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

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; http://clinicaltrials.gov/ct2/show/NCT04972474 International Registered Report Identifier (IRRID) DERR1-10.2196/32653

Engagement With a Remote Symptom-Tracking Platform Among Participants With Major Depressive Disorder: Randomized Controlled Trial

“…This study was approved by the Psychiatry, Nursing, and Midwifery Research Ethics Subcommittee at King's College London (reference number: RESCM-20/21-21083) and registered as a clinical trial (reference number: NCT04972474). A trial protocol has been previously published [25].…”

“…Findings from research with users of the RADAR-base system allowed for the translation of these functions into tangible components tailored specifically to the needs and preferences of the target cohort [32]. It was decided that an engaging app should include notifications with information on symptom tracking from a credible source, behavioral feedback via progress visualization, and instant access to researcher contact details (see the study by White et al [25] and Multimedia Appendix 2 for a detailed overview of this process).…”

RADAR-IoT: An open-source, interoperable and extensible IoT gateway framework for healthcare and beyond

“…As shown in Figure 2, it supports both passive and active data collection through two applications, pRMT and aRMT, which monitor movement, location, audio, calls, texts, and app usage, and include questionnaires to gather patient information. RADARbase has been and is currently being used in various research studies [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], focusing on personal sensing. However, the platform's integration with wearable devices is limited by vendor availability of SDKs and REST APIs.…”