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.

项目摘要 慢性疾病和肥胖，糖尿病和心血管疾病等疾病是 在美国所有健康问题中最常见，昂贵且可预防。健康的饮食模式 疾病风险降低至关重要。自2010年以来，建议采用饮食模式方法 研究美国饮食指南的饮食和健康结果的整体关系 委员会；同时，纵向饮食数据已越来越多。但是，方法是 欠发达的纵向饮食质量质量变化，甚至是基本验证饮食 - 因此，描述这些动态变化的质量模式，导致评估的不清楚证据 饮食健康/疾病关系和制定饮食指南。饮食之间存在明显的差距 模式文献和快速增长的统计学习领域。我们建议开发创新的统计 基于丰富且高度可观的纵向的饮食质量轨迹模式识别工具 来自随机对照试验（RCT）和观察性研究（OS）的饮食数据集（OS） 个人，种族/种族和地理位置以及跨越30年的个人，在4个NIH中收集了 马萨诸塞州的RCT以及2个大型多站点国家RCT和OS研究以及 根据这些试验模拟饮食数据。我们的项目建立在Pi Fang的NIH资助的行为轨迹的基础上 模式识别工具（基于多输入的模糊聚类，mifuzzy），该工具处理纵向 具有丢失和零膨胀值的试用数据，并确定表征的潜在轨迹模式 多组分RCT期间患者的复杂参与和认知反应变化，更好 解释了治疗效果的异质性。该项目将增强和扩展Mifuzzy至视觉 有效的饮食行为模式识别工具（VIP），适合饮食质量轨迹模式分析和 慢性疾病风险评估。我们的目标是提供饮食质量轨迹模式的新的多视图和 纵向研究的相关结果。基于高质量和可比的RCT和OS 来自NIDDK-，NHLBI-和NIMH资助的研究的纵向饮食数据将有助于增长更多 制定饮食指南的有效证据，并阐明我们对饮食疾病关系的理解 患者/个人类型的范围，有可能实现更好的个性化，适应性的饮食策略。发展 这种基于证据的VIP工具还将为与饮食有关的研究的基础设施做出贡献，提高模式 - 识别方法，帮助科学社区和公众与地方和国家饮食品质相比 指南并评估饮食健康风险。从长远来看，这个VIP项目将有助于创建数据 支持近实时模式分析和自适应干预措施的管理平台。