Socioeconomic status, stress, and smoking cessation

 描述（由申请人提供）： 吸烟是健康差异的主要原因，社会经济地位（SES）与较低的戒烟率密切相关。已经提出了几个主要的概念模型，它们共享一个关键的共同机制，将SES与健康行为（如戒烟）联系起来，并指出与低SES相关的生活环境会导致更大的压力暴露，从而影响行为。不幸的是，寻找有效的政策和干预措施，以减少低社会经济地位的个人吸烟是严重阻碍了缺乏研究的机制，在这一人群戒烟。这项纵向队列研究将探讨SES和社会历史，上下文和环境的影响，生物行为/心理社会倾向，和急性瞬时沉淀物的压力，吸烟失误，并在300名吸烟者试图戒烟禁欲的影响。这项研究是由一个总体的概念框架，来自健康的社会决定因素，社会认知理论和以前的实证研究结果的模型。参与者将使用实时，基于实地的科学方法进行评估，包括Autosense，生态瞬时评估（EMA）和地理定位系统（GPS）。Autosense实时跟踪行为和生理数据，并可以客观地检测个人何时吸烟或遇到压力。GPS允许位置模式的实时空间映射，其可以与EMA和Autosense数据以及相关环境暴露/特征（例如，烟草出口暴露;地区级贫困）使用地理信息系统数据。感兴趣的主要结果是通过Autosense在真实的时间内确定的失误和压力，以及早期和长期戒烟。这项研究将是有史以来第一个联合收割机的客观和动态指标的吸烟时间，压力和关键的环境影响的研究戒烟。全面的，多方法的方法是该领域的一个重大进步，因为它消除了与完全依赖自我报告的关键结果和预测因素有关的问题。此外，这是第一项包括基于经验的机器学习方法的研究，以充分挖掘由真实的时间评估方法产生的大量数据，并包括动态预测模型的框架，一种新的统计方法。研究结果将有助于制定政策和干预措施，以减少低社会经济地位个人所经历的与吸烟有关的深刻差异。

 DESCRIPTION (provided by applicant): Smoking is a major cause of health disparities, and socioeconomic status (SES) is strongly associated with lower rates of smoking cessation. Several major conceptual models have been proposed that share a key, common mechanism linking SES to health behaviors such as cessation, noting that the life circumstances associated with low SES lead to greater exposure to stress, which then influences behavior. Unfortunately, the search for effective policies and interventions to reduce smoking among low SES individuals is severely hampered by the paucity of research on the mechanisms underlying cessation in this population. This longitudinal cohort study will examine the influence of SES and social history, contextual and environmental influences, biobehavioral/psychosocial predispositions, and acute momentary precipitants on stress, smoking lapse, and abstinence among 300 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 using real-time, field-based, state of the science methodologies consisting of Autosense, ecological momentary assessment (EMA), and geographic positioning system (GPS). Autosense tracks behavioral and physiologic data in real-time and can objectively detect when an individual smokes or encounters a stressor. 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 and stress 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, 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 the first study 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 low SES individuals.