JMIR Publications

ABSTRACT

Background:

Diabetes remains a major health problem in the US affecting an estimated 10.5% of the population. Diabetes self-management interventions improve diabetes knowledge, self-management behaviors, and metabolic control. Widespread Internet connectivity facilitates the use of eHealth interventions, which positively impacts knowledge, social support, clinical, and behavioral outcomes. Particularly, diabetes interventions based in virtual environments have the potential to improve diabetes self-efficacy and support while being highly feasible and usable. However, little is known about the pattern of social interactions and support taking place within type 2 diabetes-specific virtual communities.

Objective:

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 (VE).

Methods:

Data comprised VE-meditated 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.

Results:

Findings reveal 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 participants that exchanged emotional support are likely to exchange appraisal or instrumental support, and participants that exchanged appraisal support are likely to exchange instrumental support.

Conclusions:

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.