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The growing amount of data in health-care social media requires innovative new analysis methods, which are elementary to exploration of relationship dynamics, in a bid to understand the new roles social media plays in health care. In this work, we use network analysis to explore the temporal nature of two large diabetes social networks, with a view to enhancing our knowledge of the development of community structures and cohesion factors. We compare our findings with analysis of two other nonhealth-care social networks. Current results reveal how diabetes online communities are very dynamic, suggesting diabetes patients are usually actively engaged for periods of less than a year, typically immediately following diagnosis. Additionally, we observe shrinking of both diameter and density, as well as disassortative mixing. The presented empirical study informs future online intervention strategies for promoting health behavior and lifestyle changes among people with diabetes.

Background: People with type 1 diabetes who use electronic self-help tools register a large amount of information about their disease on their participating devices; however, this information is rarely utilized beyond the immediate investigation. We have developed a diabetes diary for mobile phones and a statistics-based feedback module, which we have named Diastat, to give data-driven feedback to the patient based on their own data. Method: In this study, up to 40 participants will be given a smartphone on which is loaded a diabetes self-help application (app), the Few Touch Application (FTA). Participants will be randomized into two groups to be given access to Diastat 4 or 12 weeks, respectively after receiving the smartphone, and will use the FTA with Diastat for 8 weeks after this point. The primary endpoint is the frequency of high and low blood-glucose measurements. Discussion: The study will investigate the effect of data-driven feedback to patients. Our hypothesis is that this will improve glycemic control and reduce variability. The endpoints are robust indicators that can be assembled with minimal effort by the patient beyond normal routine. Trial registration: Clinicaltrials.gov: NCT01774149