Count Your Calories and Share Them: Health Benefits of Sharing mHealth Information on Social Networking Sites.

This study investigates the relationship between sharing tracked mobile health (mHealth) information online, supportive communication, feedback, and health behavior. Based on the Integrated Theory of mHealth, our model asserts that sharing tracked health information on social networking sites benefits users' perceptions of their health because of the supportive communication they gain from members of their online social networks and that the amount of feedback people receive moderates these associations. Users of mHealth apps (N = 511) completed an online survey, and results revealed that both sharing tracked health information and receiving feedback from an online social network were positively associated with supportive communication. Network support both corresponded with improved health behavior and mediated the association between sharing health information and users' health behavior. As users received greater amounts of feedback from their online social networks, however, the association between sharing tracked health information and health behavior decreased. Theoretical implications for sharing tracked health information and practical implications for using mHealth apps are discussed.

Modeling the impact of twitter on influenza epidemics.

Influenza remains a serious public-health problem worldwide. The rising popularity and scale of social networking sites such as Twitter may play an important role in detecting, affecting, and predicting influenza epidemics. In this paper, we develop a simple mathematical model including the dynamics of ``tweets'' --- short, 140-character Twitter messages that may enhance the awareness of disease, change individual's behavior, and reduce the transmission of disease among a population during an influenza season. We analyze the model by deriving the basic reproductive number and proving the stability of the steady states. A Hopf bifurcation occurs when a threshold curve is crossed, which suggests the possibility of multiple outbreaks of influenza. We also perform numerical simulations, conduct sensitivity test on a few parameters related to tweets, and compare modeling predictions with surveillance data of influenza-like illness reported cases and the percentage of tweets self-reporting flu during the 2009 H1N1 flu outbreak in England and Wales. These results show that social media programs like Twitter may serve as a good indicator of seasonal influenza epidemics and influence the emergence and spread of the disease.