Internet communication advances provide new opportunities to assemble individuals with rare diseases to online patient registries from wide geographic areas for research. However, there is little published information on the efficacy of different recruitment methods. Here we describe recruitment patterns and the characteristics of individuals with the self-identified autosomal dominant genetic disorder neurofibromatosis type 1 (NF1) who participated in an online patient registry during the 1-year period from 1/1/2012 to 12/31/2012. We employed four main mechanisms to alert potential participants to the registry: (1) Facebook and Google advertising, (2) government and academic websites, (3) patient advocacy groups, and (4) healthcare providers. Participants reported how they first heard about the registry through an online questionnaire. During the 1-year period, 880 individuals participated in the registry from all 50 U.S. States, the District of Columbia, Puerto Rico, and 39 countries. Facebook and Google were reported as referral sources by the highest number of participants (n=550, 72% Facebook), followed by healthcare providers (n=74), and government and academic websites (n=71). The mean participant age was 29±18 years and most participants reported White race (73%) and female sex (62%) irrespective of reported referral source. Internet advertising, especially through Facebook, resulted in efficient enrollment of large numbers of individuals with NF1. Our study demonstrates the potential utility of this approach to assemble individuals with a rare disease from across the world for research studies.

Original languageEnglish
Pages (from-to)1686-1694
Number of pages9
JournalAmerican Journal of Medical Genetics, Part A
Issue number7
StatePublished - Jul 2014


  • Genetic syndrome
  • Internet
  • NF1
  • Neurofibromatosis type 1
  • Online
  • Rare disease
  • Registry


Dive into the research topics of 'Evaluation of participant recruitment methods to a rare disease online registry'. Together they form a unique fingerprint.

Cite this