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

• 批准号: 8699178
• 负责人: Hua Fang
• 金额: $ 23.34万
• 依托单位: UNIV OF MASSACHUSETTS MED SCH WORCESTER
• 依托单位国家: 美国
• 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/8699178
• 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

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。它们通常有多个组成部分，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，导致结果的个体差异很大。然而，在纵向多组分干预中表征吸烟者复杂行为的方法还不发达。基于互联网和面对面的针对文化的干预措施是两种有希望的， 但相对来说还未被探索的行为干预。第一个是达到一般吸烟人群的成本效益，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有效性知之甚少。第二种是针对特定人群，但我们需要了解种族/族裔群体如何对这种干预作出反应，以及文化调整在多大程度上有用。我们提出了一种新的模式识别方法来描述复杂的参与/响应行为在基于互联网和文化定制的干预。我们的方法是建立在PI的初步吸烟行为研究， 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，并科普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、高维的并且包含许多缺失值的情况。在此，我们将用新功能增强MI-模糊，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）休斯顿博士的大规模NCI资助的，针对一般吸烟人群的Quit-Primo互联网干预，以及（2）Kim博士的小规模NIDA资助的认知，文化定制，基于诊所的TDTA干预少数吸烟人群。我们将描述吸烟者的在线参与（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，阐明其疗效，并对吸烟者的轨迹模式如何与不同的戒烟结果相关提供新的详细了解。更好地了解吸烟者如何参与和应对干预措施将有助于发现传统方法遗漏的重要关系，为如何改善目标人群的干预措施以及可能对早期干预具有临床重要性的高风险行为模式提供新的证据。检查不同类型的行为干预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可访问的工具，本研究将推进一般模式识别方法，并加速其在物质使用行为研究中的实用性。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，以更好的和有针对性的干预措施，最终有利于以病人为中心的护理有关的物质使用。