Time-Varying Effects of EMA Predictors on Point-Prevalence Smoking Outcome

描述（由申请人提供）：烟草使用是癌症的主要可预防原因，并且与发病率和死亡率的增加有关。尽管目前70%的吸烟者有戒烟的动机并试图戒烟，但自然戒烟和辅助戒烟的比例很低，从5%到10%不等。日记和生态瞬时评估（EMA）方法对于捕捉自然环境中的动态体验至关重要，在几天内和几天之间（例如吸烟欲望）。这些经历不仅反映了戒烟过程的复杂性，而且也是戒烟成功的潜在预测因素，目前还没有得到充分的研究。在这个项目中，我们解决了一个重要的研究问题的时变效应的预测捕获EMA上的一个点的结果。例如，在尝试戒烟之前强烈的吸烟冲动可能无法预测戒烟成功;然而，在尝试戒烟之后立即经历它们可能是有害的。该项目旨在通过开发新的灵活的统计工具来探索新的研究问题，以增加对成功戒烟障碍的理解，这些研究问题涉及吸烟相关过程（EMA密集测量）对点患病率吸烟结果的时变影响，并应用这些工具在两项已完成的EMA研究中识别戒烟的背景和心理障碍。该项目有三个具体目标。目标1涉及开发和验证EMA数据的变系数回归（VCR）模型。该模型将适应EMA的独特特征，包括大数据量、观察结果之间的不等间隔、个体间不同的测量时间以及每人评估总数的差异。目标2包括将该模型应用于两个EMA数据集，以确定戒烟的障碍及其表现的关键时期。具体来说，我们将确定a）哪些与吸烟相关的过程预测戒烟成功，B）这些过程的预测潜力如何随时间变化，以及c）戒烟过程中的哪些阶段对决定戒烟成功至关重要。产生这些数据的高质量，精心设计的研究先前由NIH资助，并在试图自然或辅助戒烟的成年人中捕获对吸烟欲望，负面影响，戒烟自我效能和其他吸烟者的存在的瞬间评估。作为目标3的一部分，将开发用户友好的软件来传播这项工作，并促进癌症相关领域的研究人员使用该方法。该提案解决了对EMA数据分析的复杂统计方法的需求，EMA数据在癌症研究中变得越来越普遍。该提案还将回答有关戒烟的心理和背景障碍的新颖和独特的问题。这些知识将直接有助于提高戒烟干预措施的重点、有效性和成本效益，并减轻吸烟给社会和个人带来的负担。它还将作为今后方法和应用工作的基础。 公共卫生相关性：与公共卫生的相关性：通过手机和其他技术收集的生态瞬时评估（EMA）越来越多地用于研究与健康相关的行为，如通过捕捉日常和瞬间的经历来研究吸烟。拟议的工作将开发一种新的统计方法来分析EMA，以预测日常生活中表现出的因素的主要健康结果。这将推进当前的分析实践，这些实践通过将详细的EMA记录折叠成几个摘要而很少利用它们，并促进现有EMA数据集的二次分析。该方法的实证应用将确定戒烟的心理和背景障碍，这可以转化为更有针对性和有效的干预措施，减轻吸烟给社会和个人带来的负担。

DESCRIPTION (provided by applicant): Tobacco use is the leading preventable cause of cancer and is associated with increased morbidity and mortality. In spite of the fact that 70% of current smokers are motivated and try to quit, the rates of natural and aided cessation are low and range from 5 to 10%. Diary and ecological momentary assessment (EMA) methodology has become vital for capturing dynamic experiences in naturalistic settings, within and across days (e.g. smoking urges). Such experiences reflect not only the complexity of the smoking-cessation process but also appear as potential, and currently under-investigated, predictors of quit success. In this project, we address an important research question of the time-varying effects of predictors captured with EMA on a point outcome. For example, strong smoking urges prior to a quit attempt may not predict quitting success; however, experiencing them immediately after a quit attempt may be detrimental. This project aims to increase the understanding of barriers to successful quitting by developing new flexible statistical tools for exploring novel research questions on time-varying effects of smoking-related processes (intensively measured with EMA) on point-prevalence smoking outcomes and applying these tools to identify contextual and psychological barriers to smoking cessation in two completed EMA studies. This project has 3 specific aims. Aim 1 involves developing and validating the varying-coefficient regression (VCR) model for EMA data. The model will accommodate unique features of EMA including large data volume, unequal spacing between observations, varying times of measurements across individuals, and difference in the total number of assessments per person. Aim 2 consists of applying the model to two EMA datasets for the purpose of identifying barriers to smoking cessation and critical periods of their manifestation. Specifically, we will determine a) what smoking-related processes predict quitting success, b) how the predictive potential of these processes changes over time, and c) what periods in the smoking-cessation process are critical for determining cessation success. The high quality, well-designed studies yielding these data were previously funded by NIH and capture momentary assessments of smoking urges, negative affect, abstinence self-efficacy, and presence of other smokers, in adults who attempted natural or aided smoking cessation. As part of aim 3, user-friendly software will be developed to disseminate the work and promote use of the method by researchers in a variety of cancer-related fields. This proposal addresses the need for sophisticated statistical methods for the analysis of EMA data that are becoming increasingly prevalent in cancer research. The proposal will also answer novel and unique questions about psychological and contextual barriers to smoking cessation. This knowledge will directly contribute to improving the focus, efficacy, and cost-efficiency of cessation interventions and to alleviating the burden smoking places on society and individuals. It will also serve as a foundation of future methodological and applied work. PUBLIC HEALTH RELEVANCE: Relevance to public health: Ecological momentary assessments (EMA) collected via cell phones and other technologies are increasingly used to study health-related behaviors such as smoking by capturing daily and momentary experiences. The proposed work will develop a novel statistical method for analysis of EMA to predict major health outcomes from factors manifesting in daily lives. This will advance current analytical practices, which take little advantage of the detailed EMA records by collapsing them into a few summaries, and promote secondary analyses of existing EMA datasets. The empirical applications of the method will identify psychological and contextual barriers to smoking cessation, which can translate into more focused and efficacious interventions and alleviation of the burden smoking places on society and individuals.