VIP:Visual-Valid Dietary Behavior Pattern Recognition for Local-National Trials

VIP：地方-国家试验的视觉有效饮食行为模式识别

基本信息

批准号：
9907572
负责人：
Hua Fang
金额：
$ 45.22万
依托单位：
UNIVERSITY OF MASSACHUSETTS DARTMOUTH
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-18 至 2021-10-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9907572
关键词：
Address Advisory Committees Behavior Behavioral Cardiovascular Diseases Chronic Chronic Disease Cognitive Communities Complex Computing Methodologies Coronary Artery Risk Development in Young Adults Study Data Data Quality Data Set Databases Diabetes Mellitus Diet Dietary Intervention Dietary Practices Disease Disease Outcome Eating Ethnic Origin Funding General Population Geographic Locations Goals Grant Guidelines Health Heterogeneity Imagery Incidence Individual Infrastructure Intervention Studies Literature Longitudinal Studies Longitudinal observational study Machine Learning Massachusetts Measures Methodology 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 Nutrient Obesity Observational Study Outcome Participant Patients Pattern Pattern Recognition Physical activity Process Psychological Factors Race Randomized Controlled Trials Research Design Risk Risk Assessment Risk Reduction Site Software Tools Subgroup Testing Time United States United States National Institutes of Health Validation Variant Visual Visual Pattern Recognition Women&apos s Health Work adaptive intervention base cohort cost data management depressive symptoms design dietary guidelines disorder risk evidence base field study good diet indexing individual patient innovation learning strategy longitudinal dataset outcome prediction prevent public health relevance response simulation social media sociodemographics tool treatment effect user-friendly

项目摘要

PROJECT SUMMARY Chronic diseases and conditions such as obesity, diabetes and cardiovascular disease are among the most common, costly, and preventable of all health problems in the United States. A healthy dietary pattern is paramount in disease risk reduction. Since 2010, the dietary pattern approach has been recommended to examine the relation of the totality of diet and health outcomes by U.S. Dietary Guidelines Advisory Committees; meanwhile longitudinal dietary data have become increasingly available. Yet, methods are underdeveloped for characterizing longitudinal diet-quality variations and even rudimentary for validating diet- quality patterns that describe these dynamic variations, therefore, leading to unclear evidence for assessing diet-health/disease relationships and formulating dietary guidelines. A noticeable gap exists between dietary pattern literature and the fast-growing statistical learning field. We propose to develop an innovative statistical learning tool for diet-quality trajectory pattern-recognition based on rich and highly-comparable longitudinal dietary datasets from randomized controlled trials (RCT) and observational studies (OS) pertaining to a variety of individuals, race/ethnicities, and geographical locations, and spanning up to 30 years, collected across 4 NIH- funded RCTs in Massachusetts, and 2 large-scale multi-site national RCT and OS studies as well as simulated dietary data based on these trials. Our project builds on PI Fang’s NIH-funded behavioral trajectory pattern-recognition tool (Multiple-Imputation based Fuzzy Clustering, MIFuzzy) which processes longitudinal trial data with missing and zero-inflated values, and identifies latent trajectory patterns that characterize patients’ complex engagement and cognitive response variations during multi-component RCTs and better explains the heterogeneity of treatment effects. This project will enhance and expand MIFuzzy to a Visual- Valid Dietary Behavior Pattern Recognition tool (VIP), adapted to diet-quality trajectory pattern analyses and chronic disease risk assessment. Our goal is to provide a new multi-view of diet-quality trajectory patterns and associated outcomes from longitudinal studies. Based upon high-quality and comparable RCT and OS longitudinal dietary data from NIDDK-, NHLBI-, and NIMH-funded studies, this VIP project will help grow more valid evidence for developing dietary guidelines and clarify our understanding of diet-disease relationships for a range of patient/individual types, potentially enabling better personalized, adaptive dietary strategies. Developing this evidence-based VIP tool will also contribute to the infrastructure for diet-related studies, advance pattern- recognition methods, help scientific communities and the lay public compare with local and national diet-quality guidelines, and assess dietary health risks. In the long run, this VIP project will contribute to creating a data management platform that support near-real-time pattern analyses and adaptive interventions.

项目概要肥胖、糖尿病和心血管疾病等慢性疾病和病症属于在美国所有健康问题中最常见、成本最高且可预防的健康饮食模式。自 2010 年以来，饮食模式方法已被推荐用于降低疾病风险。根据美国膳食指南咨询检查总体饮食与健康结果的关系委员会；虽然纵向饮食数据已变得越来越可用，但方法却越来越多。描述纵向饮食质量变化的能力还不够成熟，甚至验证饮食的基本能力也很差。因此，描述这些动态变化的质量模式导致评估证据不明确饮食与健康/疾病的关系和制定饮食指南之间存在明显的差距。我们建议开发一个创新的统计学习领域。基于丰富且高度可比的纵向数据的饮食质量轨迹模式识别学习工具来自与各种相关的随机对照试验（RCT）和观察性研究（OS）的饮食数据集个人、种族/族裔和地理位置的数据，跨度长达 30 年，跨 4 个 NIH 收集资助了马萨诸塞州的随机对照试验，以及 2 项大规模多地点国家随机对照试验和 OS 研究以及我们的项目基于 PI Fang 的 NIH 资助的行为轨迹。纵向处理的模式识别工具（基于多重插补的模糊聚类，MIFuzzy）具有缺失值和零膨胀值的试验数据，并识别表征的潜在轨迹模式多成分随机对照试验期间患者复杂的参与度和认知反应变化以及更好的结果解释治疗效果的异质性该项目将增强 MIFuzzy 并将其扩展为 Visual- 有效的饮食行为模式识别工具（VIP），适用于饮食质量轨迹模式分析和我们的目标是提供饮食质量轨迹模式和慢性疾病风险评估的新的多视角。基于高质量且可比较的 RCT 和 OS 的相关结果。来自 NIDDK、NHLBI 和 NIMH 资助的研究的纵向饮食数据，这个 VIP 项目将帮助增长更多为饮食指南提供有效的证据，并澄清我们对饮食与疾病关系的理解一系列患者/个人类型，有可能实现更好的个性化、适应性饮食策略。这个基于证据的 VIP 工具还将为饮食相关研究的基础设施做出贡献，推进模式- 识别方法，帮助科学界和普通公众与当地和国家饮食质量进行比较从长远来看，这个 VIP 项目将有助于创建数据。支持近实时模式分析和自适应干预的管理平台。