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

• 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
• 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' 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

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.

项目总结 慢性疾病和条件，如肥胖、糖尿病和心血管疾病都在 在美国所有的健康问题中，最常见、最昂贵、最可预防的。健康的饮食模式 在降低疾病风险方面是至关重要的。自2010年以来，饮食模式方法已被建议用于 根据美国膳食指南咨询研究总体饮食与健康结果的关系 同时，纵向饮食数据变得越来越容易获得。然而，方法是 在描述纵向饮食质量变化方面还不发达，甚至在验证饮食方面也是初级的。 因此，描述这些动态变化的质量模式导致评估的证据不明确 饮食与健康/疾病的关系以及制定饮食指南。饮食之间存在着明显的差距 模式文献和快速增长的统计学习领域。我们建议开发一种创新的统计方法 基于丰富性和高度可比性的饮食质量轨迹模式识别学习工具 来自随机对照试验(RCT)和观察性研究(OS)的各种饮食数据集 个人，种族/民族，和地理位置，跨度长达30年，收集了4个NIH- 资助马萨诸塞州的RCT，以及2个大规模的多地点国家RCT和OS研究以及 基于这些试验的模拟饮食数据。我们的项目建立在美国国立卫生研究院资助的皮方的行为轨迹之上 模式识别工具(基于多重归因的模糊聚类，MI模糊)，用于纵向处理 试验数据与缺失和零膨胀的值，并确定潜在的轨迹模式，以表征 多成分随机对照试验中患者复杂投入度和认知反应的变化 解释了治疗效果的异质性。该项目将增强和扩展MI模糊为可视- 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 慢性病风险评估。我们的目标是提供饮食质量轨迹模式和 纵向研究的相关结果。基于高质量和可比的RCT和操作系统 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 制定饮食指南的有效证据，并澄清我们对饮食与疾病关系的理解 患者/个体类型的范围，潜在地实现更好的个性化、适应性饮食策略。发展中 这种以证据为基础的VIP工具也将有助于饮食相关研究的基础设施，先进的模式- 识别方法，帮助科学界和公众与地方和国家的饮食质量进行比较 指南，并评估饮食健康风险。从长远来看，这个VIP项目将有助于创建一个数据 支持近实时模式分析和适应性干预的管理平台。