Background: Individuals with type 2 diabetes have an increased risk for comorbidities such as heart disease, lower limb amputations, stroke, and renal failure. Multiple factors influence development of complications in a person living with type 2 diabetes; however, an individual's self-management behaviors may delay the onset of, or lessen the severity of, these complications. Social support provides personal, informal advice and knowledge that helps individuals initiate and sustain self-management and adherence. Objective: Our aim was to gain an understanding of type 2 diabetes social interaction in a virtual environment, one type of computer-mediated environment (CME), and the social support characteristics that increase and sustain self-management in adults living with chronic illness. Methods: This study is a secondary analysis of longitudinal data collected in a CME study, Second Life Impacts Diabetes Education & Self-Management (1R21-LM010727-01). This virtual environment replicated a real-life community where 6 months of naturalistic synchronous voice conversations, emails, and text chats were recorded among participants and providers. This analysis uses a mixed-methods approach to explore and compare qualitative and quantitative findings. This analysis is guided by two theories: Strong/Weak Ties Theory and Social Penetration Theory. Qualitative data will be analyzed using content analysis, and we will complete descriptive statistics on the quantified variables (eg, average number of ties). Institutional review board approval was obtained in June 2016. Results: This study is in progress. Conclusions: Interventions provided through virtual environments are a promising solution to increasing self-management practices. However, little is known of the depth, breadth, and quality of social support that is exchanged and how interaction supports self-management and relates to health outcomes. This study will provide knowledge that will help guide clinical practice and policy to enhance social support for chronic illness via the Internet.