s41746-022-00680-z.pdf (911.25 kB)
Lessons learned from recruiting into a longitudinal remote measurement study in major depressive disorder
journal contribution
posted on 2023-06-10, 04:40 authored by Carolin Oetzmann, Katie M White, Alina Ivan, Jessica Julie, Daniel Leightley, Grace Lavelle, Femke Lamers, Sara Siddi, Peter Annas, Sara Arranz Garcia, Josep Maria Haro, David C Mohr, Brenda WJH Penninx, Sara Simblett, Til Wykes, Vaibhav A Narayan, Matthew Hotopf, Faith MatchamThe 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.
History
Publication status
- Published
File Version
- Published version
Journal
npj Digital MedicineISSN
2398-6352Publisher
Nature ResearchExternal DOI
Volume
5Page range
a133 1-8Department affiliated with
- Psychology Publications
Full text available
- Yes
Peer reviewed?
- Yes