Danielle E. McCarthy - US grants

DESCRIPTION (provided by applicant): Phenotypic Markers for Smoking Cessation: Impulsive Choice and Impulsive Action This application addresses broad Challenge Area (01): Behavior, Behavioral Change, and Prevention and specific high-priority Challenge Topic 01-AA-101 Identifying Phenotypic Markers for Positive Behavior Change. The proposed project will gather new information about temporal growth patterns in two phenotypic markers of behavior change among smokers engaged in an attempt to quit smoking with individual smoking cessation counseling and nicotine lozenge treatment. The project will also generate real time data about the associations between alcohol use and measures of impulsive choice and action in individuals attempting to stop smoking. Delay discounting is a devaluing of delayed rewards that has been documented in smokers, particularly during acute periods of withdrawal. Delay discounting has important but previously unexplored implications for momentary decisions that smokers attempting to quit must make regarding the value of abstaining now, despite withdrawal discomfort and powerful cravings, to reap health benefits years later. The proposed project will examine day-to-day fluctuations in delay discounting assessed daily by palmtop computer in the 7 days preceding a quit attempt and the first 21 days of a quit attempt and will explore relations between delay discounting and subsequent abstinence. Subjects will also complete a standard, laboratory-based delay discounting task three times in the study (one week pre-quit, on the target quit day, and 3 weeks post-quit) which will be assessed as predictors of later abstinence. Variables that may account for changes in delay discounting over time, such as alcohol use, affective states, access to cigarettes, or perceived certainty of health benefits of quitting, will be examined as well. In addition to the delay discounting measure of impulsive choice, the proposed project will also administer a task assessing impulsive action, Go-no go tasks require that individuals be able to inhibit prepotent responses, an ability with clear relevance for smoking cessation. Subjects are instructed to tap a key when a go stimulus appears and to withhold the response when the signal is not present. To date, real-time data regarding impulsive action have not been collected during the course of a quit attempt. This project will generate new information about changes in impulsive action and factors associated with such changes (e.g., alcohol use, craving) during an attempt to inhibit over-learned smoking behavior. The within-subjects longitudinal design of the proposed project will generate new knowledge about change in impulsive choice and impulsive action, and the associations between these constructs over time, during an assisted behavior change attempt. The project will also generate new information about relations between real-time behavioral measures of impulsivity and affective states, environmental contexts, behaviors (including alcohol use) assessed in real time, and subsequent abstinence. The proposed research may help identify cognitive markers of successful change or risk that could be targeted in future interventions. The proposed research will combine addiction research with cognitive psychology and behavioral economics to fill an important gap in our understanding about dynamic changes in decisions and behaviors that may influence smoking behavior. Tracking preference for immediate versus delayed rewards and impulsive action during an attempt to quit smoking and may help us identify markers of successful cessation with implications for other substances of abuse, including alcohol.

