A Virtual Agent to Support Individuals Living With Physical and Mental Comorbidities: Co-Design and Acceptability Testing.
COPD
artificial intelligence
chatbot
chronic illness
chronic obstructive pulmonary disease
comorbidity
computer-assisted therapy
conversational agent
mental health
self-management
virtual systems
Journal
Journal of medical Internet research
ISSN: 1438-8871
Titre abrégé: J Med Internet Res
Pays: Canada
ID NLM: 100959882
Informations de publication
Date de publication:
30 05 2019
30 05 2019
Historique:
received:
29
11
2018
accepted:
24
03
2019
revised:
13
03
2019
entrez:
1
6
2019
pubmed:
1
6
2019
medline:
14
2
2020
Statut:
epublish
Résumé
Individuals living with long-term physical health conditions frequently experience co-occurring mental health problems. This comorbidity has a significant impact on an individual's levels of emotional distress, health outcomes, and associated health care utilization. As health care services struggle to meet demand and care increasingly moves to the community, digital tools are being promoted to support patients to self-manage their health. One such technology is the autonomous virtual agent (chatbot, conversational agent), which uses artificial intelligence (AI) to process the user's written or spoken natural language and then to select or construct the corresponding appropriate responses. This study aimed to co-design the content, functionality, and interface modalities of an autonomous virtual agent to support self-management for patients with an exemplar long-term condition (LTC; chronic pulmonary obstructive disease [COPD]) and then to assess the acceptability and system content. We conducted 2 co-design workshops and a proof-of-concept implementation of an autonomous virtual agent with natural language processing capabilities. This implementation formed the basis for video-based scenario testing of acceptability with adults with a diagnosis of COPD and health professionals involved in their care. Adults (n=6) with a diagnosis of COPD and health professionals (n=5) specified 4 priority self-management scenarios for which they would like to receive support: at the time of diagnosis (information provision), during acute exacerbations (crisis support), during periods of low mood (emotional support), and for general self-management (motivation). From the scenario testing, 12 additional adults with COPD felt the system to be both acceptable and engaging, particularly with regard to internet-of-things capabilities. They felt the system would be particularly useful for individuals living alone. Patients did not explicitly separate mental and physical health needs, although the content they developed for the virtual agent had a clear psychological approach. Supported self-management delivered via an autonomous virtual agent was acceptable to the participants. A co-design process has allowed the research team to identify key design principles, content, and functionality to underpin an autonomous agent for delivering self-management support to older adults living with COPD and potentially other LTCs.
Sections du résumé
BACKGROUND
Individuals living with long-term physical health conditions frequently experience co-occurring mental health problems. This comorbidity has a significant impact on an individual's levels of emotional distress, health outcomes, and associated health care utilization. As health care services struggle to meet demand and care increasingly moves to the community, digital tools are being promoted to support patients to self-manage their health. One such technology is the autonomous virtual agent (chatbot, conversational agent), which uses artificial intelligence (AI) to process the user's written or spoken natural language and then to select or construct the corresponding appropriate responses.
OBJECTIVE
This study aimed to co-design the content, functionality, and interface modalities of an autonomous virtual agent to support self-management for patients with an exemplar long-term condition (LTC; chronic pulmonary obstructive disease [COPD]) and then to assess the acceptability and system content.
METHODS
We conducted 2 co-design workshops and a proof-of-concept implementation of an autonomous virtual agent with natural language processing capabilities. This implementation formed the basis for video-based scenario testing of acceptability with adults with a diagnosis of COPD and health professionals involved in their care.
RESULTS
Adults (n=6) with a diagnosis of COPD and health professionals (n=5) specified 4 priority self-management scenarios for which they would like to receive support: at the time of diagnosis (information provision), during acute exacerbations (crisis support), during periods of low mood (emotional support), and for general self-management (motivation). From the scenario testing, 12 additional adults with COPD felt the system to be both acceptable and engaging, particularly with regard to internet-of-things capabilities. They felt the system would be particularly useful for individuals living alone.
CONCLUSIONS
Patients did not explicitly separate mental and physical health needs, although the content they developed for the virtual agent had a clear psychological approach. Supported self-management delivered via an autonomous virtual agent was acceptable to the participants. A co-design process has allowed the research team to identify key design principles, content, and functionality to underpin an autonomous agent for delivering self-management support to older adults living with COPD and potentially other LTCs.
Identifiants
pubmed: 31148545
pii: v21i5e12996
doi: 10.2196/12996
pmc: PMC6658240
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e12996Subventions
Organisme : Medical Research Council
ID : MC_PC_14115
Pays : United Kingdom
Informations de copyright
©Katherine Easton, Stephen Potter, Remi Bec, Matthew Bennion, Heidi Christensen, Cheryl Grindell, Bahman Mirheidari, Scott Weich, Luc de Witte, Daniel Wolstenholme, Mark S Hawley. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 30.05.2019.
Références
J Psychosom Res. 2005 Dec;59(6):429-43
pubmed: 16310027
JMIR Ment Health. 2017 Jun 06;4(2):e19
pubmed: 28588005
J Gen Intern Med. 2008 Jan;23(1):25-36
pubmed: 17968628
J Am Med Inform Assoc. 2018 Sep 1;25(9):1248-1258
pubmed: 30010941
BMC Health Serv Res. 2018 Jul 25;18(1):585
pubmed: 30045726
BMJ. 2015 Nov 11;351:h5627
pubmed: 26559241
Respir Med. 2010 Sep;104(9):1246-53
pubmed: 20457513
Internet Interv. 2017 Oct 10;10:39-46
pubmed: 30135751
BMJ Open. 2015 Nov 30;5(11):e008581
pubmed: 26621513
Soc Sci Med. 2010 Oct;71(7):1308-1315
pubmed: 20675026
J Cardiovasc Nurs. 2016 Jul-Aug;31(4):367-79
pubmed: 25930162
Psychol Med. 2008 Nov;38(11):1521-30
pubmed: 18205964
Behav Cogn Psychother. 2014 Nov;42(6):731-46
pubmed: 23899405
Lancet. 2007 Sep 8;370(9590):851-8
pubmed: 17826170
J Med Internet Res. 2017 May 09;19(5):e151
pubmed: 28487267
Lancet Psychiatry. 2018 Oct;5(10):845-854
pubmed: 30170964
Br J Psychiatry. 2005 Jan;186:11-7
pubmed: 15630118
Epidemiol Psychiatr Sci. 2011 Jun;20(2):141-50
pubmed: 21714361
Biol Psychiatry. 2003 Aug 1;54(3):216-26
pubmed: 12893098