Construal level as a novel pathway for affect regulation and cancer control

7.项目摘要/摘要 在美国，肺癌是可预防死亡的主要原因，吸烟是一种 在80%-90%的病例中是肺癌的贡献者。尽管成人吸烟率有所下降 在过去的50年里，在某些群体中，这一比例仍然高达30%，例如生活在 在贫困中。戒烟很困难：一次戒烟尝试导致戒烟的比例不到10%，而大多数情况下 成年吸烟者曾多次尝试戒烟，但都以失败告终。我们迫切需要的是 新的戒烟干预措施通过不同的机制运作 以现有干预措施为目标，这些干预措施很可能对持续吸烟者无效。一道障碍 进展的原因是大多数治疗方法的作用机制尚不清楚，这使得人们很难知道 哪种治疗方法对谁最有效。我们求助于情感科学来确定一种候选技术 可以作为一种新型干预措施的基础。对情绪调节的研究通常集中在羽绒- 调节情感状态，如对香烟的渴望，使用努力策略，如认知 重新评估。然而，情感调控的一个新见解是，人们可以解释或主观地理解， 具有不同抽象级别的事件，并在高级别与低级别中解释与健康相关的行为 术语在几个领域促进健康行为。例如，想戒烟的吸烟者更有可能 当他们用更抽象、更高级的术语来解释相同的事件(例如，“禁欲”)时，拒绝吸烟 (例如，“成为一个更好的我”)与更具体、更低级的术语(例如，“不抽这支烟”)。那里 是否有证据表明，高层解释可能依赖于不同于传统情感调控的机制 以及减少吸烟的干预措施，但其作用机制尚不清楚。直接比较其 替代影响调节策略的机制和开发工具以诱导高水平 识解是开发一种新的干预方法的下一步。另外，建立个人 高水平解释效果的差异将使未来的干预能够针对个人 对他们来说，它们将是最有效的。我们确定了两个候选机制，通过这些机制，高级别 识解可能起作用：下调渴望和上调目标活力(即，动机 退出)。功能磁共振成像(FMRI)揭示了这些过程所涉及的神经系统 变得与众不同。因此，我们将使用fMRI数据的多变量分析来量化高级解释的相似性 给每个候选人(目标1)。这将在4种情况下的纵向平移实验中完成。 水平解释，下调渴望，上调目标能量，并照常对待-在 贫困中的持续吸烟者样本，他们最有可能受益于一种基于理论的新颖的 治疗。样本量(N=240)提供了对高水平影响的个体差异的检验 对神经活动和渴望的解释，以及它们预测吸烟减少的程度(目标2)。

7. Project Summary/Abstract Lung cancer is the leading major cause of preventable death in the United States, and cigarette smoking is a contributor to lung cancer in 80%–90% of cases. Though adult cigarette smoking rates have declined substantially during the past 50 years, they remain as high as 30% in certain groups, such as individuals living in poverty. Quitting is difficult: a given quit attempt results in cessation in fewer than 10% of cases, and most adult cigarette smokers have attempted and failed to quit, and often many times. What is urgently needed are novel interventions for cigarette smoking cessation that operate through different mechanisms from those targeted by existing interventions, which are likely to have been unsuccessful for persistent smokers. A barrier to progress is that the mechanisms of action of most treatments are not known, which makes it difficult to know which treatment will work best for whom. We turn to affective science to identify a candidate technique that could serve as the basis for a novel intervention. Research on affect regulation typically focuses on down- regulation of affective states, such as craving for cigarettes, using effortful strategies such as cognitive reappraisal. However, a new insight in affect regulation is that people can construe, or subjectively understand, events with varying levels of abstraction, and that construing health-related behaviors in high- versus low-level terms promotes health behavior in several domains. For example, smokers who want to quit are more likely to resist a cigarette when they construe the same event (e.g., “abstinence”) in more abstract, high-level terms (e.g., “becoming a better me”) versus more concrete, low-level terms (e.g., “not smoking this cigarette”). There is some evidence that high-level construal might rely on distinct mechanisms from traditional affect regulation and smoking reduction interventions, but its mechanisms of action are unknown. Directly comparing its mechanisms to those of alternative affect regulation strategies and developing tools to induce high-level construal are the next steps on the path toward developing a novel intervention. Also, establishing individual differences in the effects of high-level construal will allow future interventions to be targeted to the individuals for whom they will be maximally effective. We identified two candidate mechanisms through which high-level construal might operate: down-regulation of craving and up-regulation of goal energization (i.e., motivation to quit). Functional magnetic resonance imaging (fMRI) revealed the neural systems engaged by those processes to be distinct. So, we will use multivariate analyses of fMRI data to quantify the similarity of high-level construal to each candidate (Aim 1). This will be done in a longitudinal translational experiment with 4 conditions—high- level construal, down-regulation of craving, up-regulation of goal energization, and treatment-as-usual—in a sample of persistent smokers in poverty, who are the most likely to benefit from a novel, theory-based treatment. The sample size (N = 240) affords an examination of individual differences in the effect of high-level construal on neural activity and craving, and the degree to which they predict smoking reduction (Aim 2).