Manas Shruthi, Young Lindsay E, Fujimoto Kayo, Franklin Amy, Myneni Sahiti
Department of Biomedical Informatics, University of Texas, Houston, Texas, USA.
Department of Medicine, University of Chicago, Illinois, Chicago, USA.
Stud Health Technol Inform. 2019 Aug 21;264:1268-1272. doi: 10.3233/SHTI190430.
Unhealthy behaviors, such as tobacco use, increase individual health risk while also creating a global economic burden on the healthcare system. Social ties have been seen as an important, yet complex factor, to sustain abstinence from these modifiable risk behaviors. However, the underlying social mechanisms are still opaque and poorly understood. Digital health communities provide opportunities to understand social dependencies of behavior change because peer interactions in these platforms are digitized. In this paper, we present a novel approach that integrates theories of behavior change and Exponential Random Graph Models (ERGMs) to understand structural dependencies between users of an online community and the behavior change techniques that are manifested in their communication using an affiliation network. Results indicate population specific traits in terms of individuals' engagement in peer communication embed behavior change techniques in online social settings. Implications for personalized health promotion technologies are discussed.
不健康行为，如吸烟，会增加个人健康风险，同时也给医疗保健系统带来全球经济负担。社会关系被视为维持戒除这些可改变风险行为的一个重要但复杂的因素。然而，潜在的社会机制仍然不透明且了解甚少。数字健康社区提供了了解行为改变的社会依赖性的机会，因为这些平台上的同伴互动是数字化的。在本文中，我们提出了一种新颖的方法，该方法整合了行为改变理论和指数随机图模型（ERGMs），以利用隶属网络理解在线社区用户之间的结构依赖性以及在他们的交流中表现出的行为改变技巧。结果表明，就个人参与同伴交流而言，特定人群的特征将行为改变技巧融入了在线社交环境。文中还讨论了对个性化健康促进技术的启示。
