Affective science and smoking cessation: Real time real world assessment

吸烟在近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，绝大多数人尝试戒烟 失败众多的概念模型以及大量的经验证据都强调， 情感是戒烟的重要决定因素。不幸的是，我们对它是如何运作的知之甚少。 不同情绪和其他因素的星座和时间动态在真实的中发挥作用， 时间在真实的世界中影响失效风险。这种知识的缺乏严重阻碍了我们的 概念模型和我们最佳干预的能力。因此， 本研究旨在建立一个更详细和全面的概念模型， 自我调节中的不同情绪，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 可以针对真实的时间、真实的世界机制的行为。研究建议直接 解决了PAR的几个目标，包括不同情绪的影响， 他们对癌症风险行为的时间进程，不同情绪的作用是否被改变， 其他情绪的存在（例如，“混合”情绪状态），以及 情感体验受语境的影响。拟在300名受试者中进行纵向队列研究， 试图戒烟的吸烟者受到基于情感科学的概念框架的指导 以及自我调节和成瘾的概念模型。参与者将从1 戒烟前一周至戒烟后6个月。他们将从1周开始评估 使用AutoSense地理定位系统，从戒烟前日期到戒烟后2周日期 (GPS)生态瞬时评价（EMA）。AutoSense、GPS和EMA收集真实的 在自然环境中的时间数据，彼此无线通信，并且数据被 在智能手机上进行真实的处理。AutoSense检测特定的行为和生理 吸烟的“特征”（主要结果）和自我调节能力（中间结果） 结果;使用高频心率变异性进行评估）。GPS真实的时空 跟踪将与环境的空间和时间相关特征相联系 利用地理信息系统（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 学习方法来模拟真实的时间的动态，真实的世界之间的不同关联， 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.