Can neuroscience dramatically improve our ability to design health communications

描述(由申请人提供) 摘要：神经科学能显著提高我们设计健康沟通的能力吗？在美国和整个发达国家，可改变的健康行为，包括不良饮食、缺乏运动以及烟酒消费，都是导致发病率和死亡率的主要原因2；然而，改变这些行为已被证明是一个极具挑战性的问题。经典的行为改变理论为制定和理解有效的健康运动和干预措施提供了基础；3然而，此类运动的有效性仍然存在相当大的变异性，我们无法预测和解释。通过提高我们理解和预测行为变化的能力，神经成像方法，如功能磁共振成像(FMRl)，可能有助于创建最有效的健康运动。可能有一些行为改变的重要前兆不容易通过自我报告获得，但可以通过fmrl进行评估。特别是，人们预测自己未来行为并通过口头和书面自我报告准确识别内部心理过程的能力是出了名的有限。我们的团队发现，在优先定义的感兴趣神经区域的活动可以使说服性信息传递后个人行为变化中解释的差异比例增加一倍，而不是自我报告测量(e.9。5‘6目前的提议提出了下一个飞跃：神经成像技术也可以应用于更准确地预测人群对健康传播的反应，并可以极大地改进我们设计和选择健康传播的方式。为此，我们建议：(1)识别成功改变人群行为的健康沟通的神经认知特征；(2)使用这些地图来预测新健康信息的成功；以及(3)使用获得的关于信息成功的潜在机制的信息来推进理论和开发新的信息设计策略。我们将采用复杂的多变量和机器学习数据分析技术(e.9。强化学习模型和模式分类)，以表征参与处理成功的健康消息(即，最终促进较大的独立群体中的行为改变的消息)的神经系统。这类技术将提供有关机制的洞察，这些机制导致消息平均对人群最有效，并有助于了解人群中的异质性(即向谁提供可能最有效的消息)。这些技术还将使我们能够定义模型，以最佳方式将神经成像数据与其他可用的数据源(e.9。自我报告)。我们目标的实现(识别预测信息成功的神经模式并测试这些激活的心理意义)将促进更有效的健康信息的设计和传播，并将允许更有效地将核心理论进展转化为行为和疾病特定筒仓的核心理论进展。 与公共健康的相关性：在美国和整个发达国家，可改变的健康行为，包括不良饮食、缺乏运动以及烟酒消费，都是导致发病率和死亡率的主要原因2；然而，改变这些行为已被证明是一个极具挑战性的问题。拟议的研究计划旨在(1)识别成功地在人群水平上改变行为的健康沟通的神经认知特征；(2)使用这些地图来预测新的健康信息的成功；以及(3)使用获得的关于促进信息成功的潜在机制的信息来推进理论。我们目标的实现(识别预测信息成功的神经模式并测试这些激活的心理意义)将促进更有效的健康信息的设计和传播，并将允许更有效地将核心理论进展转化为行为和疾病特定筒仓的核心理论进展。

DESCRIPTION (Provided by the applicant) Abstract: Can neuroscience dramatically improve our ability to design health communications? Modifiable health behaviors including poor diet, physical inactivity, and tobacco and alcohol consumption are leading causes of morbidity and mortaiity, both in the United Statesl and throughout the developed world2; yet changing these behaviors has proved an immensely challenging problem. Classic behavior change theories provide a foundation to develop and understand effective health campaigns and interventions;3 however there is still considerable variability in the effectiveness of such campaigns that we are unable to predict and explain. By improving our ability to understand and predict behavior change, neuroimaging methods such as functional magnetic resonance imaging (fMRl) may aid in the creation of maximally effective health campaigns. There may be important precursors of behavior change that are not easily obtained through self-reports, but that can be assessed with fMRl. In particular, people are notoriously limited in their ability to predict their own future o. behavior and accurately identiy their internal mental processes through verbal and written self-report Our team has found that activity in a prioridefined neural regions of interest can double the proportion of variance explained in individual behavior change following persuasive messaging, beyond self-report measures (e.9. attitudes, intentions, self-efficacy).5'6 The current proposal posits a next leap: neuroimaging technology may also be applied to more accurately forecast population level responses to health communications, and could dramatically improve the way that we design and select health communications. To this end, we propose to: (1) identify the neurocognitive signatures of health communications that are successful at changing behavior at the population level; (2) use these maps to forecast the success of new health messages; and, (3) use the information gained about underlying mechanisms of message success to advance theory and to develop novel strategies for message design. We will employ sophisticated multivariate and machine learning data analysis techniques (e.9. reinforcement learning models and pattern classification) to characterize the neural systems that are involved in processing successful health messages (i.e. messages that ultimately facilitate behavior change in larger, independent groups). Such techniques will provide insight about the mechanisms that lead messages to be optimally effective for populations on average, as well as helping to understand heterogeneity within populations (i.e. for whom are given messages likely to be most effective). These techniques will also allow us to define models that optimally combine neuroimaging data with other available data sources (e.9. self-report). Achievement of our goals (to identify neural patterns that predict message success and to test the psychological meaning of these activations) will facilitate the design and dissemination of more effective health messages, and will allow more efficient translation of core theoretical advances across behavior and disease specific silos. Public Health Relevance: Modifiable health behaviors including poor diet, physical inactivity, and tobacco and alcohol consumption are leading causes of morbidity and mortality, both in the United States1 and throughout the developed world2; yet changing these behaviors has proved an immensely challenging problem. The proposed program of research is designed to (1) identify the neurocognitive signatures of health communications that are successful at changing behavior at the population level; (2) use these maps to forecast the success of novel health messages; and, (3) use the information gained about underlying mechanisms that promote message success to advance theory. Achievement of our goals (to identify neural patterns that predict message success and to test the psychological meaning of these activations) will facilitate the design and dissemination of more effective health messages, and will allow more efficient translation of core theoretical advances across behavior and disease specific silos.