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Content-based social network analysis methods for data-driven health promotion

基于内容的社交网络分析方法，用于数据驱动的健康促进

基本信息

批准号：
9146395
负责人：
SAHITI MYNENI
金额：
$ 17.33万
依托单位：
UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-18 至 2018-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9146395
关键词：
Behavior Behavior Therapy Behavioral Mechanisms Characteristics Chronic Chronic Disease Cognitive Communication Communities Data Data Set Dependency Development Discipline Disease Management Event Evolution Graph Health Health Promotion Health Status Health behavior Health behavior change Human Individual Intervention Knowledge Life Link Maps Measures Mediating Methods Modeling Nature Network-based Outcome Pathway Analysis Patients Pattern Plant Roots Population Qualitative Methods Research Research Personnel Research Proposals Role Self Management Semantics Social Network Structure Techniques Technology Text Time Withholding Treatment Work acronyms base behavior change behavioral construct design diaries digital experience improved insight interest longitudinal analysis member network models novel online community peer smoking cessation social social model social networking website theories

项目摘要

DESCRIPTION (provided by applicant): The effects of social relationships on health behavior change and management of chronic conditions has been well documented. Investigators from a variety of disciplines have attempted to understand and harness the role of these social ties in health promotion. Online communities, which digitize peer-to-peer communication, provide a unique opportunity to researchers to understand the mechanisms underlying human behavior change. The onset of digital communication platforms has led investigators to question the use of existing behavior change theories that were formulated in the context of face-to-face communication. Manifestations of socio-behavioral constructs in online communities are often embedded in the content of the messages exchanged by users. Most studies of online and offline social networks have not considered communication content of peer-to-peer interactions when attempting to discern the theoretical underpinnings of network influence. In the proposed research, we will develop a framework for the incorporation of message content into network-based models of social influence. While generally applicable, the methods will be developed based on communications among individuals attempting to quit smoking in an online community, QuitNet, the first online social network for smoking cessation. The methods will be extended to analyze communication among members managing chronic conditions on TreatmentDiaries, an online community where patients share their daily experiences of disease management. Combining qualitative methods with automated text analysis, our work will facilitate high-throughput empirical analysis of user communication in online social networks allowing the identification of emergent user needs and community culture. Temporal models that capture the evolution of cognitive factors pertinent to health behavior change and chronic disease management will be developed. Content-specific communication characteristics of network influence and dependencies will be studied by adapting affiliation exposure models and exponential random graph modeling techniques. This research proposal will result in 1) novel methods to incorporate communication content into network models of social influence to enhance our understanding of the theoretical roots of behavior change in digital communication platforms; 2) scalable techniques to model temporal changes and network dependencies of user interactions in terms of communication content and structure; and 3) new proposals for the development of digital interventions and technology features that harness social ties to support individuals engaging in health behavior change and chronic disease management.

描述（由申请人提供）：社会关系对健康行为改变和慢性病管理的影响已有很好的记录。来自不同学科的研究人员试图了解和利用这些社会关系在健康促进中的作用。网络社区是一种点对点的交流方式，它为研究人员提供了一个独特的机会来了解人类行为改变的机制。数字通信平台的出现导致调查人员质疑在面对面交流的背景下制定的现有行为改变理论的使用。在线社区中的社会行为结构的表现往往嵌入在用户交换的消息的内容中。大多数关于在线和离线社交网络的研究在试图辨别网络影响力的理论基础时，都没有考虑对等互动的通信内容。在拟议的研究中，我们将开发一个框架，将消息内容纳入基于网络的社会影响力模型。虽然普遍适用，但这些方法将基于在线社区中试图戒烟的个人之间的通信来开发，QuitNet是第一个戒烟的在线社交网络。这些方法将被扩展到分析治疗日记（TreatmentDiaries）上管理慢性病的成员之间的沟通，治疗日记是一个在线社区，患者在那里分享他们的疾病管理日常经验。将定性方法与自动文本分析相结合，我们的工作将促进在线社交网络中用户通信的高通量实证分析，从而识别紧急用户需求和社区文化。将开发时间模型，捕获与健康行为变化和慢性病管理相关的认知因素的演变。网络的影响和依赖性的特定内容的通信特性将通过适应联系暴露模型和指数随机图建模技术进行研究。这项研究建议将导致1）新的方法，将通信内容纳入社会影响力的网络模型，以提高我们对数字通信平台中行为变化的理论根源的理解; 2）可扩展的技术，以模拟通信内容和结构方面的用户交互的时间变化和网络依赖性;以及3）开发数字干预措施和技术功能的新建议，这些措施和技术功能利用社会关系来支持个人参与健康行为改变和慢性病管理。