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

• 批准号: 8355324
• 负责人: Emily Falk
• 金额: $ 14万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-30 至 2013-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8355324
• 关键词: Achievement; Alcohol consumption; Attitude; Behavior; Classification; Data; Data Analyses; Data Sources; Diet; Disease; Effectiveness; Foundations; Functional Magnetic Resonance Imaging; Future; Goals; Health; Health Campaign; Health Communication; Health behavior; Heterogeneity; Individual; Intervention; Lead; Learning; Machine Learning; Maps; Measures; Mental Processes; Methods; Modeling; Morbidity - disease rate; Neurocognitive; Neurosciences; Patient Self-Report; Pattern; Population; Population Projection; Process; Psychological Tests; Psychological reinforcement; Research; Self Efficacy; System; Techniques; Technology; Tobacco use; Translations; Writing; abstracting; behavior change; design; improved; insight; interest; mortality; neural patterning; neuroimaging; novel; novel strategies; programs; public health relevance; relating to nervous system; response; success; theories

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（由申请人提供） 翻译后摘要：神经科学可以显着提高我们的能力，设计健康的通信？可改变的健康行为，包括不良饮食、缺乏身体活动以及吸烟和饮酒，是美国和整个发达国家发病率和死亡率的主要原因;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发展和理解有效的健康运动和干预措施提供了基础;3然而，这种运动的有效性仍然存在相当大的差异，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，功能性磁共振成像（fMRI）等神经成像方法可能有助于创建最有效的健康活动。可能存在行为改变的重要前兆，其不容易通过自我报告获得，但可以用fMRI评估。特别是，众所周知，人们预测自己未来的能力有限。行为和准确识别他们的内部心理过程通过口头和书面的自我报告我们的团队已经发现，在一个优先定义的感兴趣的神经区域的活动可以加倍的方差解释的比例在个人行为改变后，有说服力的消息，超越自我报告措施（e.9. 5 '6目前的提案提出了下一个飞跃：神经成像技术也可能被应用于更准确地预测人口对健康传播的反应，并可能大大改善我们设计和选择健康传播的方式。为此，我们建议：（1）识别在人群水平上成功改变行为的健康传播的神经认知特征;（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并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）来表征参与处理成功的健康消息（即最终促进较大独立群体中的行为改变的消息）的神经系统。这些技术将提供关于导致信息对平均人群最有效的机制的见解，并有助于理解人群中的异质性（即，对谁来说，信息可能最有效）。这些技术还将允许我们定义最佳地将联合收割机神经成像数据与其他可用数据源相结合的模型（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，并将允许更有效地翻译跨行为和疾病特定筒仓的核心理论进展。 公共卫生相关性：在美国1和整个发达国家2，可改变的健康行为，包括不良饮食、缺乏身体活动以及吸烟和饮酒，是发病率和死亡率的主要原因;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）确定在人群水平上成功改变行为的健康传播的神经认知特征;（2）使用这些地图来预测新健康信息的成功;以及（3）使用获得的有关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，并将允许更有效地翻译跨行为和疾病特定筒仓的核心理论进展。