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:
- 金额:$ 219.26万
- 依托单位:
- 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:
- 关键词:
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(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。
项目成果
期刊论文数量(17)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Brain and Social Networks: Fundamental Building Blocks of Human Experience.
- DOI:10.1016/j.tics.2017.06.009
- 发表时间:2017-09
- 期刊:
- 影响因子:19.9
- 作者:Falk EB;Bassett DS
- 通讯作者:Bassett DS
Social networks and neural receptivity to persuasive health messages.
- DOI:10.1037/hea0001059
- 发表时间:2021-04
- 期刊:
- 影响因子:0
- 作者:Pandey P;Kang Y;Cooper N;O'Donnell MB;Falk EB
- 通讯作者:Falk EB
Neural bases of recommendations differ according to social network structure.
- DOI:10.1093/scan/nsw158
- 发表时间:2017-01-01
- 期刊:
- 影响因子:4.2
- 作者:O'Donnell MB;Bayer JB;Cascio CN;Falk EB
- 通讯作者:Falk EB
Big data in the new media environment.
新媒体环境下的大数据。
- DOI:10.1017/s0140525x13001672
- 发表时间:2014
- 期刊:
- 影响因子:0
- 作者:O'Donnell,MatthewBrook;Falk,EmilyB;Konrath,Sara
- 通讯作者:Konrath,Sara
Deliberation and Valence as Dissociable Components of Counterarguing among Smokers: Evidence from Neuroimaging and Quantitative Linguistic Analysis.
深思熟虑和效价作为吸烟者反驳的可分离成分:来自神经影像和定量语言分析的证据。
- DOI:10.1080/10410236.2020.1712521
- 发表时间:2021
- 期刊:
- 影响因子:3.9
- 作者:Liu,Jiaying;O'Donnell,MatthewB;Falk,EmilyB
- 通讯作者:Falk,EmilyB
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Emily Falk其他文献
Emily Falk的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Emily Falk', 18)}}的其他基金
Cancer prevention through neural and geospatial examination of tobacco marketing effects in smokers
通过神经和地理空间检查烟草营销对吸烟者的影响来预防癌症
- 批准号:
9906870 - 财政年份:2019
- 资助金额:
$ 219.26万 - 项目类别:
Cancer prevention through neural and geospatial examination of tobacco marketing effects in smokers
通过神经和地理空间检查烟草营销对吸烟者的影响来预防癌症
- 批准号:
10469308 - 财政年份:2019
- 资助金额:
$ 219.26万 - 项目类别:
PQA - 3: Neural predictors of receptivity to health communication and behavior ch
PQA - 3:健康沟通和行为接受度的神经预测因子
- 批准号:
8590270 - 财政年份:2013
- 资助金额:
$ 219.26万 - 项目类别:
PQA - 3: Neural predictors of receptivity to health communication and behavior ch
PQA - 3:健康沟通和行为接受度的神经预测因子
- 批准号:
8733640 - 财政年份:2013
- 资助金额:
$ 219.26万 - 项目类别:
Neural predictors of risky driving and susceptibility to peer influences in adole
阿多危险驾驶和对同伴影响的敏感性的神经预测因素
- 批准号:
8706932 - 财政年份:2013
- 资助金额:
$ 219.26万 - 项目类别:
Neural predictors of risky driving and susceptibility to peer influences in adole
阿多危险驾驶和对同伴影响敏感性的神经预测因子
- 批准号:
8512122 - 财政年份:2013
- 资助金额:
$ 219.26万 - 项目类别:
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 - 财政年份:2012
- 资助金额:
$ 219.26万 - 项目类别:
相似海外基金
Development and deployment of novel strategies to dramatically increase the efficiency of intracellular delivery of extracellular vesicles
开发和部署新策略以显着提高细胞外囊泡的细胞内递送效率
- 批准号:
23KJ1368 - 财政年份:2023
- 资助金额:
$ 219.26万 - 项目类别:
Grant-in-Aid for JSPS Fellows
Novel Optimisation and Prediction models to dramatically grow and expand flight-free travel
新颖的优化和预测模型可大幅增长和扩大免航班旅行
- 批准号:
10080586 - 财政年份:2023
- 资助金额:
$ 219.26万 - 项目类别:
Collaborative R&D
Study for developent of innovative acute brain protection device to dramatically improve functional outcome after severe ischemic stroke
研究开发创新型急性脑保护装置以显着改善严重缺血性中风后的功能结果
- 批准号:
23K14790 - 财政年份:2023
- 资助金额:
$ 219.26万 - 项目类别:
Grant-in-Aid for Early-Career Scientists
Mechanism and regulation of splicing in the dramatically reduced spliceosome of C. merolae
C. merolae 剪接体显着减少的剪接机制和调控
- 批准号:
RGPIN-2017-04783 - 财政年份:2022
- 资助金额:
$ 219.26万 - 项目类别:
Discovery Grants Program - Individual
Mechanism and regulation of splicing in the dramatically reduced spliceosome of C. merolae
C. merolae 剪接体显着减少的剪接机制和调控
- 批准号:
RGPIN-2017-04783 - 财政年份:2021
- 资助金额:
$ 219.26万 - 项目类别:
Discovery Grants Program - Individual
STTR Phase I:Dramatically improving algorithm execution on quantum devices through advanced noise-awareness and control
STTR 第一阶段:通过先进的噪声感知和控制显着改善量子设备上的算法执行
- 批准号:
2036347 - 财政年份:2021
- 资助金额:
$ 219.26万 - 项目类别:
Standard Grant
High temperature resistance mechanism of Japonica rice that dramatically improve the quality of brown rice under high temperature ripening
粳米高温催熟显着提高糙米品质的抗高温机理
- 批准号:
21K05554 - 财政年份:2021
- 资助金额:
$ 219.26万 - 项目类别:
Grant-in-Aid for Scientific Research (C)
A Revolutionary Fintech Predicted to Dramatically Reduce the Cost of Automated Payments for Businesses and Customers in a Post-COVID 19 World
革命性的金融科技预计将大幅降低后 COVID 19 世界中企业和客户的自动支付成本
- 批准号:
10004434 - 财政年份:2021
- 资助金额:
$ 219.26万 - 项目类别:
Collaborative R&D
Basic research on drugs that can dramatically improve the effectiveness of BNCT in multifaceted cancer control
可显着提高BNCT在多方面癌症控制中的有效性的药物基础研究
- 批准号:
21K19449 - 财政年份:2021
- 资助金额:
$ 219.26万 - 项目类别:
Grant-in-Aid for Challenging Research (Exploratory)
A New Proposal and Demonstration of Highly Efficient System Control for Dramatically Increasing the Throughput of Multibeam Satellite Communication System
大幅提高多波束卫星通信系统吞吐量的高效系统控制的新建议和论证
- 批准号:
21K04052 - 财政年份:2021
- 资助金额:
$ 219.26万 - 项目类别:
Grant-in-Aid for Scientific Research (C)