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PLoS One. 2011 Apr 27;6(4):e19256. doi: 10.1371/journal.pone.0019256.

Sharing data for public health research by members of an international online diabetes social network.

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  • 1Children's Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology, Children's Hospital Boston, Boston, Massachusetts, United States of America.



Surveillance and response to diabetes may be accelerated through engaging online diabetes social networks (SNs) in consented research. We tested the willingness of an online diabetes community to share data for public health research by providing members with a privacy-preserving social networking software application for rapid temporal-geographic surveillance of glycemic control.


SN-mediated collection of cross-sectional, member-reported data from an international online diabetes SN entered into a software application we made available in a "Facebook-like" environment to enable reporting, charting and optional sharing of recent hemoglobin A1c values through a geographic display. Self-enrollment by 17% (n = 1,136) of n = 6,500 active members representing 32 countries and 50 US states. Data were current with 83.1% of most recent A1c values reported obtained within the past 90 days. Sharing was high with 81.4% of users permitting data donation to the community display. 34.1% of users also displayed their A1cs on their SN profile page. Users selecting the most permissive sharing options had a lower average A1c (6.8%) than users not sharing with the community (7.1%, p = .038). 95% of users permitted re-contact. Unadjusted aggregate A1c reported by US users closely resembled aggregate 2007-2008 NHANES estimates (respectively, 6.9% and 6.9%, p = 0.85).


Success within an early adopter community demonstrates that online SNs may comprise efficient platforms for bidirectional communication with and data acquisition from disease populations. Advancing this model for cohort and translational science and for use as a complementary surveillance approach will require understanding of inherent selection and publication (sharing) biases in the data and a technology model that supports autonomy, anonymity and privacy.

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