Predicting engagement with an online psychosocial intervention for psychosis: Exploring individual- and intervention-level predictors.
ACMTQ, Autonomous and Controlled Motivations for Treatment Questionnaire
Digital mental health
Digital technology
Engagement
IRR, Incidence rate ratio
Intervention
Psychosis
RSQ, Recovery Style Questionnaire
SCID, Structured Clinical Interview for DSM-IV-TR Axis I Disorders
SMART, Self-Management and Recovery Technology
Journal
Internet interventions
ISSN: 2214-7829
Titre abrégé: Internet Interv
Pays: Netherlands
ID NLM: 101631612
Informations de publication
Date de publication:
Dec 2019
Dec 2019
Historique:
received:
18
04
2019
revised:
09
07
2019
accepted:
10
07
2019
entrez:
1
1
2020
pubmed:
1
1
2020
medline:
1
1
2020
Statut:
epublish
Résumé
Individuals with psychosis demonstrate positive attitudes towards utilising digital technology in mental health treatment. Although preliminary research suggests digital interventions are feasible and acceptable in this population, little is known about how to best promote engagement with these resources. Candidate predictors include therapist support, sources of motivation and recovery style. Understanding what factors predict engagement will aid more effective design and implementation of digital interventions to improve clinical benefits. This study aimed to investigate demographic, psychological, and treatment variables that predict overall and type of engagement with a psychosocial, online intervention for individuals with psychosis. Ninety-eight participants with a history of psychosis were given access to a web program containing modules on self-management and recovery, which they were asked to use flexibly at their own pace. Activity was automatically logged by the system. Baseline measures of demographics, recovery style and motivation were administered, and participants were randomised to receive either website access alone, or website access plus weekly, asynchronous emails from an online coach over 12 weeks. Log and baseline assessment data were used in negative binomial regressions to examine predictors of depth and breadth of use over the intervention period. A logistic regression was used to examine the impact of predictor variables on usage profiles (active or passive). Depth and breadth of engagement were positively predicted by receiving email support, low levels of externally controlled motivations for website use, older age, and having a tertiary education. There was a significant interaction between level of controlled motivation and condition (+/-email) on breadth and depth of engagement: receiving asynchronous emails was associated with increased engagement for individuals with low, but not high, levels of externally controlled motivations. Receiving email support and more autonomous motivations for treatment predicted more active use of the website. Asynchronous email support can promote engagement with online interventions for individuals with psychosis, potentially enabling self-management of illness and improving clinical outcomes. However, those using online interventions due to external motivating factors, may have low levels of engagement with the intervention, irrespective of coaching provided. These findings may guide design and implementation of future online interventions in this population.
Sections du résumé
BACKGROUND
BACKGROUND
Individuals with psychosis demonstrate positive attitudes towards utilising digital technology in mental health treatment. Although preliminary research suggests digital interventions are feasible and acceptable in this population, little is known about how to best promote engagement with these resources. Candidate predictors include therapist support, sources of motivation and recovery style. Understanding what factors predict engagement will aid more effective design and implementation of digital interventions to improve clinical benefits.
OBJECTIVE
OBJECTIVE
This study aimed to investigate demographic, psychological, and treatment variables that predict overall and type of engagement with a psychosocial, online intervention for individuals with psychosis.
METHODS
METHODS
Ninety-eight participants with a history of psychosis were given access to a web program containing modules on self-management and recovery, which they were asked to use flexibly at their own pace. Activity was automatically logged by the system. Baseline measures of demographics, recovery style and motivation were administered, and participants were randomised to receive either website access alone, or website access plus weekly, asynchronous emails from an online coach over 12 weeks. Log and baseline assessment data were used in negative binomial regressions to examine predictors of depth and breadth of use over the intervention period. A logistic regression was used to examine the impact of predictor variables on usage profiles (active or passive).
RESULTS
RESULTS
Depth and breadth of engagement were positively predicted by receiving email support, low levels of externally controlled motivations for website use, older age, and having a tertiary education. There was a significant interaction between level of controlled motivation and condition (+/-email) on breadth and depth of engagement: receiving asynchronous emails was associated with increased engagement for individuals with low, but not high, levels of externally controlled motivations. Receiving email support and more autonomous motivations for treatment predicted more active use of the website.
CONCLUSIONS
CONCLUSIONS
Asynchronous email support can promote engagement with online interventions for individuals with psychosis, potentially enabling self-management of illness and improving clinical outcomes. However, those using online interventions due to external motivating factors, may have low levels of engagement with the intervention, irrespective of coaching provided. These findings may guide design and implementation of future online interventions in this population.
Identifiants
pubmed: 31890619
doi: 10.1016/j.invent.2019.100266
pii: S2214-7829(19)30034-X
pii: 100266
pmc: PMC6926321
doi:
Types de publication
Journal Article
Langues
eng
Pagination
100266Informations de copyright
© 2019 The Authors.
Déclaration de conflit d'intérêts
None.
