Eliminating Tobacco-Related Disparities amount African American Smokers

 描述(申请人提供)：在美国，吸烟是主要的可预防的死亡和残疾原因，也是造成健康差距的主要原因。与白人吸烟者相比，非裔美国人(AA)吸烟者与烟草相关的总体发病率和死亡率更高，戒烟率更低。不幸的是，由于缺乏对AAA人群戒烟机制的研究，寻求有效的政策和干预措施来减少AA中的吸烟严重受阻。这项纵向队列研究将考察人口统计学和社会历史、生物行为/心理社会倾向、背景和环境影响以及急性瞬间诱因对300名试图戒烟的AA吸烟者的吸烟失误和戒烟行为的影响。这项研究是以一个总体概念框架为指导的，该框架来源于健康的社会决定因素模型、社会认知理论和先前的经验发现。参与者将使用基于现场的实时科学方法进行评估，这些方法包括生态瞬时评估(EMA)、自动感知和地理定位系统(GPS)。AutoSense实时跟踪行为和生理数据，并可以客观地检测到个人何时吸烟或经历负面影响/压力。全球定位系统允许使用地理信息系统数据实时绘制位置模式的空间地图，这些模式可以与EMA和Autosense数据配对，并与相关的环境暴露/特征(例如，烟草销售点暴露；地区水平的贫困)配对。感兴趣的主要结果是通过AutoSense以及早期和长期戒烟实时确定的。这项研究将首次在戒烟研究中结合客观和动态的吸烟失误、负面情绪/压力和关键环境影响指标。综合、多方法方法是该领域的一项重大进步，因为它消除了与完全依赖自我报告获得关键成果和预测因素有关的问题。此外，这也是一款 首次研究包括基于经验的机器学习方法，以充分挖掘实时评估方法产生的海量数据，并包括动态预测模型的框架，这是一种新的统计方法。这一结果将为制定政策和干预措施提供参考，这些政策和干预措施旨在减少与吸烟有关的严重差距。

 DESCRIPTION (provided by applicant): Smoking is the leading preventable cause of death and disability in the U.S., and a major cause of health disparities. African American (AA) smokers have higher overall rates of tobacco-related morbidity and mortality and lower smoking cessation rates than do white smokers. Unfortunately, the search for effective policies and interventions to reduce smoking among AAs is severely hampered by the paucity of research on the mechanisms underlying smoking cessation in this population. This longitudinal cohort study will examine the influence of demographics and social history, biobehavioral/psychosocial predispositions, contextual and environmental influences, and acute momentary precipitants on smoking lapse and abstinence among 300 AA smokers attempting to quit. This study is guided by an overarching conceptual framework derived from models of the social determinants of health, social cognitive theories, and prior empirical findings. Participants will be assessed usin real-time, field-based, state of the science methodologies consisting of ecological momentary assessment (EMA), AutoSense, and geographic positioning system (GPS). AutoSense tracks behavioral and physiologic data in real-time and can objectively detect when an individual smokes or experiences negative affect/stress. GPS permits real-time spatial mapping of location patterns, which can be paired with EMA and Autosense data, and with relevant environmental exposures/characteristics (e.g., tobacco outlet exposure; area-level poverty) using geographic information system data. Principal outcomes of interest are lapse ascertained in real time through AutoSense, and early and long-term abstinence from smoking. This research would be the first to ever combine objective and dynamic indices of smoking lapse, negative affect/stress, and key environmental influences in the study of smoking cessation. The comprehensive, multi-method approach is a major advance for the field as it eliminates problems related to an exclusive reliance on self-report for key outcomes and predictors. In addition, this is one off the first studies to include empirically based machine learning approaches to fully mine the voluminous body of data yielded by real time assessment approaches, and to include the framework of dynamic prediction models, a novel statistical approach. The results will inform the tailoring of policies and interventions targeted at reducing the profound smoking-related disparities experienced by AAs.