Methods: We recruited, consented, and randomized 138 AYA (ages 17-25 years) with T1D to two versions of an alcohol reduction intervention. All study components were implemented virtually and participants recruited using a variety of social media platforms (i.e., Facebook, Twitter, Instagram) and other internet-based channels (i.e., e-mail newsletter, website banners). To compare the yield of the different platforms, each recruitment post directed respondents to unique landing pages with engagement tracked via Google Analytics. We quantified response and participant characteristics by channel, recruitment method acceptability, and completion at each of two study time points. We applied a series of decision rules to identify invalid (duplicative/false) cases and compared them to valid cases.
Results: Across eight platforms, Facebook was the highest yield recruitment source (42% of participants); demographics differed by platform. Invalid cases were prevalent, with 89 cases identified as invalid; invalid cases were more likely to be recruited from Twitter or Instagram and differed from valid cases across most demographics. Valid cases (N=138) were recruited from 85 colleges in 30 states, DC, and Canada over only 4 days; cases closely resembled characteristics obtained from Google Analytics and from prior data on platform user-base. Retention was high, with complete follow-up for 88.4%. Participants reported high acceptability for future recruitment via social media.
Conclusions: We demonstrate recruitment of traditionally hard-to-reach groups (college-students with T1D) into a longitudinal intervention trial via social media is feasible, efficient, acceptable to participants, and yields a sample that is largely representative of the user-base from which they were drawn. Given differences in participant characteristics across recruitment platforms, researchers may wish to consider diversifying recruitment efforts across platforms to obtain a more diverse sample. Tracking of engagement with recruitment posts and subsequent drop-off at each stage of the study is feasible with off-the-shelf tools and pre-existing platforms.