Predictors of engagement with remote sensing technologies for symptom measurement in Major Depressive Disorder.

Anxiety Disorders Cross-Sectional Studies Depressive Disorder, Major / diagnosis Humans Recurrence Remote Sensing Technology
Cohort study Engagement Major Depressive Disorder Predictors Remote sensing

Journal

Journal of affective disorders
ISSN: 1573-2517
Titre abrégé: J Affect Disord
Pays: Netherlands
ID NLM: 7906073

Informations de publication

Date de publication:
01 08 2022
Historique:
received: 21 03 2022
revised: 28 04 2022
accepted: 02 05 2022
pubmed: 8 5 2022
medline: 9 6 2022
entrez: 7 5 2022
Statut: ppublish

Résumé

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.

Sections du résumé

BACKGROUND
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.
METHODS
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.
RESULTS
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.
LIMITATIONS
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.
CONCLUSIONS
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.

Identifiants

DOI: 10.1016/j.jad.2022.05.005 PMID: 35525507
pubmed: 35525507
pii: S0165-0327(22)00507-9
doi: 10.1016/j.jad.2022.05.005
pii:
doi:

Types de publication

Journal Article Observational Study Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

106-115

Commentaires et corrections

Type : CommentIn

Informations de copyright

Copyright © 2022 Elsevier B.V. All rights reserved.

Auteurs

F Matcham (F)

Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK. Electronic address: faith.matcham@kcl.ac.uk.

E Carr (E)

Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.

K M White (KM)

Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.

D Leightley (D)

Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.

F Lamers (F)

Department of Psychiatry and Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands.

S Siddi (S)

Parc Sanitari Sant Joan de Déu, Fundació San Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain.

P Annas (P)

H. Lundbeck A/S, Valby, Denmark.

G de Girolamo (G)

IRCCS Instituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.

J M Haro (JM)

Parc Sanitari Sant Joan de Déu, Fundació San Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain.

M Horsfall (M)

Department of Psychiatry and Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands.

A Ivan (A)

Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.

G Lavelle (G)

Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.

Q Li (Q)

Janssen Research and Development, LLC, Titusville, NJ, USA.

F Lombardini (F)

Parc Sanitari Sant Joan de Déu, Fundació San Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain.

D C Mohr (DC)

Center for Behavioral Intervention Technologies, Department of Preventative Medicine, Northwestern University, Chicago, IL, USA.

V A Narayan (VA)

Janssen Research and Development, LLC, Titusville, NJ, USA.

B W H J Penninx (BWHJ)

Department of Psychiatry and Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands.

C Oetzmann (C)

Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.

M Coromina (M)

Parc Sanitari Joan de Déu, Barcelona, Spain.

S K Simblett (SK)

Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.

J Weyer (J)

RADAR-CNS Patient Advisory Board.

T Wykes (T)

Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK; South London and Maudsley NHS Foundation Trust, London, UK.

S Zorbas (S)

RADAR-CNS Patient Advisory Board.

J C Brasen (JC)

H. Lundbeck A/S, Valby, Denmark.

I Myin-Germeys (I)

Department for Neurosciences, Center for Contextual Psychiatry, KU Leuven, Leuven, Belgium.

P Conde (P)

Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.

R J B Dobson (RJB)

Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.

A A Folarin (AA)

Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.

Y Ranjan (Y)

Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.

Z Rashid (Z)

Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.

N Cummins (N)

Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.

J Dineley (J)

Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK; EIHW - Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany.

S Vairavan (S)

Janssen Research and Development, LLC, Titusville, NJ, USA.

M Hotopf (M)

Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK; South London and Maudsley NHS Foundation Trust, London, UK.
www.radar-cns.org, UK.

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Classifications MeSH

questionsmedicales.fr › Troubles mentaux › Troubles anxieux › Predictors of engagement with remote sensing...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Caractéristiques des études épidémiologiques › Études épidémiologiques › Études transversales › Predictors of engagement with remote sensing...
questionsmedicales.fr › Troubles mentaux › Troubles de l'humeur › Trouble dépressif › Trouble dépressif majeur › Predictors of engagement with remote sensing...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Predictors of engagement with remote sensing...
questionsmedicales.fr › États, signes et symptômes pathologiques › Processus pathologiques › Caractéristiques de la maladie › Récidive › Predictors of engagement with remote sensing...
questionsmedicales.fr › Sciences de l'information › Sources d'information › Moyens de communication › Télécommunications › Télémétrie › Technologie de télédétection › Predictors of engagement with remote sensing...