Affective science and smoking cessation: Real time real world assessment

情感科学和戒烟：实时现实世界评估

基本信息

批准号：
10545164
负责人：
Cho Yan Lam
金额：
$ 60.01万
依托单位：
UNIVERSITY OF UTAH
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-01-19 至 2024-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10545164
关键词：
Address Affect Affective Algorithms Anger Behavioral Cause of Death Cellular Phone Characteristics Cognition Complement Complex Data Depressed mood Ecological momentary assessment Emotional Emotions Environment Failure Foundations Frequencies Geographic Information Systems Geography Happiness Home Informal Social Control Intervention Knowledge Link Longitudinal cohort study Measurement Mediating Mediator Modeling Outcome Participant Patient Self-Report Physiological Play Positioning Attribute Process Proliferating Public Health Real-Time Systems Reporting Research Risk Risk Behaviors Risk Reduction Role Science Self Assessment Smoker Smoking Smoking Cessation Intervention Stress System Testing Time Tobacco Tobacco use Volition adaptive intervention addiction advanced analytics cancer prevention cancer risk cancer type disability effective intervention experience heart rate variability knowledge base machine learning prediction mobile computing negative affect primary outcome risk prediction model role model smoking cessation success wireless communication

项目摘要

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.

烟草使用与近 20 种不同类型的癌症有关，尽管吸烟戒烟是降低癌症风险的基石，绝大多数戒烟尝试许多概念模型以及大量的经验证据都强调了这一点。不幸的是，影响是戒烟的一个重要决定因素，但我们对其具体机制知之甚少。不同情绪和其他因素的星座和时间动态在真实中发挥作用这种知识的缺乏严重伤害了我们在现实世界中影响失误风险的时间。概念模型和我们最佳干预的能力因此是总体目标。这项研究的目的是创建一个更详细和更全面的角色概念模型自我调节的独特情绪，以及技术、经验和分析基础有必要制定有效的干预措施来戒烟和其他癌症风险可以直接针对实时、现实世界机制的行为。解决 PAR 的几个目标，包括不同情绪的影响和他们对癌症风险行为的时间进程，不同情绪的作用是否是由其他情绪的存在（例如“混合”情绪状态），以及这些情绪的影响如何拟议的纵向队列研究涉及 300 人。试图戒烟的吸烟者受到基于情感科学的概念框架的指导自我调节和成瘾的概念模型将从 1 开始进行跟踪。戒烟日期前一周至戒烟后 6 个月期间他们将从 1 周开始接受评估。使用 AutoSense、地理定位系统的戒烟前日期至戒烟后 2 周的日期 (GPS) 和生态瞬时评估 (EMA)、AutoSense、GPS 和 EMA 采集真实数据。自然环境中的时间数据，相互无线通信，数据 AutoSense 会在智能手机上实时处理，检测特定的行为和生理状况。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实时评估）。跟踪将与环境的空间和时间相关特征联系起来使用地理信息系统 (GIS) 数据评估自我报告的情绪，分析利用先进的动态风险预测模型和机器。学习方法来模拟不同事物之间实时、现实世界关联的动态情绪、SRC 和失效。