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: 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
• 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

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。它们通常有多个组件，并且随着时间的推移而实现。吸烟者的参与和反应行为在干预过程中发生变化，导致结果的实质性个体差异。然而，在纵向多成分干预中，描述吸烟者复杂行为的方法尚不发达。基于互联网的干预和面对面的文化定制干预是两种有希望的干预，