Data Sharing in an Online Diabetes Social Network
Author Information
Author(s): Weitzman Elissa R., Adida Ben, Kelemen Skyler, Mandl Kenneth D.
Primary Institution: Children's Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology
Hypothesis
We hypothesized high willingness to participate and share information and a gradient in information sharing among application users such that participants selecting highly public sharing conditions would report better diabetes health metrics than participants selecting more private sharing conditions.
Conclusion
The study shows that online social networks can effectively facilitate data sharing for diabetes research and public health surveillance.
Supporting Evidence
- 83.1% of the most recent A1c values reported were obtained within the past 90 days.
- 81.4% of users permitted data donation to the community display.
- Users selecting the most permissive sharing options had a lower average A1c (6.8%) than those not sharing (7.1%).
- Aggregate A1c reported by US users closely resembled aggregate NHANES estimates.
Takeaway
People with diabetes can share their health data online, which helps researchers understand diabetes better and improve health care.
Methodology
The study used a software application to collect self-reported A1c values from members of an online diabetes social network.
Potential Biases
The data may be skewed towards individuals with better health metrics due to self-selection in sharing.
Limitations
The sample may not represent the general population of people with diabetes due to participation biases.
Participant Demographics
Participants were primarily patients with diabetes, with about 15% being friends or family members of patients.
Statistical Information
P-Value
0.038
Confidence Interval
95% CI 1.2–3.2
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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