Eliminating Tobacco-Related Disparities amount African American Smokers

消除与烟草相关的差异 非洲裔美国吸烟者数量

• 批准号: 9902662
• 负责人: Cho Yan Lam
• 金额: $ 10.27万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-03-03 至 2021-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9902662
• 关键词: Abstinence; Acute; Affect; African American; Area; Award; Behavioral; Big Data to Knowledge; Cause of Death; Cellular Phone; Characteristics; Cigarette; Crime; Data; Disadvantaged; Discrimination; Distal; Drug usage; Ecological momentary assessment; Economics; Environment; Environmental Exposure; Environmental Risk Factor; Exposure to; Frequencies; Geographic Information Systems; Geography; Health; Individual; Infrastructure; Intervention; Letters; Link; Location; Longitudinal cohort study; Machine Learning; Mental Health; Methodology; Methods; Modeling; Morbidity - disease rate; Motivation; Nicotine Dependence; Outcome; Participant; Pathway interactions; Patient Self-Report; Pattern; Physiological; Policies; Population; Positioning Attribute; Poverty; Predisposition; Process; Publications; Recording of previous events; Reporting; Research; Risk Factors; Science; Self Efficacy; Severities; Smoke; Smoker; Smoking; Social support; Stress; Surgeon; System; Time; Tobacco; Tobacco use; Trust; Underserved Population; United States National Institutes of Health; Volition; Wireless Technology; base; behavior change; biobehavior; built environment; cohesion; contextual factors; data to knowledge; demographics; disability; distress tolerance; experience; health disparity; indexing; innovation; interest; mindfulness; mortality; negative affect; novel; predictive modeling; psychosocial; public health relevance; sensor; smoking abstinence; smoking cessation; social; social cognitive theory; social health determinants; tool; wearable device

 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.

 描述（由申请人提供）：吸烟是美国导致死亡和残疾的主要可预防原因，也是造成健康差异的主要原因。与白人吸烟者相比，非洲裔美国人 (AA) 吸烟者的烟草相关发病率和死亡率总体较高，戒烟率较低。不幸的是，由于缺乏对这一人群戒烟机制的研究，严重阻碍了寻找有效的政策和干预措施来减少AA人群吸烟。这项纵向队列研究将考察 300 名试图戒烟的 AA 吸烟者中人口统计学和社会史、生物行为/心理社会倾向、背景和环境影响以及急性瞬时诱因对戒烟和戒烟的影响。这项研究以一个总体概念框架为指导，该框架源自健康的社会决定因素模型、社会认知理论和先前的实证研究结果。将使用实时、基于现场的科学方法对参与者进行评估，其中包括生态瞬时评估 (EMA)、AutoSense 和地理定位系统 (GPS)。 AutoSense 实时跟踪行为和生理数据，可以客观地检测个人何时吸烟或经历负面情绪/压力。 GPS 允许对位置模式进行实时空间测绘，可与 EMA 和 Autosense 数据以及使用地理信息系统数据的相关环境暴露/特征（例如，烟草店暴露；地区贫困）配对。感兴趣的主要结果是通过 AutoSense 实时确定戒烟情况，以及早期和长期戒烟。这项研究将是第一个将戒烟研究中的客观和动态指标、负面情绪/压力和关键环境影响结合起来的研究。全面的多方法方法是该领域的重大进步，因为它消除了与完全依赖自我报告关键结果和预测因素相关的问题。此外，这是其中之一 第一项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大量数据，并包括动态预测模型的框架，这是一种新颖的统计方法。研究结果将为制定旨在减少 AA 所经历的与吸烟相关的深刻差异的政策和干预措施提供参考。