The COVID-19 pandemic affected health research practices. The large-scale impacts of COVID-19 and restrictions on face-to-face interaction led to increased use of remote online data collection methods. However, it is unclear if such efforts led to the representation of populations at risk for health disparities. We evaluated whether multiple recruitment strategies would capture a representative sample of individuals at risk for health disparities.


The COVIDsmart registry collected clinical, social, economic, and behavioral data in the state of Virginia from March to November, 2021. Seven hundred eighty-two adult participants were enrolled. We compared the representation of COVIDsmart participants at risk for health disparities against state data with two-tailed Z tests. Monte-Carlo estimates evaluated the association between recruitment strategies utilized and health disparity risk status.


The majority of participants were non-Hispanic White (81.5%), female (78.6%), non-rural (98%), had a Masters’ degree or higher (62.6%), and an income of $100,000 or higher (51%). The recruitment strategy that brought participants into the study did not differ significantly based on racial/ethnic minority status (p>.05), but did differ for low SES versus high SES groups, p=.03. Low SES and ethnic/racial minority representation proportions were significantly lower for COVIDsmart participants than state data, (p>.05).


Participants at risk for health disparities were not well represented in this registry despite multiple recruitment strategies. The use of targeted emails, social media, and community collaboration may improve the participation of populations at risk for health disparities in remote online research studies.



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