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

ABSTRACT: 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

SUBMITTER: Oetzmann C 

PROVIDER: S-EPMC9440458 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature