Association between Recruitment Methods and Attrition in Internet-Based Studies.


Bajardi, P; Paolotti, D; Vespignani, A; Eames, K; Funk, S; Edmunds, WJ; Turbelin, C; Debin, M; Colizza, V; Smallenburg, R; Koppeschaar, C; Franco, AO; Faustino, V; Carnahan, A; Rehn, M; Merletti, F; Douwes, J; Firestone, R; Richiardi, L; (2014) Association between Recruitment Methods and Attrition in Internet-Based Studies. PLoS One, 9 (12). e114925. ISSN 1932-6203 DOI: 10.1371/journal.pone.0114925

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Abstract

Internet-based systems for epidemiological studies have advantages over traditional approaches as they can potentially recruit and monitor a wider range of individuals in a relatively inexpensive fashion. We studied the association between communication strategies used for recruitment (offline, online, face-to-face) and follow-up participation in nine Internet-based cohorts: the Influenzanet network of platforms for influenza surveillance which includes seven cohorts in seven different European countries, the Italian birth cohort Ninfea and the New Zealand birth cohort ELF. Follow-up participation varied from 43% to 89% depending on the cohort. Although there were heterogeneities among studies, participants who became aware of the study through an online communication campaign compared with those through traditional offline media seemed to have a lower follow-up participation in 8 out of 9 cohorts. There were no clear differences in participation between participants enrolled face-to-face and those enrolled through other offline strategies. An Internet-based campaign for Internet-based epidemiological studies seems to be less effective than an offline one in enrolling volunteers who keep participating in follow-up questionnaires. This suggests that even for Internet-based epidemiological studies an offline enrollment campaign would be helpful in order to achieve a higher participation proportion and limit the cohort attrition.

Item Type: Article
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Infectious Disease Epidemiology
Research Centre: Centre for the Mathematical Modelling of Infectious Diseases
PubMed ID: 25490045
Web of Science ID: 347515300095
URI: http://researchonline.lshtm.ac.uk/id/eprint/2030887

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