iPAT:Intelligent Diet Quality Pattern Analysis for Harmonized MA-National Trials

iPAT：用于协调 MA 国家试验的智能饮食质量模式分析

• 批准号: 10640972
• 负责人: Hua Fang
• 金额: $ 64.97万
• 依托单位: UNIVERSITY OF MASSACHUSETTS DARTMOUTH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10640972
• 关键词: Address; Advisory Committees; Age; Algorithms; Behavior; Behavioral; Chronic; Chronic Disease; Clinical; Cognitive; Communities; Complex; Computing Methodologies; Coronary Artery Risk Development in Young Adults Study; Data; Data Collection; Data Set; Databases; Diabetes Mellitus; Diet; Dietary Practices; Disease; Disease Outcome; Eating; Ethnic Origin; Funding; Gender; Geographic Locations; Goals; Growth; Health; Heterogeneity; Human; Incidence; Individual; Infrastructure; Intelligence; Learning; Literature; Longitudinal Studies; Longitudinal, observational study; Massachusetts; Measures; Methods; National Heart, Lung, and Blood Institute; National Institute of Diabetes and Digestive and Kidney Diseases; National Institute of Drug Abuse; National Institute of Mental Health; Neighborhood Health Center; Nutrient; Obesity; Observational Study; Outcome; Patients; Pattern; Pattern Recognition; Personal Satisfaction; Persons; Physical activity; Process; Psychological Factors; Randomized, Controlled Trials; Recommendation; Regional Disease; Research Design; Risk; Safety; Site; Software Tools; Testing; Time; Validation; Variant; Visualization; Women' s Health; Work; adaptive intervention; artificial intelligence algorithm; data harmonization; data management; depressive symptoms; design; detection method; dietary; dietary guidelines; evidence base; field study; geographic difference; improved; indexing; innovation; outcome prediction; prevent; public health relevance; randomized, controlled study; response; simulation; social media; sociodemographics; statistical learning; tool; treatment effect; user-friendly

PROJECT SUMMARY A decade ago, the U.S. Dietary Guidelines Advisory Committee recommended dietary pattern approaches to examine relationships between diet and health outcomes. Meanwhile, longitudinal dietary data have become increasingly available. However, methods are underdeveloped for characterizing dynamic diet-quality variations and remain rudimentary for validating longitudinal diet-quality patterns, thus, leading to unclear evidence for assessing diet-health relationships and formulating dietary guidelines. A noticeable gap exists between the dietary pattern literature and the fast-growing statistical learning field with explosive growth of artificial intelligence algorithms. We propose to develop “iPAT:Intelligent Diet Quality Pattern Analysis for Harmonized MA-National Trials”. iPAT will leverage original and newly harmonized dietary data generated from 7 studies funded by NIDDK, NHLBI, and NIMH: 4 longitudinal randomized controlled trials (RCT) in Massachusetts (MA), and 3 large-scale longitudinal multi-site national studies, an RCT and one observational study (OS) from the Women’s Health Initiative (WHI), and one OS from the Coronary Artery Risk Development in Young Adults (CARDIA) study. We aim to harness over 20 newly-harmonized dietary datasets from these highly-comparable longitudinal studies that span up to 35 years and cross 50 clinical and health community centers to: 1) innovate by adapting our new visualization-aided trajectory pattern-recognition and validation algorithm to an intelligent and streamlined pattern analysis tool (iPAT) for longitudinal dietary data; 2) enable a new multi-view and comprehensive understanding of diet-quality trajectory patterns for multiple chronic disease outcomes that may not be discoverable from individual studies at different levels of granularity; and 3) create an accessible and expandable harmonized dietary database and open-access iPAT tool for diet-related studies. Our harmonized-data-driven approach will increase the likelihood of successfully addressing complex and subtle questions with large-scale dietary data, including but not limited to the cultural, age, gender and geographic variation in diet quality patterns and how diet quality may vary with context and time. Our iPAT approach will be built upon PI Fang’s behavioral trajectory pattern-recognition method which has been validated and replicated in five NIDA/NCI/NHLBI-funded longitudinal OS and RCTs. Developing this evidence- based iPAT tool will contribute to the infrastructure for diet-related studies, advance pattern-recognition methods, help scientific communities and the public to compare individual dietary behavior with local and national diet- quality patterns and associated dietary health risks. Our work will also help grow more valid evidence for dietary guidelines. More broadly, this iPAT project will contribute to creating a platform that supports harmonized data management, near-real-time pattern analyses and adaptive interventions.

