HHSN261201400052C; TOPIC 308 AUTOMATED COLLECTION, STORAGE, ANALYSIS, AND REPORTING SYSTEMS FOR DIETARY IMAGES;TITLE: DDMP: A SYSTEM FOR AUTOMATED COLLECTION, STORAGE, ANALYSIS, AND REPORTING OF ANNOT

HHSN261201400052C；

• 批准号: 8947292
• 负责人: ALEXEI ZYUZIN
• 金额: $ 99.52万
• 依托单位: ILLIONIX, LLC
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-16 至 2016-09-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8947292
• 关键词: Clinical; Clinical Research; Cloud Computing; Cohort Studies; Collection; Computer software; Data; Data Collection; Databases; Diet; Dietary Intervention; Eating; Food; Goals; Health; Image; Individual; Intake; Measurement; Minor; Modification; Monitor; Nutrient; Nutritional; Output; Paper; Participant; Patients; Phase; Process; Protocols documentation; Records; Reporting; Research Design; Schedule; Series; System; Testing; Training; Validation; computerized data processing; design; improved; mobile application; screening; system architecture; tool; usability; validation studies

This project’s long-term goal is to develop the Dietary Data Monitoring Platform (DDMP): an integrated, scalable system for both clinical and research applications that collects and manages data on an individual’s food intake. DDMP is designed to reduce the burden of collecting multilpe-day records of food intake, improve the accuracy of serving size measurement, and streamline the processes of patient/participant training, scheduling and data processing. The Phase II effort will beta test and finalize DDMP during a series of validation studies. During beta testing, the interface, software, and reporting tools developed in Phase I will be reevaluated for clinical and research applications. Linkages to the NSDR, FNDDS, MPED, and the Gladson Nutritional Database will be used to estimate the energy and nutrient intake associated with the reconstructed food volume. DDMP will utilize a high throughput, low latency data collection framework and intuitive reporting system. The database will utilize cloud computing and storage for remote access, providing much needed improvement over disparate data collection platforms used clinically and in health-related studies. This will enable more efficient nutrition intervention in clinical settings and make possible several types of research designs (e.g., case-cohort studies) that are unfeasible using paper food records.

该项目的长期目标是开发膳食数据监测平台（DSTF）：一个集成的可扩展系统 用于收集和管理个人食物摄入数据的临床和研究应用。多吉里斯 旨在减轻收集多日食物摄入记录的负担，提高食用量的准确性， 测量，并简化患者/参与者培训，调度和数据处理的过程。相位 在一系列的验证研究中，II努力将进行beta测试并最终确定DALVE。在beta测试期间，界面，软件， 在第一阶段开发的报告工具将重新评估临床和研究应用。键联到 NSDR、FNDDS、MPED和Gladson营养数据库将用于估计能量和营养素摄入量 与重建的食物体积相关。DSPs将利用高吞吐量、低延迟的数据收集 框架和直观的报告系统。该数据库将利用云计算和存储进行远程访问， 为临床和健康相关领域使用的不同数据收集平台提供急需的改进 问题研究这将使临床环境中更有效的营养干预成为可能， 研究设计（例如，病例队列研究），这是不可行的使用纸质食品记录。