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: 8273952
• 负责人: Danielle Erin McCarthy
• 金额: $ 24.42万
• 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/8273952
• 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. 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.

描述(由申请者提供)：复发是戒烟和其他行为改变领域的一个中心问题。尽管我们对复发的概念模型，以及我们实时测量行为及其前因的方法在过去20年里变得更加复杂，但我们的分析模型并没有效仿。动态变化概念模型的丰富性与通常采用的相对简单、线性的变化统计模型之间的差距减缓了理解和防止复发的进展。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(即通过什么近端机制)影响瞬时吸烟决定知之甚少。因此，我们不知道在戒烟干预中应该监测或针对哪些近端突起。此外，我们还不知道如何从主观痛苦和士气低落的角度来识别戒烟时最容易受到不利体验影响的吸烟者。因此，我们还不知道如何改进戒烟过程，同时也有效地促进禁欲。减少戒烟过程中的痛苦和士气低落可能会对后来的复发和再循环(或在复发后重新戒除)产生重要影响。在拟议的项目中，研究小组将弥合复发概念模型和分析模型之间的差距，并解决这些关于复发过程的重要、尚未回答的问题。为了实现这些目标，该团队将应用最先进的统计建模范式，从四个参与辅助性戒烟尝试的吸烟者样本中，通过生态瞬时评估(EMA)收集关于吸烟及其前因的实时数据。首先，该团队将进行潜在的转变分析，以确定戒烟过程中关键转变的远端和近端预测因素(即首次吸烟、再次吸烟和重新吸烟)。其次，该团队将使非线性动力系统模型与数据相匹配，以确定预测失误和复发风险非线性增加的远端、近端和背景影响的组合。第三，该团队将使用潜在生长混合建模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，以努力确定不良经历的预测因素，这些不良经历可能会通过未来的治疗得到改善。这些分析的结果将扩大对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战略，使吸烟者与治疗相匹配，或在风险增加的时期提供及时的治疗。拟议的分析结果可能对其他成瘾或健康行为的改变也有影响。此外，建议将最先进的分析建模应用于行为变化数据，可以作为其他研究人员的模型，从而可能刺激不同研究领域的进步和创新。 与公共健康相关：尽管有几十年的研究，复发仍然是戒烟努力中最常见的结果，无论治疗方法如何。该项目将应用新的统计方法来模拟变化，以加速发现影响戒烟成功和风险的因素，并针对保护因素进行治疗，以促进持久戒烟。