Predictors of engagement with remote sensing technologies for symptom measurement in Major Depressive Disorder
https://doi.org/10.1016/j.jad.2022.05.005
F. Matcham a,*,1, E. Carrb,1, K.M. Whitea, D. Leightleya, F. Lamersc, S. Siddid, P. Annase, G. de Girolamof, J.M. Harod, M. Horsfallc, A. Ivana, G. Lavellea, Q. Lig, F. Lombardinid, D.C. Mohrh, V.A. Narayang, B.W.H.J. Penninxc, C. Oetzmanna, M. Corominai, S.K. Simblettj, J. Weyerk, T. Wykesj,l, S. Zorbask, J.C. Brasene, I. Myin-Germeysm, P. Condeb, R.J.B. Dobsonb, A. A. Folarinb, Y. Ranjanb, Z. Rashidb, N. Cumminsb, J. Dineleyb,n, S. Vairavang, M. Hotopfa,l, on behalf of the RADAR-CNS consortiumo.
1Joint first authors.
*Corresponding author.
a. Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
b. Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
c. Department of Psychiatry and Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
d. Parc Sanitari Sant Joan de Déu, Fundació San Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
e. H. Lundbeck A/S, Valby, Denmark
f. IRCCS Instituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
g. Janssen Research and Development, LLC, Titusville, NJ, USA
h. Center for Behavioral Intervention Technologies, Department of Preventative Medicine, Northwestern University, Chicago, IL, USA
i. Parc Sanitari Joan de D ́eu, Barcelona, Spain
j. Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
k. RADAR-CNS Patient Advisory Board
l. South London and Maudsley NHS Foundation Trust, London, UK
m. Department for Neurosciences, Center for Contextual Psychiatry, KU Leuven, Leuven, Belgium
n. EIHW – Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany
o. www.radar-cns.org, UK
Remote sensing for the measurement and management of long-term conditions such as Major Depressive Disorder (MDD) is becoming more prevalent. User-engagement is essential to yield any benefits. We tested three hypotheses examining associations between clinical characteristics, perceptions of remote sensing, and objective user engagement metrics.
The Remote Assessment of Disease and Relapse – Major Depressive Disorder (RADAR-MDD) study is a multicentre longitudinal observational cohort study in people with recurrent MDD. Participants wore a FitBit and completed app-based assessments every two weeks for a median of 18 months. Multivariable random effects regression models pooling data across timepoints were used to examine associations between variables.
A total of 547 participants (87.8% of the total sample) were included in the current analysis. Higher levels of anxiety were associated with lower levels of perceived technology ease of use; increased functional disability was associated with small differences in perceptions of technology usefulness and usability. Participants who reported higher system ease of use, usefulness, and acceptability subsequently completed more app-based questionnaires and tended to wear their FitBit activity tracker for longer. All effect sizes were small and unlikely to be of practical significance.
Symptoms of depression, anxiety, functional disability, and perceptions of system usability are measured at the same time. These therefore represent cross-sectional associations rather than predictions of future perceptions.
These findings suggest that perceived usability and actual use of remote measurement technologies in people with MDD are robust across differences in severity of depression, anxiety, and functional impairment.
Major Depressive Disorder, Remote sensing, Cohort study, Engagement, Predictors.