Lessons learned from recruiting into a longitudinal remote measurement study in major depressive disorder

Oetzmann, Carolin, White, Katie M, Ivan, Alina, Julie, Jessica, Leightley, Daniel, Lavelle, Grace, Lamers, Femke, Siddi, Sara, Annas, Peter, Garcia, Sara Arranz, Haro, Josep Maria, Mohr, David C, Penninx, Brenda WJH, Simblett, Sara, Wykes, Til, Narayan, Vaibhav A, Hotopf, Matthew and Matcham, Faith (2022) Lessons learned from recruiting into a longitudinal remote measurement study in major depressive disorder. npj Digital Medicine, 5. a133 1-8. ISSN 2398-6352

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The use of remote measurement technologies (RMTs) across mobile health (mHealth) studies is becoming popular, given their potential for providing rich data on symptom change and indicators of future state in recurrent conditions such as major depressive disorder (MDD). Understanding recruitment into RMT research is fundamental for improving historically small sample sizes, reducing loss of statistical power, and ultimately producing results worthy of clinical implementation. There is a need for the standardisation of best practices for successful recruitment into RMT research. The current paper reviews lessons learned from recruitment into the Remote Assessment of Disease and Relapse- Major Depressive Disorder (RADAR-MDD) study, a large-scale, multi-site prospective cohort study using RMT to explore the clinical course of people with depression across the UK, Netherlands, and Spain. More specifically, the paper reflects on key experiences from the UK site and consolidates these into four key recruitment strategies, alongside a review of barriers to recruitment. Finally, the strategies and barriers outlined are combined into a model of lessons learned. This work provides a foundation for future RMT study design, recruitment and evaluation.

Item Type: Article
Keywords: Participant recruitment, Recruitment model, Digital health, eHealth, mHealth, Depression
Schools and Departments: School of Psychology > Psychology
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 06 Sep 2022 13:49
Last Modified: 29 Sep 2022 10:45
URI: http://sro.sussex.ac.uk/id/eprint/107800

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