Remote assessment of disease and relapse in major depressive disorder (RADAR-MDD): a multi-centre prospective cohort study protocol.
M-health
Major depressive disorder
Observational cohort
Outcome measurement
Passive sensing
Prospective study
Remote measurement technology
Journal
BMC psychiatry
ISSN: 1471-244X
Titre abrégé: BMC Psychiatry
Pays: England
ID NLM: 100968559
Informations de publication
Date de publication:
18 02 2019
18 02 2019
Historique:
received:
07
09
2018
accepted:
01
02
2019
entrez:
20
2
2019
pubmed:
20
2
2019
medline:
18
12
2019
Statut:
epublish
Résumé
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). 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. RADAR-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). This 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.
Sections du résumé
BACKGROUND
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). 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.
METHODS
RADAR-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).
DISCUSSION
This 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.
Identifiants
pubmed: 30777041
doi: 10.1186/s12888-019-2049-z
pii: 10.1186/s12888-019-2049-z
pmc: PMC6379954
doi:
Types de publication
Clinical Trial Protocol
Journal Article
Multicenter Study
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
72Subventions
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Alzheimer's Society
ID : 171
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_PC_17214
Pays : United Kingdom
Investigateurs
Sonia DiFrancesco
(S)
Katie White
(K)
Alina Ivan
(A)
Ashley Polhemus
(A)
Jose Ferrao
(J)
Michiel Ringkjøbing-Elema
(M)
Francesco Nobilia
(F)
Wolfgang Viechtbauer
(W)
Sjaak Peelen
(S)
Zulqarnain Rashid
(Z)
Janneke Boere
(J)
Nicholas Cummins
(N)
Nick Meyer
(N)
Références
Br J Psychiatry. 2002 May;180:461-4
pubmed: 11983645
Curr Psychiatry Rep. 2004 Dec;6(6):430-7
pubmed: 15538991
Soc Sci Med. 2013 Dec;98:179-86
pubmed: 24331897
Psychol Med. 2017 Jan;47(2):279-289
pubmed: 27702414
JMIR Mhealth Uhealth. 2019 Aug 01;7(8):e11734
pubmed: 31373275
J Med Internet Res. 2018 Jul 12;20(7):e10480
pubmed: 30001997
Am J Psychiatry. 2006 Jan;163(1):28-40
pubmed: 16390886
JMIR Res Protoc. 2016 Aug 01;5(3):e149
pubmed: 27480247
Sci Transl Med. 2015 Apr 15;7(283):283rv3
pubmed: 25877894
J Consult Clin Psychol. 2015 Oct;83(5):964-75
pubmed: 26371618
J Biomed Inform. 2009 Apr;42(2):377-81
pubmed: 18929686
Arch Intern Med. 2006 May 22;166(10):1092-7
pubmed: 16717171
JMIR Mhealth Uhealth. 2019 Jan 30;7(1):e11325
pubmed: 30698535
J Res Pers. 2011 Feb 1;45(1):2-9
pubmed: 21516166
BMC Med. 2017 Dec 12;15(1):215
pubmed: 29228943
Neurol Ther. 2013 Mar 13;2(1-2):25-42
pubmed: 26000214
Br J Psychiatry Suppl. 2000;(39):s28-33
pubmed: 10945075
J Med Internet Res. 2017 Jul 24;19(7):e262
pubmed: 28739561
J Voice. 2017 Mar;31(2):256.e1-256.e6
pubmed: 27473933
Annu Rev Clin Psychol. 2017 May 8;13:23-47
pubmed: 28375728
Chronobiol Int. 2018 Apr;35(4):465-476
pubmed: 29235907
Clin Psychol Rev. 2015 Nov;41:16-26
pubmed: 25754289
Psychol Med. 2004 Aug;34(6):1001-11
pubmed: 15554571
J Ment Health. 2015;24(5):321-32
pubmed: 26017625
J Psychosom Res. 2006 Jun;60(6):631-7
pubmed: 16731240
J Clin Psychiatry. 2010 Dec;71(12):1645-56
pubmed: 21190638
J Consult Clin Psychol. 2013 Jun;81(3):508-17
pubmed: 23477478
J Occup Environ Med. 2004 Jun;46(6 Suppl):S46-55
pubmed: 15194895
Clin Psychol Rev. 2012 Aug;32(6):510-23
pubmed: 22721999
J Affect Disord. 2009 Apr;114(1-3):163-73
pubmed: 18752852
Psychol Med. 2009 Sep;39(9):1533-47
pubmed: 19215626
J Clin Psychiatry. 2014 Jan;75(1):8-14
pubmed: 24345473
Alcohol Clin Exp Res. 2000 May;24(5):659-65
pubmed: 10832907
Epidemiol Psychiatr Sci. 2011 Jun;20(2):141-50
pubmed: 21714361
Int J Neuropsychopharmacol. 2015 Jul 07;19(2):
pubmed: 26152228
Evid Based Ment Health. 2017 Nov;20(4):128-133
pubmed: 29056608
J Clin Epidemiol. 1996 Dec;49(12):1373-9
pubmed: 8970487
Acta Psychiatr Scand. 1990 Jul;82(1):77-81
pubmed: 2399824