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
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

e12996

Subventions

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.

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Auteurs

Katherine Easton (K)

School of Health and Related Research, The University of Sheffield, Sheffield, United Kingdom.
Centre for Assistive Technology and Connected Healthcare, The University of Sheffield, Sheffield, United Kingdom.

Stephen Potter (S)

School of Health and Related Research, The University of Sheffield, Sheffield, United Kingdom.
Centre for Assistive Technology and Connected Healthcare, The University of Sheffield, Sheffield, United Kingdom.

Remi Bec (R)

Collaboration for Leadership in Applied Health Research and Care Yorkshire and Humber, Royal Hallamshire Hospital, National Institute of Health, Sheffield, United Kingdom.
Lab4Living, Art and Design Research Centre, Sheffield Hallam University, Sheffield, United Kingdom.

Matthew Bennion (M)

Centre for Assistive Technology and Connected Healthcare, The University of Sheffield, Sheffield, United Kingdom.
Department of Computer Science, The University of Sheffield, Sheffield, United Kingdom.
Department of Psychology, The University of Sheffield, Sheffield, United Kingdom.

Heidi Christensen (H)

Centre for Assistive Technology and Connected Healthcare, The University of Sheffield, Sheffield, United Kingdom.
Department of Computer Science, The University of Sheffield, Sheffield, United Kingdom.

Cheryl Grindell (C)

Collaboration for Leadership in Applied Health Research and Care Yorkshire and Humber, Royal Hallamshire Hospital, National Institute of Health, Sheffield, United Kingdom.

Bahman Mirheidari (B)

Department of Computer Science, The University of Sheffield, Sheffield, United Kingdom.

Scott Weich (S)

School of Health and Related Research, The University of Sheffield, Sheffield, United Kingdom.

Luc de Witte (L)

School of Health and Related Research, The University of Sheffield, Sheffield, United Kingdom.
Centre for Assistive Technology and Connected Healthcare, The University of Sheffield, Sheffield, United Kingdom.

Daniel Wolstenholme (D)

Collaboration for Leadership in Applied Health Research and Care Yorkshire and Humber, Royal Hallamshire Hospital, National Institute of Health, Sheffield, United Kingdom.

Mark S Hawley (MS)

School of Health and Related Research, The University of Sheffield, Sheffield, United Kingdom.
Centre for Assistive Technology and Connected Healthcare, The University of Sheffield, Sheffield, United Kingdom.

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Classifications MeSH