Affective science and smoking cessation: Real time real world assessment

烟草的使用在近20种不同类型的癌症中起着因果作用，尽管吸烟 戒烟是降低癌症风险的基石，绝大多数戒烟尝试 失败了。许多概念模型以及大量的经验证据都强调了这一点 情绪是戒烟的一个强有力的决定因素。不幸的是，人们对此知之甚少。 不同情绪和其他因素的星座和时间动态在现实中上演 在现实世界中影响失误风险的时间。这种知识的缺乏严重阻碍了我们 概念模型和我们以最佳方式干预的能力。因此，总的目标是 本研究旨在建立一个更详细、更全面的角色概念模型 自我调节中的独特情绪，以及技术、经验和分析基础 有必要为戒烟和其他癌症风险制定有效的干预措施 可以针对实时、真实世界机制的行为。建议的研究直接 从标准杆上解决了几个目标，包括不同情绪和 他们在癌症危险行为上的时间进程，不同情绪的作用是否被改变 其他情绪(例如，混合的情绪状态)的存在，以及 情感体验会受到语境的影响。建议在300人中进行纵向队列研究 试图戒烟的吸烟者受到以情感科学为基础的概念框架的指导 以及自我调节和上瘾的概念模型。参赛者将从1开始跟踪 戒烟日期前一周至戒烟后6个月。他们将从1周开始进行评估 使用AutoSense地理定位系统，戒烟前日期至戒烟后2周 (GPS)和生态瞬时评估(EMA)。AutoSense、GPS和EMA收集REAL 自然环境中的时间数据，相互无线通信，数据 在智能手机上实时处理。AutoSense检测特定的行为和生理 吸烟(主要结果)和自我调节能力(中间产物)的“特征” 结果；使用高频心率变异性进行评估)实时。GPS实时空间 跟踪将与空间和时间上相关的环境特征联系起来 使用地理信息系统(GIS)数据。EMA评估自我报告的情绪， 认知和语境。分析利用先进的动态风险预测模型和机器 学习方法来模拟实时、真实世界中不同的 情绪、SRC和失误。

Tobacco use plays a causal role in almost 20 different types of cancer, and although smoking cessation is a cornerstone of cancer risk reduction, the vast majority of smoking quit attempts fail. Numerous conceptual models, as well as a large body of empirical evidence, underscore that affect is a potent determinant of smoking lapse. Unfortunately, very little is known about how the constellation and temporal dynamics of distinct emotions and other factors play out in real time in the real world to influence lapse risk. This lack of knowledge severely hampers both our conceptual models and our ability to optimally intervene. Thus, the overarching objectives of this research are to create a more detailed and comprehensive conceptual model of the role of distinct emotions in self-regulation, as well as the technical, empirical, and analytic foundation necessary to develop effective interventions for smoking cessation and other cancer risk behaviors that can target real time, real world mechanisms. The proposed research directly addresses several objectives from the PAR including the influence of distinct emotions and their time course on cancer risk behaviors, whether the role of distinct emotions is altered by the presence of other emotions (e.g., “blended” emotional states), and how the influence of affective experience is modified by context. The proposed longitudinal cohort study among 300 smokers attempting to quit is guided by a conceptual framework grounded in affective science and conceptual models of self-regulation and addiction. Participants will be followed from 1 week prior to their quit date through 6 months post-quit date. They will be assessed from 1 week pre-quit date through 2 weeks post-quit date using AutoSense, geographic positioning system (GPS), and ecological momentary assessment (EMA). AutoSense, GPS, and EMA collect real time data in natural environments, communicate wirelessly with each other, and data are processed in real time on a smartphone. AutoSense detects specific behavioral and physiologic “signatures” of smoking (the primary outcome) and self regulatory capacity (an intermediate outcome; assessed using high frequency heart rate variability) in real time. GPS real time spatial tracking will be linked with spatially and temporally relevant characteristics of the environment using geographic information system (GIS) data. EMAs assess self-reported emotions, cognition, and context. Analyses utilize advanced dynamic risk prediction models and machine learning approaches to model the dynamics of real time, real world associations among distinct emotions, SRC, and lapse.