Exploring Engagement With Remote Symptom Tracking for Depression (RADAR: Engage)

Study Overview
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Brief Summary
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The aim of this study is to understand how best to promote engagement with remote measurement technology (RMT) research in major depressive disorder, using the RADAR-MDD infrastructure as a case study. An adapted questionnaire app with insightful notifications and progress visualization will be compared against the app as usual, in terms of behavioural and experiential engagement.

Detailed Description
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Remote measurement technologies (RMTs) provide an opportunity for real-time, longitudinal health tracking through a combination of smartphone apps for symptom reporting (active RMT; aRMT) and mobile/wearable sensors for passive data collection (passive RMT; pRMT). The use of RMTs to track relapse and remission of symptoms in major depressive disorder (MDD) is thought to be more reflective of patient daily experience, in comparison to retrospective recall during clinic visits. The Remote Assessment of Disease and Relapse- Major Depressive Disorder (RADAR-MDD) study uses RMTs to identify predictors of MDD relapse. It collects multiparametric RMT data through the RADAR-base system over a two year follow-up period; aRMT data is collected via mood-tracking questionnaires in the active app, and pRMT data is collected via a fitness watch, the Fitbit Charge device. The promise of RADAR-MDD depends heavily on user engagement with the app. Currently, engagement with aRMT symptom tracking in the field is hugely heterogeneous and preliminary estimates of the RADAR-MDD study suggest around 50% completion of fortnightly questionnaires. There are several, in-app methods available to promote engagement with mHealth tools. Notifications with theoretically informed content can provide a trigger to perform a behaviour, and data visualisation of progress can prompt continued data input. It is unclear which combination of in-app features can promote engagement with the RADAR-base system, while minimising participant burden. This study therefore aims to understand how best to promote engagement with RMT research, using the RADAR-MDD project as a case study. This protocol will outline a mixed-methods approach to exploring the impact of additional, in-app components on engagement with symptom tracking via the RADAR-Base infrastructure. First, a two-armed randomized controlled trial will compare the RADAR-MDD questionnaire app as usual with an adapted app with insightful notifications and progress visualization, aimed at promoting behavioural and experiential engagement. Engagement will be measured as a) provision of symptom tracking scores over the 12-week study period, and b) the degree to which participants feel experientially engaged with symptom tracking via the system. Second, qualitative interviews will reveal participant experiences of the techniques used. The study has three main objectives: To examine the impact of an adapted smartphone app on behavioural engagement with RMT symptom tracking, in comparison with the RADAR-MDD app as usual; To examine the impact of an adapted smartphone app on experiential engagement with RMT symptom tracking, in comparison with the RADAR-MDD app as usual; Qualitatively explore the views of participants on the use of an adapted smartphone app to increase engagement with the RADAR-Base system. Findings in this field would go some way to providing scalable solutions for engagement in RMT studies, higher quality results and applications for implementation into clinical practice.

Official Title
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A Two-Armed Trial Exploring the Effects of In-App Components on User Engagement With a Symptom-Tracking System for Depression (RADAR: Engage)

Conditions
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Major Depressive Disorder

Intervention / Treatment
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* Other: Smartphone app in-app components


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Participation in the RADAR-MDD London site study Consent for future research contact given during participation in the RADAR-MDD study Willing and able to continue using an Android smartphone Willing and able to continue using a Fitbit device Capacity to give informed consent Exclusion Criteria: Development of a comorbid psychiatric disorder since participation in the RADAR-MDD study

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Supportive Care
Allocation: Randomized
Intervention Model: Parallel Assignment
Interventional Model Description: Participants will be randomised to one of two arms, the app as usual or the adapted app.
Masking: Single


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| No Intervention: RADAR-MDD Questionnaire App as Usual<br>The RADAR-MDD questionnaire app as usual asks participants to complete 3x active tasks per week, with one reminder notification at 9am on a day that a questionnaire is due. The notification reads 'Questionnaire Time. Won't usually take longer than 3 minutes'. The participant is not able to view any data progress, aside from through the Fitbit app, which was present in the original RADAR-MDD study. | |
| Experimental: RADAR-MDD Adapted Questionnaire App<br>The following components are also present: Notifications: The notification will alternate between the phrases 'Questionnaire Time. Symptom tracking might increase self-awareness of your emotions (Bakker & Rickard, 2018)', 'Questionnaire Time. Symptom tracking is a technique often used in treatment to increase insight into your symptoms (Kramer et al., 2014)', and 'Questionnaire Time. Tracking your symptoms through a smartphone and Fitbit might help research to better understand health conditions'. Progress visualisation: Participants will be able to view their questionnaire completion progress as a visualisation through the app, in the form of a graph. Additional components: An additional text on the home screen of the active app will read 'You can contact your research team between 9am-5pm Mon-Fri if you have questions, onradar-engage@kcl.ac.uk'. | Other: Smartphone app in-app components<br>* Insightful notification text, data visualisation, research team contact details.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Behavioural engagement | Data completion of active symptom tracking (PHQ-8 scale). Value of 0-12. | Total completion at the 12-week end point | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Experiential engagement (1) | User Engagement Scale for mHealth Technology. 30 items, 5-point likert scale. Minimum value 0, maximum value 150. Higher value relates to increased experiential engagement. | Baseline and 12-week point | 
| Experiential engagement (2) | Emotional Self-Awareness Questionnaire. 33 items, 5-point likert scale. Minimum value 0, maximum value 165. Higher value relates to increased emotional awareness. | Baseline and 12-week point | 
| System Usability | mHealth App Usability Questionnaire. 18 items, 7-point likert scale. Minimum value 0, maximum value 126. Higher value relates to increased app usability rating. | Baseline and 12-week point | 
| Passive monitoring adherence | Fitbit device weartime | Continuously across a 12-week time period |