DESCRIPTION (provided by applicant): Relapse is a central problem in smoking cessation and other areas of behavior change. Although our conceptual models of relapse and our methods of measuring behavior and its antecedents in real-time have grown in sophistication over the past 20 years, our analytical models have not followed suit. The gap between the richness of dynamic conceptual models of change, and the relatively simple, linear statistical models of change typically adopted has slowed progress in understanding and preventing relapse. Although research has identified individual differences that predict increased relapse risk, we know little about how (i.e., by what proximal mechanisms) such factors influence momentary smoking decisions. As a result, we do not know which proximal processes to monitor or target in smoking cessation interventions. In addition, we do not yet know how to identify smokers most vulnerable to unfavorable experiences when they quit smoking, in terms of subjective distress and demoralization. As such, we do not yet know how to improve the process of quitting while also effectively promoting abstinence. Reducing distress and demoralization during the process of quitting may have important implications for late relapse and recycling (or returning to abstinence following relapse). In the proposed project, the research team will bridge the gap between conceptual and analytic models of relapse and address these important, unanswered questions about the relapse process. To achieve these aims, the team will apply state-of-the-art statistical modeling paradigms to real-time data on smoking and its antecedents collected via ecological momentary assessment (EMA) from four samples of smokers engaged in assisted smoking cessation attempts. First, the team will conduct latent transition analyses to identify both distal and proximal predictors of key transitions in the smoking cessation process (i.e., a first lapse, relapse to regular smoking, and recycling). Second, the team will fit nonlinear dynamical systems models to the data to identify the combinations of distal, proximal, and contextual influences that predict non-linear increases in lapse and relapse risk. Third, the team will use latent growth mixture modeling to identify classes of trajectories in smoking and subjective distress or demoralization during the first 2-6 weeks of a quit attempt in an effort to identify predictors of unfavorable experiences that could be ameliorated with future treatments. Results of these analyses will extend knowledge of critical, distal determinants of important smoking and subjective outcomes, and will illuminate how these influences affect key transitions or trajectories in the smoking cessation process. Such information could suggest new treatment targets and new strategies for matching smokers to treatments or delivering just-in-time treatments during periods of elevated risk. Results from the proposed analyses may have implications for other addictive or health behavior changes, as well. In addition, the proposed application of state-of-the-art analytic modeling to behavior change data may serve as a model to other researchers, and thus may spur advances and innovations in diverse research areas. PUBLIC HEALTH RELEVANCE: Despite decades of research, relapse remains the most common outcome in smoking cessation efforts, regardless of treatment. This project will apply novel statistical approaches to modeling change to accelerate discovery of the factors that influence smoking cessation success and risk and protective factors to target with treatments to promote lasting abstinence from tobacco use.

Project Summary A small minority of the 25 million US smokers who receive primary care each year receive evidence-based smoking treatment. Healthcare systems have enormous potential to connect smokers with high quality, evidence-based care. Diverse strategies have been used to increase treatment use, but treatment reach remains stubbornly low. The proposed research study will be the first rigorous factorial experiment to optimize a package of centralized healthcare system interventions to enhance smoking cessation treatment reach, and through this mediating pathway, reduce smoking prevalence after 2 years of reach intervention exposure and treatment access. Intervention components with promising empirical support and grounding in behavioral theory will be fully crossed in a 2x2x2x2 factorial experiment in 1664 patients who smoke daily from 8 primary care clinics. These patient-focused intervention components include: 1) monetary incentives for treatment initiation, 2) electronic-health-record (EHR)-enabled automation of quarterly tailored treatment invitations offering low-barrier treatment access, and 3) proactive telephone care management and motivational intervention. These components will be used to promote use of either standard care (referral to a toll-free tobacco quitline and the primary care provider), or to more intensive treatment (3 phone counseling sessions with either combination nicotine replacement therapy or varenicline), as randomly assigned on a 4th experimental factor (access to intensive treatment). All 4 experimental intervention components will be on or off (as randomly assigned) for 2 years to permit analysis of their cumulative impact on treatment reach and abstinence among primary care patients who are not initially willing to make a quit attempt. Analyses after 1 year of follow up will identify an especially effective (optimized) reach-intervention package to be evaluated in the Optimized Care Project in this Program Project. Clinic-based recruitment will support the project aims to identify: 1) a highly effective intervention package that promotes usual and intensive treatment use (reach) and downstream abstinence from smoking at low cost, 2) the mediating pathways for these effects, and 3) subpopulations of patients who particularly benefit from these intervention components (reach representativeness). This pragmatic project will use diverse real-world clinics, clinic staff interventionists, and patient participants, with minimal assessment to enhance generalizability and dissemination potential (as per RE-AIM). At the same time, rigorous experimental design and statistical analyses will enhance internal validity. This project will uniquely optimize interventions to enhance smoking cessation treatment reach. The project will be integrated into the Program Project to yield additional information (e.g., whether some components are more difficult to implement than others; how an optimized reach intervention works with optimized cessation treatments). The project will identify interventions that may prevent cancer and other chronic illnesses by connecting more smokers with effective smoking cessation treatments.