Can neuroscience dramatically improve our ability to design health communications

神经科学能否显着提高我们设计健康沟通的能力

基本信息

批准号：
8727801
负责人：
Emily Falk
金额：
$ 219.26万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-09-30 至 2017-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8727801
关键词：
neuroscience dramatically improve our ability

项目摘要

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.

描述（申请人提供）摘要：神经科学能否显着提高我们设计健康沟通的能力？在美国以及整个发达的World2中，包括饮食不良，身体不活跃，烟草和饮酒，包括饮食不良，烟草和饮酒的原因是发病率和抵押的主要原因；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开发和理解有效的健康运动和干预提供了基础； 3然而，我们无法预测和解释的此类运动的有效性仍然存在很大的差异。通过提高我们理解和预测行为变化的能力，神经影像学方法（例如功能磁共振成像（FMRL））可以帮助创建最大有效的健康运动。行为改变的重要前体可能不容易通过自我报告获得，但是可以用FMRL评估。尤其是，人们在预测自己的未来o的能力上受到限制。行为并通过口头和书面自我报告准确地识别其内部心理过程，我们的团队发现，在先验构成的感兴趣的神经区域中的活动可以使人有说服力的消息传递后的个体行为变化中解释的方差比例增加一倍，超出了自我报告措施（e） .9。选择健康通信。为此，我们建议：（1）确定健康传播的神经认知签名，这些神经认知能够成功地改变人群的行为；（2）使用这些地图预测新的健康消息的成功；（3）使用有关信息成功的潜在机制获得的信息来推进理论并制定新的消息设计策略。我们将采用复杂的多元和机器学习数据分析技术（E.9。强化学习模型和模式分类）来表征与处理成功的健康消息有关的神经系统（即最终促进较大，独立群体的行为变化的消息）。这种技术将提供有关导致信息平均对人群最佳有效的机制的见解，并有助于了解人群中的异质性（即给出的消息可能最有效）。这些技术还将使我们能够定义将神经成像数据与其他可用数据源（E.9。自我报告）相结合的模型。实现我们的目标（确定可以预测信息成功并测试这些激活的心理意义的神经模式）将有助于设计和传播更有效的健康信息，并将允许对行为和特定疾病的核心理论进步更有效地翻译筒仓。公共卫生相关性：可修改的健康行为，包括饮食不良，身体不活跃，烟草和饮酒是在美国和整个发达世界2的主要原因。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）确定健康通信的神经认知签名，这些神经认知能够成功地改变人群的行为；（2）使用这些地图来预测新型健康信息的成功；（3）使用有关促进信息成功以推进理论的潜在机制获得的信息。实现我们的目标（确定可以预测信息成功并测试这些激活的心理意义的神经模式）将有助于设计和传播更有效的健康信息，并将允许对行为和特定疾病的核心理论进步更有效地翻译筒仓。