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

Published online by Cambridge University Press:  01 September 2022

F. Matcham ,
D. Leightley ,
S. Siddi ,
F. Lamers ,
K. White ,
P. Annas ,
G. De Girolamo ,
S. Difrancesco ,
J.M. Haro  and
M. Horsfall
...Show all authors
F. Matcham*
Affiliation:
Institute of Psychiatry, Psychology & Neuroscience, King’s College London, Department Of Psychological Medicine, London, United Kingdom
D. Leightley
Affiliation:
Institute of Psychiatry, Psychology & Neuroscience,King’s College London, Department Of Psychological Medicine, London, United Kingdom
S. Siddi
Affiliation:
Parc Sanitari Sant Joan de Déu, Fundació Sant Joan De Déu, Cibersam, Barcelona, Spain
F. Lamers
Affiliation:
Department of Psychiatry and Amsterdam Public Health Research Institute, Amsterdam Umc, Amsterdam, Netherlands
K. White
Affiliation:
Institute of Psychiatry, Psychology & Neuroscience,King’s College London, Department Of Psychological Medicine, London, United Kingdom
P. Annas
Affiliation:
H. Lundbeck A/S, N/a, Valby, Denmark
G. De Girolamo
Affiliation:
IRCCS Instituto Centro San Giovanni di Dio Fatebenefratelli, N/a, Brescia, Italy
S. Difrancesco
Affiliation:
Department of Psychiatry and Amsterdam Public Health Research Institute, Amsterdam Umc, Amsterdam, Netherlands
J.M. Haro
Affiliation:
Parc Sanitari Sant Joan de Déu, Fundació Sant Joan De Déu, Cibersam, Barcelona, Spain
M. Horsfall
Affiliation:
Department of Psychiatry and Amsterdam Public Health Research Institute, Amsterdam Umc, Amsterdam, Netherlands
A. Ivan
Affiliation:
Institute of Psychiatry, Psychology & Neuroscience,King’s College London, Department Of Psychological Medicine, London, United Kingdom
G. Lavelle
Affiliation:
Institute of Psychiatry, Psychology & Neuroscience, King’s College London, Department Of Psychological Medicine, London, United Kingdom
Q. Li
Affiliation:
Janssen Research and Development, LLC, N/a, New Jersey, United States of America
F. Lombardini
Affiliation:
Parc Sanitari Sant Joan de Déu, Fundació Sant Joan De Déu, Cibersam, Barcelona, Spain
D. Mohr
Affiliation:
Center for Behavioral Intervention Technologies, Department Of Preventative Medicine, Northwestern University, United States of America
V. Narayan
Affiliation:
Janssen Research and Development, LLC, N/a, New Jersey, United States of America
C. Oetzmann
Affiliation:
Institute of Psychiatry, Psychology & Neuroscience, King’s College London, Department Of Psychological Medicine, London, United Kingdom
B. Penninx
Affiliation:
Department of Psychiatry and Amsterdam Public Health Research Institute, Amsterdam Umc, Amsterdam, Netherlands
S. Simblett
Affiliation:
Institute of Psychiatry, Psychology & Neuroscience,King’s College London, Psychology, London, United Kingdom
S. Bruce
Affiliation:
Institute of Psychiatry, Psychology & Neuroscience,King’s College London, Psychology, London, United Kingdom
R. Nica
Affiliation:
Institute of Psychiatry, Psychology & Neuroscience,King’s College London, Psychology, London, United Kingdom
T. Wykes
Affiliation:
King’s College London, Institute Of Psychiatry, Psychology And Neuroscience, London, United Kingdom
J. Brasen
Affiliation:
H. Lundbeck A/S, N/a, Valby, Denmark
I. Myin-Germeys
Affiliation:
KU Leuven, Neurosciences, Leuven, Belgium
A. Rintala
Affiliation:
KU Leuven, Neurosciences, Leuven, Belgium
P. Conde
Affiliation:
Institute of Psychiatry, Psychology & Neuroscience,King’s College London, Psychology, London, United Kingdom
R. Dobson
Affiliation:
King’s College London, Institute Of Psychiatry, Psychology And Neuroscience, London, United Kingdom
A. Folarin
Affiliation:
Institute of Psychiatry, Psychology & Neuroscience,King’s College London, Psychology, London, United Kingdom
C. Stewart
Affiliation:
Institute of Psychiatry, Psychology & Neuroscience,King’s College London, Department Of Psychological Medicine, London, United Kingdom
Y. Ranjan
Affiliation:
King’s College London, Institute Of Psychiatry, Psychology And Neuroscience, London, United Kingdom
Z. Rashid
Affiliation:
Institute of Psychiatry, Psychology & Neuroscience,King’s College London, Department Of Psychological Medicine, London, United Kingdom
N. Cummins
Affiliation:
Institute of Psychiatry, Psychology & Neuroscience,King’s College London, Psychology, London, United Kingdom
N. Manyakov
Affiliation:
Janssen Pharmaceutica NV, N/a, Beerse, Belgium
S. Vairavan
Affiliation:
Janssen Research and Development, LLC, N/a, New Jersey, United States of America
M. Hotopf
Affiliation:
Institute of Psychiatry, Psychology & Neuroscience, King’s College London, Department Of Psychological Medicine, London, United Kingdom
*
*Corresponding author.

Abstract

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Introduction

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.

Objectives

To describe the amount of data collected during a multimodal longitudinal RMT study, in an MDD population.

Methods

RADAR-MDD is a multi-centre, prospective observational cohort study. 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 and followed-up for a maximum of 2 years.

Results

A total of 623 individuals with a history of MDD were enrolled in the study with 80% completion rates for primary outcome assessments across all 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. 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.

Disclosure

No significant relationships.

Keywords

remote measurement technologieslongitudinalmajor depressive disorderobservational
Type
Abstract
Information
European Psychiatry , Volume 65 , Special Issue S1: Abstracts of the 30th European Congress of Psychiatry , June 2022 , pp. S112
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of the European Psychiatric Association
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