Remote assessment of disease and relapse in major depressive disorder (RADAR-MDD): a multi-centre prospective cohort study protocol

Faith Matcham ,Nikolay V Manyakov ,Josep María Haro ,Wolfgang Viechtbauer ,José Ferrão ,Hans Eriksson ,Yatharth Ranjan ,Katie M White ,Matthew Hotopf ,Nicholas Cummins ,Zulqarnain Rashid ,Vaibhav A Narayan ,Janneke Boere ,Til Wykes ,Sonia Difrancesco ,Ashley Polhemus ,Penninx Bwjh ,Amos Folarin ,Sara Simblett ,Giovanni De Girolamo ,Q Li ,Francesco Nobilia ,Alina Ivan ,Sjaak Peelen ,Aki Rintala ,Femke Lamers ,Maximilian Kerz ,Chiara Barattieri Di San Pietro ,Michiel Ringkjøbing-Elema ,Richard Dobson ,Sara Siddi ,Inez Myin-Germeys ,Viola Bulgari ,David C Mohr ,Nick Meyer

doi:10.1186/s12888-019-2049-z

Abstract

BackgroundThere is a growing body of literature highlighting the role that wearable and mobile remote measurement technology (RMT) can play in measuring symptoms of major depressive disorder (MDD). Outcomes assessment typically relies on self-report, which can be biased by dysfunctional perceptions and current symptom severity. Predictors of depressive relapse include disrupted sleep, reduced sociability, physical activity, changes in mood, prosody and cognitive function, which are all amenable to measurement via RMT. This study aims to: 1) determine the usability, feasibility and acceptability of RMT; 2) improve and refine clinical outcome measurement using RMT to identify current clinical state; 3) determine whether RMT can provide information predictive of depressive relapse and other critical outcomes.MethodsRADAR-MDD is a multi-site prospective cohort study, aiming to recruit 600 participants with a history of depressive disorder across three sites: London, Amsterdam and Barcelona. Participants will be asked to wear a wrist-worn activity tracker and download several apps onto their smartphones. These apps will be used to either collect data passively from existing smartphone sensors, or to deliver questionnaires, cognitive tasks, and speech assessments. The wearable device, smartphone sensors and questionnaires will collect data for up to 2-years about participants’ sleep, physical activity, stress, mood, sociability, speech patterns, and cognitive function. The primary outcome of interest is MDD relapse, defined via the Inventory of Depressive Symptomatology- Self-Report questionnaire (IDS-SR) and the World Health Organisation’s self-reported Composite International Diagnostic Interview (CIDI-SF).DiscussionThis study aims to provide insight into the early predictors of major depressive relapse, measured unobtrusively via RMT. If found to be acceptable to patients and other key stakeholders and able to provide clinically useful information predictive of future deterioration, RMT has potential to change the way in which depression and other long-term conditions are measured and managed.

Highlights

There is a growing body of literature highlighting the role that wearable and mobile remote measurement technology (RMT) can play in measuring symptoms of major depressive disorder (MDD)
There is substantial evidence to suggest that changes in physical activity, sleep, stress, prosodic features, cognitive function, mood and sociability are associated with an increased risk of relapse [9,10,11,12,13]
If RADAR-Major Depressive Disorder (MDD) demonstrates that remote measurement technologies (RMT) is feasible to use in large populations, acceptable for participants, and provides clinically useful data, it may represent that start of a paradigm shift in how clinical data are collected and respective change in how health services are delivered [54]

Summary

Introduction

There is a growing body of literature highlighting the role that wearable and mobile remote measurement technology (RMT) can play in measuring symptoms of major depressive disorder (MDD). Predictors of depressive relapse include disrupted sleep, reduced sociability, physical activity, changes in mood, prosody and cognitive function, which are all amenable to measurement via RMT. The development of remote measurement technologies (RMT), using these inbuilt sensors to unobtrusively measure human behaviour and physiology, combined with active measurement of daily experiences via smartphone apps, is an innovation which could be used to provide real-time information about patients’ current clinical state, as well as information potentially predictive of future deterioration [1]. Symptom recall in patients with depression may not be accurate, potentially biased by current symptom severity and environmental stressors [16, 17] This retrospective recall cannot capture symptom variability and context reactivity which would allow interventions to be administered at a crucial point in the relapse signature

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