Accelerating Smoking Relapse Research Using Longitudinal Models of EMA Data

使用 EMA 数据的纵向模型加速吸烟复吸研究

• 批准号: 8468672
• 负责人: Danielle Erin McCarthy
• 金额: $ 21.29万
• 依托单位: RUTGERS, THE STATE UNIV OF N.J.
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-05-15 至 2015-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8468672
• 关键词: Abstinence; Addictive Behavior; Address; Adopted; Adult; Affect; Alcohol consumption; Algorithms; Area; Back; Behavior; Biological Models; Complex; Data; Data Set; Devices; Distal; Distress; Electronics; Environment; Event; Future; Growth; Health; Health behavior change; Individual; Individual Differences; Intervention; Knowledge; Measures; Methodology; Methods; Modeling; Monitor; Motivation; Outcome; Prevalence; Process; Recycling; Relapse; Research; Research Personnel; Research Project Grants; Risk; Risk Factors; Sampling; Self Efficacy; Smoker; Smoking; Smoking Cessation Intervention; Statistical Models; System; Techniques; Testing; Theoretical model; Time; Tobacco use; Treatment outcome; Withdrawal; Work; addiction; behavior change; craving; data sharing; experience; improved; innovation; novel; prevent; smoking cessation; smoking relapse; success; theories; therapy design

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.

描述（申请人提供）：复发是戒烟和其他行为改变领域的核心问题。尽管我们的复发概念模型和测量行为的方法及其实时的方法在过去20年中已经成长，但我们的分析模型尚未遵循。动态变化概念模型的丰富度与相对简单的线性统计模型通常采用的差距在理解和预防复发方面的进展缓慢。尽管研究已经确定了预测复发风险增加的个体差异，但我们对这些因素如何影响暂时的吸烟决策了解（即，通过哪些近端机制）了解。结果，我们不知道在戒烟干预措施中要监测或靶向哪些近端过程。此外，我们尚不知道如何在主观困扰和沮丧的情况下戒烟时最容易受到不利的经历的影响。因此，我们尚不知道如何改善退出过程，同时也有效地促进禁欲。在退出过程中减少困扰和沮丧可能对晚期复发和回收（或复发后恢复禁欲）具有重要意义。在拟议的项目中，研究团队将弥合复发概念和分析模型之间的差距，并解决有关复发过程的这些重要的，未解决的问题。为了实现这些目标，该团队将对吸烟及其通过生态瞬时评估（EMA）收集的吸烟及其先决条件的实时数据应用最新的统计建模范式，从参与辅助吸烟尝试的四个吸烟者样本中收集。首先，该团队将进行潜在的过渡分析，以确定戒烟过程中关键过渡的远端和近端预测指标（即，首次失效，复发到常规吸烟和回收）。其次，团队将拟合非线性动力学系统模型与数据拟合，以确定远端，近端和上下文影响的组合，这些影响预测非线性在失误和复发风险中会增加。第三，该团队将使用潜在的增长混合物建模来识别戒烟试图的前2-6周中的吸烟和主观困扰或沮丧的类别，以确定可以通过未来治疗来改善的不利经历的预测指标。这些分析的结果将扩展有关重要吸烟和主观结果的关键，远端决定因素的知识，并将阐明这些影响如何影响戒烟过程中的关键过渡或轨迹。这些信息可能会暗示新的治疗目标和新策略，以使吸烟者与治疗或在风险升高期间提供即时治疗。拟议的分析的结果可能对其他成瘾或健康行为改变也有影响。此外，提出的最先进的分析建模在行为改变数据中的应用可能会成为其他研究人员的模型，因此可能刺激了各种研究领域的进步和创新。