Références
Inform Health Soc Care. 2015;40(3):267-78
pubmed: 24960233
JMIR Res Protoc. 2016 Apr 28;5(2):e77
pubmed: 27125771
Arch Gen Psychiatry. 1975 Oct;32(10):1269-72
pubmed: 1180660
J Med Internet Res. 2010 Nov 18;12(4):e52
pubmed: 21087922
J Med Internet Res. 2016 Nov 22;18(11):e309
pubmed: 27876684
PLoS One. 2010 Jun 08;5(6):e10939
pubmed: 20544030
Compr Psychiatry. 2017 Apr;74:204-213
pubmed: 28231480
J Med Internet Res. 2009 Apr 24;11(2):e13
pubmed: 19403466
Schizophr Res. 2014 Jun;156(1):96-106
pubmed: 24746468
JMIR Mhealth Uhealth. 2017 Jan 12;5(1):e1
pubmed: 28082257
Int J Behav Med. 2016 Dec;23(6):776-794
pubmed: 26957109
Schizophr Res. 2013 Jan;143(1):143-9
pubmed: 23146146
Australas Psychiatry. 2017 Jun;25(3):257-261
pubmed: 28139947
BMC Psychiatry. 2016 Sep 07;16(1):312
pubmed: 27604363
Clin Schizophr Relat Psychoses. 2017 Aug 4;:null
pubmed: 28777029
Psychiatr Serv. 2010 Nov;61(11):1099-105
pubmed: 21041348
Psychol Med. 2015 Dec;45(16):3357-76
pubmed: 26235445
World Psychiatry. 2007 Jun;6(2):68-74
pubmed: 18235855
Psychiatry Res. 2017 Jan;247:152-154
pubmed: 27893996
J Biomed Inform. 2016 Feb;59:299-307
pubmed: 26732997
BMC Psychiatry. 2016 Jul 11;16:232
pubmed: 27400874
Schizophr Bull. 2014 Nov;40(6):1244-53
pubmed: 24609454
Psychol Serv. 2007 Aug;4(3):202-224
pubmed: 26321884
Neuropsychiatr Dis Treat. 2017 Apr 27;13:1201-1209
pubmed: 28490882
Br J Clin Psychol. 1998 Sep;37(3):269-84
pubmed: 9784883
Schizophr Bull. 2012 May;38(3):414-25
pubmed: 22080492
J Med Internet Res. 2018 Dec 14;20(12):e11491
pubmed: 30552077
JMIR Ment Health. 2016 Jul 27;3(3):e34
pubmed: 27465803
Br J Psychiatry. 2003 Feb;182:123-8
pubmed: 12562739
Cogn Behav Ther. 2011;40(4):251-66
pubmed: 22060248
Rev Psiquiatr Salud Ment. 2017 Jul - Sep;10(3):168-178
pubmed: 28258835
Schizophr Bull. 2016 Mar;42(2):448-55
pubmed: 26400871
J Med Internet Res. 2005 Mar 31;7(1):e11
pubmed: 15829473
J Med Internet Res. 2011 Mar 10;13(1):e30
pubmed: 21393123
J Med Internet Res. 2013 Oct 17;15(10):e231
pubmed: 24135213
Behav Res Ther. 2011 Mar;49(3):158-69
pubmed: 21255767
Can J Psychiatry. 2014 May;59(5):236-42
pubmed: 25007276
J Nerv Ment Dis. 2011 May;199(5):295-300
pubmed: 21543947
J Med Internet Res. 2014 Mar 28;16(3):e95
pubmed: 24681761
Front Psychiatry. 2016 Dec 23;7:196
pubmed: 28066271
J Med Internet Res. 2016 Jun 29;18(6):e146
pubmed: 27357528
J Med Internet Res. 2018 Nov 16;20(11):e292
pubmed: 30446482
Schizophr Bull. 2019 Jun 18;45(4):752-762
pubmed: 30281086
JMIR Mhealth Uhealth. 2017 Jul 20;5(7):e94
pubmed: 28729235
Am Psychol. 2000 Jan;55(1):68-78
pubmed: 11392867
JMIR Ment Health. 2016 May 04;3(2):e15
pubmed: 27146094
J Nerv Ment Dis. 1987 Nov;175(11):681-5
pubmed: 3681279
Am J Prev Med. 2016 Nov;51(5):833-842
pubmed: 27745683
JMIR Ment Health. 2018 Nov 23;5(4):e10652
pubmed: 30470681
J Med Internet Res. 2017 Sep 15;19(9):e306
pubmed: 28916506
J Med Internet Res. 2016 Mar 08;18(3):e52
pubmed: 26957354
J Affect Disord. 2011 Mar;129(1-3):117-25
pubmed: 20889214
J Med Internet Res. 2016 May 31;18(5):e121
pubmed: 27245693
PLoS One. 2013 Aug 21;8(8):e70086
pubmed: 23990896
Br J Psychiatry. 2011 Dec;199(6):445-52
pubmed: 22130746
J Med Internet Res. 2011 Nov 04;13(4):e89
pubmed: 22057287
Psychiatr Serv. 2017 Nov 1;68(11):1172-1181
pubmed: 28669284