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 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. A key question for the field is the extent to which participants can adhere to research protocols and the completeness of data collected. We aimed to describe drop out and data completeness in a naturalistic multimodal longitudinal RMT study, in people with a history of recurrent MDD. We further aimed to determine whether those experiencing a depressive relapse at baseline contributed less complete data. Methods Remote Assessment of Disease and Relapse – Major Depressive Disorder (RADAR-MDD) is a multi-centre, prospective observational cohort study conducted as part of the Remote Assessment of Disease and Relapse – Central Nervous System (RADAR-CNS) program. People with a history of MDD were provided with a wrist-worn wearable device, and smartphone apps designed to: a) collect data from smartphone sensors; and b) deliver questionnaires, speech tasks, and cognitive assessments. Participants were followed-up for a minimum of 11 months and maximum of 24 months. Results Individuals with a history of MDD (n = 623) 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. 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. 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.

Longitudinal Relationships Between Depressive Symptom Severity and Phone-Measured Mobility: Dynamic Structural Equation Modeling Study

“…All participants in the study had at least one diagnosis of depression in the most recent 2 years and were recruited from 3 countries (Netherlands, Spain, and the United Kingdom); additional details descriptions are reported in [ 28 ]. Participants’ passive data (eg, location, steps, and sleep) and active data (eg, questionnaires) were respectively collected via passive remote measurement technologies and active remote measurement technologies apps provided by an open-source platform (RADAR-base) [ 29 ].…”

Clinical Targets and Attitudes Toward Implementing Digital Health Tools for Remote Measurement in Treatment for Depression: Focus Groups With Patients and Clinicians

“…Future longitudinal studies could include active data that capture the emotional and psychological factors presented in this study and could generate passive data features that closely match the behavioral markers reported here. Studies examining the severity of depression and device engagement are already underway [ 37 ]. Applying such methods to patients currently in psychotherapy could provide insights into how real engagement and device use vary within mental health services.…”

Lessons learned from recruiting into a longitudinal remote measurement study in major depressive disorder

“…The use of mobile technology in healthcare (mobile health; mHealth) has the potential to revolutionise both clinical practice and research 1 . Novel remote measurement technologies (RMTs), for example, smartphone applications, sensors and wearable technologies, are a subsection of mHealth, and can enable frequent, longitudinal and personalised health monitoring 2 . Currently, the assessment, and subsequent treatment, of many chronic health conditions is limited to retrospective recall during routine clinic visits, which can be biased by cognitive and memory heuristics 3 , and social desirability bias 4 .…”

“…The economic burden of MDD is currently estimated at $326 billion (2020 values), exacerbated further by an increased risk of comorbidities and healthcare resource utilisation in those with high relapse and recurrence rates 7,8 . Data obtained through a combination of smartphone questionnaires (active RMT; aRMT) and inbuilt sensors (passive RMT; pRMT) from phones and wearables can provide rich, multi-parametric information on symptom change, risk factors, cognition, sleep and diurnal patterns of behaviour, sociability and physical activity 2 . Crucially, the integration of RMTs into MDD research could provide the temporal resolution needed to detect indicators of future depressive episodes 9 .…”

“…Recruitment began in November 2017 at the London, UK site, acting as a year-long pilot for the recruitment and enrolment process before the remaining two sites joined in September 2018 and February 2019 respectively. By end of follow-up in June 2020, RADAR-MDD had exceeded its overall recruitment target (N = 623), and in the London site alone had recruited 175% (N = 350) of its target sample, making it the largest longitudinal observational cohort study of RMTs to date 2,16 . It was also able to sustain a steady level of recruitment throughout the COVID-19 pandemic.…”