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）：行为干预通常用于促进戒烟。它们通常具有多个组件并随着时间的推移而实施。吸烟者的参与和反应行为在干预过程中发生变化，导致结果存在巨大的个体差异。然而，用于表征吸烟者在纵向多成分干预中的复杂行为的方法尚不成熟。基于互联网和面对面的文化定制干预措施是两种有前途的干预措施但相对未经探索的行为干预。第一个对于覆盖一般吸烟人群来说具有成本效益，但我们对如何充分衡量个人对干预措施的动态在线参与或检查其效果知之甚少。第二个针对特定人群，但我们需要了解种族/族裔群体如何应对此类干预措施以及文化定制有多大用处。我们提出了一种新的模式识别方法来描述基于互联网和文化定制干预期间复杂的参与/响应行为。我们的方法建立在 PI 的初步吸烟行为研究的基础上，她开发了一种基于多重插补的模糊聚类模型（MI-Fuzzy）来识别怀孕吸烟行为模式，并应对现实世界中吸烟者在多个聚类中具有成员资格并且他们的吸烟数据是纵向的、非正态的、高维且包含许多缺失值。在这里，我们将用新功能增强 MI-Fuzzy，将其与典型模型进行比较，并将我们的模式方法扩展到两项纵向行为干预研究：（1）Houston 博士由 NCI 资助的大规模 Quit-Primo 互联网干预(2) Kim 博士为少数吸烟人群提供了由 NIDA 资助的小规模认知、文化定制、基于临床的 TDTA 干预措施。我们将描述吸烟者的在线参与度（Quit-Primo）和认知反应（TDTA），评估干预措施的组成部分如何对不同的吸烟者起作用，阐明其功效，并提供关于吸烟者的轨迹模式如何与不同吸烟者相关的新的、详细的理解。戒烟结果。更好地了解吸烟者如何参与和响应干预措施将有助于发现传统方法所遗漏的重要关系，提供关于如何改进针对目标人群的干预措施以及对早期干预可能具有临床重要意义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也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。通过提供分析原型和易于使用的工具，这项研究将推进通用模式识别方法，并加速其在物质使用行为研究中的应用。随着我们传播活动的扩大，这项工作可能会刺激类似的研究，以提供更好、更有针对性的干预措施，最终有利于与药物使用相关的以患者为中心的护理。