项目摘要 十年前，美国膳食指南咨询委员会推荐了饮食模式方法， 来研究饮食和健康结果之间的关系。与此同时，纵向饮食数据显示， 变得越来越可用。然而，用于表征动态膳食质量的方法还不发达 变化，并保持基本的验证纵向饮食质量模式，因此，导致不清楚 评估饮食与健康关系和制定膳食指南的证据。存在明显的差距 在饮食模式文献和快速增长的统计学习领域之间， 人工智能算法我们建议开发“iPAT：智能饮食质量模式分析， 协调千年评估-国家试验”。iPAT将利用生成的原始和新协调的饮食数据 来自NIDDK、NHLBI和NIMH资助的7项研究：4项纵向随机对照试验（RCT） 马萨诸塞州（MA），以及3项大规模纵向多中心国家研究、1项RCT和1项观察性研究 来自妇女健康倡议（WHI）的一项研究（OS）和来自冠状动脉风险发展的一项OS 年轻人（CARDIA）研究我们的目标是利用20多个新协调的饮食数据集， 高度可比的纵向研究，跨度长达35年，跨越50个临床和健康社区 中心：1）通过调整我们新的可视化辅助轨迹模式识别和验证进行创新 算法，以智能和精简的模式分析工具（iPAT）的纵向饮食数据; 2）使 一个新的多角度和全面的理解饮食质量的轨迹模式，为多种慢性 可能无法从不同粒度水平的个体研究中获得的疾病结局;以及3） 创建一个可访问和可扩展的协调饮食数据库和开放获取的iPAT工具， 问题研究我们的协调数据驱动方法将增加成功解决复杂问题的可能性。 和微妙的问题与大规模的饮食数据，包括但不限于文化，年龄，性别和 饮食质量模式的地理差异以及饮食质量如何随环境和时间而变化。我们的iPAT 方法将建立在皮芳的行为轨迹模式识别方法，该方法已被 在五个NIDA/NCI/NHLBI资助的纵向OS和RCT中得到验证和复制。把这些证据- 基于iPAT的工具将有助于饮食相关研究的基础设施，先进的模式识别方法， 帮助科学界和公众比较个人饮食行为与当地和国家的饮食- 质量模式和相关的饮食健康风险。我们的工作也将有助于增加更多有效的证据， 指南更广泛地说，这个iPAT项目将有助于创建一个支持统一数据的平台。 管理、近实时模式分析和适应性干预。

项目成果

Topic modeling for systematic review of visual analytics in incomplete longitudinal behavioral trial data.

• DOI: 10.1016/j.smhl.2020.100142
• 发表时间: 2020-11
• 期刊: Smart health
• 作者: Joshua Rumbut;Hua Fang;Honggong Wang

A review of harmonization methods for studying dietary patterns.

研究饮食模式的协调方法综述。

• DOI: 10.1016/j.smhl.2021.100263
• 发表时间: 2022
• 期刊: Smart health (Amsterdam, Netherlands)
• 作者: Gurugubelli,VenkataSukumar;Fang,Hua;Shikany,JamesM;Balkus,SalvadorV;Rumbut,Joshua;Ngo,Hieu;Wang,Honggang;Allison,JeroanJ;Steffen,LynM
• 通讯作者: Steffen,LynM