Exchanges in a Virtual Environment for Diabetes Self-Management Education and Support: Social Network Analysis.
Exchanges in a Virtual Environment for Diabetes Self-Management Education and Support: Social Network Analysis.
复制标题
糖尿病自我管理教育和支持的虚拟环境中的交流：社会网络分析。
作者：
Pérez-Aldana CA;Lewinski AA;Johnson CM;Vorderstrasse AA;Myneni S
糖尿病仍然是美国的一个主要健康问题，影响着大约 10.5% 的人口。糖尿病自我管理干预措施可以提高糖尿病知识、自我管理行为和临床结果。广泛的互联网连接促进了电子卫生干预措施的使用，这对知识、社会支持以及临床和行为结果产生积极影响。特别是，基于虚拟环境的糖尿病干预措施有可能提高糖尿病患者的自我效能和支持，同时具有高度的可行性和可用性。然而，人们对 2 型糖尿病特定虚拟社区中的社交互动和支持模式知之甚少。本研究的目的是检查通过虚拟环境提供的 2 型糖尿病自我管理教育和支持干预的社会支持交换。数据包括参与者之间以及参与者与提供者之间的虚拟环境介导的同步互动，这些互动来自对 2 型糖尿病自我管理教育和支持的干预。从此类社交互动中获得的网络数据被用来创建网络，以通过社交网络分析的视角来分析社会支持交换模式。此外，网络相关性被用来探索社会支持网络之间的关联。研究结果揭示了支持网络之间的结构差异，以及干预促进的支持性互动的关键网络特征。情感和评价支持网络是规模最大、最集中、最活跃的网络，这表明虚拟社区可以成为此类支持的良好来源。此外，评估和工具支持网络的联系更加紧密，这表明虚拟社区的成员更有可能参与更大的群体互动，在其中可以交换这些类型的支持。最后，网络相关性表明，交换情感支持的参与者可能会交换评估或工具支持，而交换评估支持的参与者可能会交换工具支持。可以使用社交网络分析方法来研究特定疾病虚拟环境中的社交互动模式，以更好地了解社会支持的交换。鉴于虚拟环境提供了独特的机会，可以促进有效且可持续的现实环境，可以利用社交互动来实现不同的健康目标，网络数据可以为新颖且有效的电子卫生干预措施的设计提供有价值的见解。
Diabetes remains a major health problem in the United States, affecting an estimated 10.5% of the population. Diabetes self-management interventions improve diabetes knowledge, self-management behaviors, and clinical outcomes. Widespread internet connectivity facilitates the use of eHealth interventions, which positively impacts knowledge, social support, and clinical and behavioral outcomes. In particular, diabetes interventions based on virtual environments have the potential to improve diabetes self-efficacy and support, while being highly feasible and usable. However, little is known about the patterns of social interactions and support taking place within type 2 diabetes–specific virtual communities. The objective of this study was to examine social support exchanges from a type 2 diabetes self-management education and support intervention that was delivered via a virtual environment. Data comprised virtual environment–mediated synchronous interactions among participants and between participants and providers from an intervention for type 2 diabetes self-management education and support. Network data derived from such social interactions were used to create networks to analyze patterns of social support exchange with the lens of social network analysis. Additionally, network correlations were used to explore associations between social support networks. The findings revealed structural differences between support networks, as well as key network characteristics of supportive interactions facilitated by the intervention. Emotional and appraisal support networks are the larger, most centralized, and most active networks, suggesting that virtual communities can be good sources for these types of support. In addition, appraisal and instrumental support networks are more connected, suggesting that members of virtual communities are more likely to engage in larger group interactions where these types of support can be exchanged. Lastly, network correlations suggest that participants who exchange emotional support are likely to exchange appraisal or instrumental support, and participants who exchange appraisal support are likely to exchange instrumental support. Social interaction patterns from disease-specific virtual environments can be studied using a social network analysis approach to better understand the exchange of social support. Network data can provide valuable insights into the design of novel and effective eHealth interventions given the unique opportunity virtual environments have facilitating realistic environments that are effective and sustainable, where social interactions can be leveraged to achieve diverse health goals.