物摄入量的反馈。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捉他们的图像， 食物选择和浪费，但它立即为用户提供食物摄入数据。PortionSize应用程序 包括创新的技术，以尽量减少丢失的数据，并帮助用户准确地估计部分大小。 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， 以及常用的基于智能手机的食物记录的有效性将被测试 反对“金本位”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）基于实验室的测试餐（研究1），2）自由生活条件，其中 参与者将从冷却器中食用预先称重的食物，该冷却器提供能量和营养摄入测试 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 标签水（研究3）。如果发现有效，则PortionSize应用程序将通过提供 可以广泛和负担得起的方法传播，以评估食物摄入量和促进/跟踪遵守 真实的个性化饮食。

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.