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: 8653559
• 负责人: Danielle Erin McCarthy
• 金额: $ 21.93万
• 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 2016-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8653559
• 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（由申请人提供）：复吸是戒烟和其他行为改变领域的一个核心问题。尽管我们的复发概念模型和我们实时测量行为及其前因的方法在过去20年中已经变得越来越复杂，但我们的分析模型并没有随之而来。变化的动态概念模型丰富，而通常采用的变化的相对简单的线性统计模型之间存在差距，这减缓了理解和预防复发的进展。虽然研究已经确定了预测复发风险增加的个体差异，但我们对如何（即，通过什么样的近端机制）这些因素影响瞬时吸烟决定。因此，我们不知道在戒烟干预中监测或靶向哪些近端过程。此外，我们还不知道如何识别吸烟者在戒烟时最容易受到主观痛苦和士气低落的不利影响。因此，我们还不知道如何在有效促进禁欲的同时改善戒烟过程。在戒烟过程中减少痛苦和士气低落可能对晚期复发和再循环（或在复发后恢复禁欲）具有重要意义。在拟议的项目中，研究团队将弥合复发的概念模型和分析模型之间的差距，并解决有关复发过程的这些重要且未解答的问题。为了实现这些目标，该团队将采用最先进的统计建模范式，通过生态瞬时评估（EMA）从四个参与辅助戒烟尝试的吸烟者样本中收集有关吸烟及其前因的实时数据。首先，研究小组将进行潜在转变分析，以确定戒烟过程中关键转变的远端和近端预测因子（即，第一次失误，复发到经常吸烟，和回收）。其次，该团队将非线性动力系统模型与数据相匹配，以确定远端，近端和背景影响的组合，这些影响预测了失效和复发风险的非线性增加。第三，该团队将使用潜在增长混合模型来确定戒烟尝试的前2-6周内吸烟和主观痛苦或士气低落的轨迹类别，以确定可以通过未来治疗改善的不利经历的预测因素。这些分析的结果将扩展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。这些信息可能会提出新的治疗目标和新的策略，以匹配吸烟者的治疗或在风险升高期间提供及时的治疗。拟议分析的结果也可能对其他成瘾或健康行为的变化产生影响。此外，将最先进的分析建模应用于行为变化数据的建议可以作为其他研究人员的模型，从而可以刺激不同研究领域的进步和创新。