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.
Behavioral interventions are commonly used to help people quit smoking, among which Internet-based and culturally tailored interventions are promising but relatively unexplored formats. Even when interventions are delivered uniformly, smokers engage with and respond to them differently. Our innovative pattern-recognition approach, MI-Fuzzy, will help characterize smokers'complex engagement and response behaviors during interventions, evaluate how intervention components work for different types of smokers, clarify the efficacy of each intervention, and provide a new, detailed understanding of how smokers'distinct behavioral patterns are associated with cessation outcomes. Better insight on smokers'engagement and responses during interventions will help improve these interventions for targeted populations and learn about high-risk behavioral patterns that may be clinically important for early intervention. By testing MI-Fuzzy in two funded studies (one Internet-based and one culturally tailored) and providing accessible software tools, our study will accelerate behavioral research on substance use, thereby benefiting large numbers of patients with substance use issues.
|Zhang, Zhaoyang; Fang, Hua; Wang, Honggang (2016) Multiple Imputation based Clustering Validation (MIV) for Big Longitudinal Trial Data with Missing Values in eHealth. J Med Syst 40:146|
|Zhang, Zhaoyang; Fang, Hua; Wang, Honggang (2016) A New MI-Based Visualization Aided Validation Index for Mining Big Longitudinal Web Trial Data. IEEE Access 4:2272-2280|
|Wang, Chanpaul Jin; Fang, Hua; Wang, Honggang (2016) ESammon: A Computationaly Enhanced Sammon Mapping based on Data Density. Int Conf Comput Netw Commun 2016:|
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|Carreiro, Stephanie; Fang, Hua; Zhang, Jianying et al. (2015) iMStrong: Deployment of a Biosensor System to Detect Cocaine Use. J Med Syst 39:186|
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