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

EMA 预测因子对点吸烟结果的随时间变化的影响

基本信息

批准号：
8515372
负责人：
Mariya Petrovna Shiyko
金额：
$ 7.04万
依托单位：
NORTHEASTERN UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-07-23 至 2014-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8515372
关键词：
Abstinence Address Adult Affect Behavior Cancer Etiology Caring Cellular Phone Centers for Disease Control and Prevention (U.S.)Cessation of life Characteristics Data Data Set Development Diet Distal Educational workshop Foundations Frequencies Funding Future Health Individual Individual Differences Intervention Knowledge Literature Longitudinal Studies Malignant Neoplasms Measurement Measures Medical Methodology Methods Modeling Morbidity - disease rate Nature Outcome Outcome Measure Pain management Peer Review Performance Persons Physical activity Process Productivity Public Health Publications Quality of life Records Research Research Design Research Personnel Safety Sampling Self Efficacy Series Simulate Smoker Smoking Societies Statistical Methods Technology The Sun Time Tobacco use Translating United States National Institutes of Health Validation Work analytical tool anticancer research cost cost effective critical period diaries effective intervention experience flexibility improved mortality novel psychologic simulation smoking cessation smoking prevalence success symposium tool user friendly software

项目摘要

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.

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增加相关。尽管目前 70% 的吸烟者有动机并尝试戒烟，但自然戒烟和辅助戒烟的比率很低，在 5% 到 10% 之间。日记和生态瞬时评估 (EMA) 方法对于捕捉自然环境中几天内和几天内的动态体验（例如吸烟冲动）至关重要。这些经历不仅反映了戒烟过程的复杂性，而且似乎是戒烟成功的潜在预测因素，目前尚未得到充分研究。在这个项目中，我们解决了一个重要的研究问题，即 EMA 捕获的预测变量对点结果的时变影响。例如，尝试戒烟之前强烈的吸烟冲动可能并不能预示戒烟成功；然而，尝试戒烟后立即经历这些可能是有害的。该项目旨在通过开发新的灵活统计工具来探索吸烟相关过程（通过 EMA 集中测量）对点流行率吸烟结果的时变影响的新研究问题，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，从而增加对成功戒烟障碍的理解。该项目有 3 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、观察之间的不等间距、不同个体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，以识别戒烟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可以预测戒烟成功，b）这些过程的预测潜力如何随时间变化，以及c）戒烟过程中的哪些时期对于确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，并捕获了对尝试自然或辅助戒烟的成年人的吸烟冲动、负面影响、戒烟自我效能以及其他吸烟者的存在的瞬时评估。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各个癌症相关领域的研究人员使用该方法。该提案解决了对复杂的统计方法来分析 EMA 数据的需求，这些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而独特的问题。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。它还将作为未来方法和应用工作的基础。