Exploring the Effects of In-App Components on Engagement With a Symptom-Tracking Platform Among Participants With Major Depressive Disorder (RADAR-Engage): Protocol for a 2-Armed Randomized Controlled Trial

Katie M White, Faith Matcham, Daniel Leightley, Ewan Carr, Pauline Conde, Erin Dawe-Lane, Yatharth Ranjan, Sara Simblett, Claire Henderson, Matthew Hotopf, Katie M White, Faith Matcham, Daniel Leightley, Ewan Carr, Pauline Conde, Erin Dawe-Lane, Yatharth Ranjan, Sara Simblett, Claire Henderson, Matthew Hotopf

Abstract

Background: 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.

Objective: 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.

Methods: 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).

Results: Recruitment commenced in April 2021. Data collection was completed in September 2021. The results of this study will be communicated via publication in 2022.

Conclusions: 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.

Trial registration: ClinicalTrials.gov NCT04972474; https://ichgcp.net/clinical-trials-registry/NCT04972474.

International registered report identifier (irrid): DERR1-10.2196/32653.

Keywords: app; engagement; major depressive disorder; mobile phone; remote measurement technologies; research.

Conflict of interest statement

Conflicts of Interest: MH is the principal investigator of the RADAR-CNS consortium, a private-public pre-competitive consortium with research funding from Janssen, UCB, MSD, Biogen and Lundbeck. No further conflicts are declared.

©Katie M White, Faith Matcham, Daniel Leightley, Ewan Carr, Pauline Conde, Erin Dawe-Lane, Yatharth Ranjan, Sara Simblett, Claire Henderson, Matthew Hotopf. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 21.12.2021.

Figures

Figure 1
Figure 1
Study design from screening to follow-up end point, including time points 0, 1, and 2. RADAR-MDD: Remote Assessment of Disease and Relapse in Major Depressive Disorder.
Figure 2
Figure 2
Service user involvement in the design of the adapted app. RMT: remote measurement technology.
Figure 3
Figure 3
Screenshots of the in-app components included in the adapted app.

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