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 W J H, Simblett Sara K, Wykes Til, Narayan Vaibhav A, Hotopf Matthew, Matcham Faith
Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
South London and Maudsley NHS Foundation Trust, London, UK.
NPJ Digit Med. 2022 Sep 3;5(1):133. doi: 10.1038/s41746-022-00680-z.
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, the 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.
鉴于远程测量技术（RMTs）在提供关于症状变化以及诸如重度抑郁症（MDD）等复发性疾病未来状态指标的丰富数据方面具有潜力，其在移动健康（mHealth）研究中的应用正变得越来越普遍。了解RMT研究的招募情况对于改善历来较小的样本量、减少统计功效的损失以及最终产生值得临床应用的结果至关重要。成功招募到RMT研究需要标准化最佳实践。本文回顾了从疾病与复发远程评估——重度抑郁症（RADAR-MDD）研究招募工作中吸取的经验教训，该研究是一项大规模、多中心的前瞻性队列研究，使用RMT来探究英国、荷兰和西班牙抑郁症患者的临床病程。更具体地说，本文反思了英国研究点的关键经验，并将其整合为四项关键招募策略，同时回顾了招募障碍。最后，将所概述的策略和障碍整合为一个经验教训模型。这项工作为未来RMT研究的设计、招募和评估奠定了基础。
