White Katie M, Matcham Faith, Leightley Daniel, Carr Ewan, Conde Pauline, Dawe-Lane Erin, Ranjan Yatharth, Simblett Sara, Henderson Claire, Hotopf Matthew
Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.
Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.
JMIR Res Protoc. 2021 Dec 21;10(12):e32653. doi: 10.2196/32653.
Multi-parametric remote measurement technologies (RMTs) comprise smartphone apps and wearable devices for both active and passive symptom tracking. They hold potential for understanding current depression status and predicting future depression status. However, the promise of using RMTs for relapse prediction is heavily dependent on user engagement, which is defined as both a behavioral and experiential construct. A better understanding of how to promote engagement in RMT research through various in-app components will aid in providing scalable solutions for future remote research, higher quality results, and applications for implementation in clinical practice.
The aim of this study is to provide the rationale and protocol for a 2-armed randomized controlled trial to investigate the effect of insightful notifications, progress visualization, and researcher contact details on behavioral and experiential engagement with a multi-parametric mobile health data collection platform, Remote Assessment of Disease and Relapse (RADAR)-base.
We aim to recruit 140 participants upon completion of their participation in the RADAR Major Depressive Disorder study in the London site. Data will be collected using 3 weekly tasks through an active smartphone app, a passive (background) data collection app, and a Fitbit device. Participants will be randomly allocated at a 1:1 ratio to receive either an adapted version of the active app that incorporates insightful notifications, progress visualization, and access to researcher contact details or the active app as usual. Statistical tests will be used to assess the hypotheses that participants using the adapted app will complete a higher percentage of weekly tasks (behavioral engagement: primary outcome) and score higher on self-awareness measures (experiential engagement).
Recruitment commenced in April 2021. Data collection was completed in September 2021. The results of this study will be communicated via publication in 2022.
This study aims to understand how best to promote engagement with RMTs in depression research. The findings will help determine the most effective techniques for implementation in both future rounds of the RADAR Major Depressive Disorder study and, in the long term, clinical practice.
ClinicalTrials.gov NCT04972474; http://clinicaltrials.gov/ct2/show/NCT04972474.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/32653.
多参数远程测量技术（RMTs）包括用于主动和被动症状跟踪的智能手机应用程序和可穿戴设备。它们在了解当前抑郁状态和预测未来抑郁状态方面具有潜力。然而，使用RMTs进行复发预测的前景在很大程度上取决于用户参与度，用户参与度被定义为一种行为和体验结构。更好地理解如何通过各种应用内组件促进RMT研究中的用户参与度，将有助于为未来的远程研究提供可扩展的解决方案、更高质量的结果以及在临床实践中的应用。
本研究的目的是为一项双臂随机对照试验提供理论依据和方案，以研究有洞察力的通知、进展可视化和研究人员联系方式对使用多参数移动健康数据收集平台疾病与复发远程评估（RADAR）基础版时的行为和体验参与度的影响。
我们的目标是在伦敦站点完成RADAR重度抑郁症研究的参与者招募140名。将通过一个主动智能手机应用程序、一个被动（后台）数据收集应用程序和一个Fitbit设备，使用3个每周任务来收集数据。参与者将以1:1的比例随机分配，以接收包含有洞察力的通知、进展可视化和研究人员联系方式的主动应用程序的改编版本，或照常接收主动应用程序。将使用统计测试来评估以下假设：使用改编应用程序的参与者将完成更高比例的每周任务（行为参与度：主要结果），并且在自我意识测量方面得分更高（体验参与度）。
招募工作于2021年4月开始。数据收集于2021年9月完成。本研究的结果将于2022年通过发表进行传达。
本研究旨在了解如何最好地促进抑郁症研究中对RMTs的参与度。研究结果将有助于确定在未来几轮RADAR重度抑郁症研究以及长期临床实践中实施的最有效技术。
ClinicalTrials.gov NCT04972474；http://clinicaltrials.gov/ct2/show/NCT04972474。
国际注册报告识别码（IRRID）：DERR1-10.2196/32653。
