DISC: Describe Smoking Cessation in RCT Multi-Component Behavioral Intervention

DISC：在 RCT 多成分行为干预中描述戒烟

基本信息

批准号：
8505922
负责人：
Hua Fang
金额：
$ 23.73万
依托单位：
UNIV OF MASSACHUSETTS MED SCH WORCESTER
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-07-15 至 2016-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8505922
关键词：
Abstinence Alcohol or Other Drugs use Asian Americans Behavior Behavior Therapy Behavioral Behavioral Medicine Behavioral Research Characteristics Clinic Cognitive Complex Computer software Data Doctor of Philosophy Early Intervention Ethnic group Funding Goals Grouping Health Services Research Healthcare Individual Internet Intervention Intervention Studies Korean American Lead Learning Link Manuals Massachusetts Measures Mental Health Methodology Methods Minority Modeling National Institute of Drug Abuse Neonatal Observational Study Outcome Patient-Centered Care Patients Pattern Pattern Recognition Population Population Study Pregnancy Provider Psychiatry Research Risk Factors Simulate Smoker Smoking Smoking Behavior Social Network Software Tools Target Populations Testing Time Tobacco Tobacco Dependence Universities Variant Work base behavior change coping cost cost effective demographics health disparity high risk implementation science improved innovation insight mathematical model medical schools microsystems multi-component intervention patient oriented prototype public health relevance racial and ethnic response smoking cessation smoking intervention tool user friendly software

项目摘要

DESCRIPTION (provided by applicant): Behavioral interventions are commonly used to promote smoking cessation. They typically have multiple components and are implemented over time. Smokers' engagement and response behaviors change over the course of interventions, resulting in substantial individual variations in outcomes. However, methods are underdeveloped for characterizing smokers' complex behaviors in longitudinal multi-component interventions. Internet-based and face-to-face culturally-tailored interventions are two promising, but relatively unexplored, behavioral interventions. The first is cost effective for reaching generl smoking populations, yet we know little about how to adequately measure individuals' dynamic online engagement with an intervention or examine its efficacy. The second targets specific populations, but we need to learn how racial/ethnic groups respond to such interventions and how much cultural tailoring is useful. We propose a new pattern-recognition approach to characterize complex engagement/response behaviors during Internet-based and culturally tailored interventions. Our approach is built on the PI's preliminary smoking behavior studies, for which she developed a multiple-imputation-based fuzzy clustering model (MI-Fuzzy) to identify pregnancy smoking behavioral patterns, and to cope with real-world situations where smokers have memberships in multiple clusters and their smoking data are longitudinal, non-normal, high dimensional and contain many missing values. Herein, we will enhance MI-Fuzzy with new features, compare it to typical models, and expand our pattern approach to two longitudinal behavioral intervention studies: (1) Dr. Houston's large-scale NCI-funded, Quit-Primo Internet intervention for a general smoking population, and (2) Dr. Kim's small-scale NIDA-funded cognitive, culturally tailored, clinic-based TDTA intervention for a minority smoking population. We will characterize smokers' online engagement (Quit-Primo) and cognitive responses (TDTA), evaluate how the interventions' components work for different smokers, clarify their efficacy, and provide a new, detailed understanding of how smokers' trajectory patterns relate to different cessation outcomes. Better understanding of how smokers engage with and respond to interventions will help uncover important relationships missed by traditional approaches, yield new evidence on how to improve these interventions for targeted populations and on high-risk behavioral patterns that may be clinically important for early intervention. Examining different types of behavioral interventions will also facilitate generalizing our pattern approach to other substance-use studies and populations. By providing analytical prototypes and accessible tools, this study will advance general pattern recognition methodology, and accelerate its utility in behavioral studies of substance use. As our dissemination activities expand, this work will likely stimulate similar studies for better and targeted interventions, ultimately benefiting patient-centered care related to substance use.

描述（由申请人提供）：行为干预措施通常用于促进戒烟。它们通常具有多个组件，并且会随着时间的推移而实现。在干预过程中，吸烟者的参与和反应行为发生了变化，从而导致了结果的实质性差异。然而，方法不发达用于表征吸烟者在纵向多组分干预措施中的复杂行为。基于互联网的和面对面的文化限制干预措施是两个有前途的干预措施，但是相对尚未探索的行为干预措施。首先是达到一代吸烟人群的成本效益，但是我们对如何通过干预措施充分衡量个人的在线参与或检查其功效知之甚少。第二个目标是特定的人群，但我们需要了解种族/族裔如何应对这种干预措施以及对文化剪裁多少有用。我们提出了一种新的模式识别方法，以表征基于互联网和文化量身定制的干预措施中复杂的参与/反应行为。我们的方法建立在PI的初步吸烟行为研究基础上她开发了一种基于多进口的模糊聚类模型（MI-Fuzzy）来识别妊娠吸烟行为模式，并应付吸烟者在多个集群中具有成员资格的现实情况，且吸烟数据是纵向，非正态分子，非正态分子，高维度和许多缺失值。 Herein, we will enhance MI-Fuzzy with new features, compare it to typical models, and expand our pattern approach to two longitudinal behavioral intervention studies: (1) Dr. Houston's large-scale NCI-funded, Quit-Primo Internet intervention for a general smoking population, and (2) Dr. Kim's small-scale NIDA-funded cognitive, culturally tailored, clinic-based TDTA intervention for a minority smoking 人口。我们将表征吸烟者的在线参与（戒烟）和认知反应（TDTA），评估干预措施的组件如何对不同的吸烟者起作用，阐明其功效，并对吸烟者的轨迹模式如何与不同的停止现象相关。更好地了解吸烟者如何与干预措施互动和反应将有助于发现传统方法所遗漏的重要关系，并提供有关如何改善目标人群的这些干预措施以及对可能对早期干预至关重要的高风险行为模式的新证据。检查不同类型的行为干预措施还将有助于将我们的模式方法推广到其他物质使用研究和人群中。通过提供分析原型和可访问的工具，本研究将提高一般模式识别方法，并加快其在物质使用行为研究中的效用。随着我们的传播活动的扩大，这项工作可能会刺激类似的研究，以进行更好和有针对性的干预措施，最终使与药物使用有关的患者以患者为中心的护理受益。