Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): Recruitment, retention, and data availability in a longitudinal remote measurement study.

Abstract

Background Major Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response to treatment and identify early indicators of relapse. Remote Measurement Technologies (RMT) provide an exciting opportunity to transform the measurement and management of MDD, via data collected from inbuilt smartphone sensors and wearable devices alongside app-based questionnaires and tasks. Our aim is to describe the amount of data collected during a multimodal longitudinal RMT study, in an MDD population. Methods The Remote Assessment of Disease and Relapse – Central Nervous System (RADAR-CNS) program explores the potential to use RMT across a range of central nervous system disorders. Remote Assessment of Disease and Relapse – Major Depressive Disorder (RADAR-MDD) is a multi-centre, prospective observational cohort study conducted as part of the RADAR-CNS program. People with a history of MDD were provided with a wrist-worn wearable, and several apps designed to: a) collect data from smartphone sensors; and b) deliver questionnaires, speech tasks and cognitive assessments. Participants were followed-up for a maximum of 2 years. Results A total of 623 individuals with a history of MDD were enrolled in the study. We report 80% completion rates for primary outcome assessments across all follow-up timepoints. 79.8% of people participated for the maximum amount of time available and 20.2% withdrew prematurely. Data availability across all RMT data types varied depending on the source of data and the participant-burden for each data type. We found no evidence of an association between the severity of depression symptoms at baseline and the availability of data. In total, 110 participants had > 50% data available across all data types, and thus able to contribute to multiparametric analyses. Conclusions RADAR-MDD is the largest multimodal RMT study in the field of mental health. Here, we have shown that collecting RMT data from a clinical population is feasible. We found comparable levels of data availability in active and passive forms of data collection, demonstrating that both are feasible in this patient group. Our next steps are to illustrate the predictive value of these data, which will be the focus of our future data analysis aims.

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 literacy has also been noted as a key facilitator of mHealth app engagement [63]. Nonetheless, there is good reason to believe that the new in-app components can encourage engagement over and above the moderate data availability reported in RADAR-MDD [21]. Third, although concerted efforts were made to include health-, user-, and technology-related barriers to engagement in the app development process, we acknowledge that this is not all-encompassing.…”

“…A wider review of RMT for health management found large variations in aRMT and pRMT use times [20]. Preliminary data from RADAR-MDD indicate that participants completed a median of 21 (IQR 9-31) out of a possible 52 aRMT questionnaires, and 52.3% (326/623) provided wearable data for over 75% of the participating days [21]. Iterative work on the RADAR-base platform has also addressed the challenges of deciphering between low user engagement and technical issues with the technology [22].…”

“…Combined adherence to active and passive (Fitbit) components of the system will be measured as follows: adherence rate for the active app measured as the proportion of participants with over 50% of completed data across all 3 weekly questionnaire tasks and adherence rate for the passive Fitbit measured as the proportion of participants with over 50% of study days with any recorded data. These measures were chosen to align with data availability reporting in RADAR-MDD [21], previous literature [25], and the minimum amount of data sufficient for performing predictive analyses.…”

“…A total of 132 participants are required to detect a difference of 25% completion of PHQ-8 tasks between the control and active arms, with 80% power and 95% CI at the 12-week end point. Allowing for 10% attrition (based on previous research [21] but accounting for a much shorter follow-up period in this study), we will aim to recruit 140 participants. A total of 345 participants will be available to be contacted from the RADAR-MDD study cohort; assuming 50% acceptance of invitation (given the recruitment from a previously motivated cohort), a target of 140 participants should be feasible.…”

“…A key strength of this study is its grounding in a previous research project, using a system that has already been well-documented, designed, and developed for the purpose of RMT data collection [21,61,62]. It also takes an additional theory-driven and user-centered approach to adapting components of the system to promote optimal user engagement.…”