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.
app
engagement
major depressive disorder
mobile phone
remote measurement technologies
research
Journal
JMIR research protocols
ISSN: 1929-0748
Titre abrégé: JMIR Res Protoc
Pays: Canada
ID NLM: 101599504
Informations de publication
Date de publication:
21 Dec 2021
21 Dec 2021
Historique:
received:
05
08
2021
accepted:
14
10
2021
revised:
13
10
2021
entrez:
21
12
2021
pubmed:
22
12
2021
medline:
22
12
2021
Statut:
epublish
Résumé
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. DERR1-10.2196/32653.
Sections du résumé
BACKGROUND
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
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
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
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
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
BACKGROUND
ClinicalTrials.gov NCT04972474; http://clinicaltrials.gov/ct2/show/NCT04972474.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)
UNASSIGNED
DERR1-10.2196/32653.
Identifiants
pubmed: 34932005
pii: v10i12e32653
doi: 10.2196/32653
pmc: PMC8734922
doi:
Banques de données
ClinicalTrials.gov
['NCT04972474']
Types de publication
Journal Article
Langues
eng
Pagination
e32653Informations de copyright
©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.
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