Evidence map›Paper›PMID 35401031›Full record

ArticleAutonomous agents and multi-agent systems2022

Exploring the influence of a user-specific explainable virtual advisor on health behaviour change intentions.

Amal Abdulrahman, Deborah Richards, Ayse Aysin Bilgin

Abstract read
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In one paragraph

Article in Autonomous agents and multi-agent systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Amal AbdulrahmanSchool of Computing, Macquarie University, Balaclava Road, Sydney, 2109 NSW Australia.ORCID 0000-0001-5360-0833
Deborah RichardsSchool of Computing, Macquarie University, Balaclava Road, Sydney, 2109 NSW Australia.
Ayse Aysin BilginSchool of Mathematical and Physical Sciences, Macquarie University, Balaclava Road, Sydney, 2109 NSW Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Virtual advisors (VAs) are being utilised almost in every service nowadays from entertainment to healthcare. To increase the user's trust in these VAs and encourage the users to follow their advice, they should have the capability of explaining their decisions, particularly, when the decision is vital such as health advice. However, the role of an explainable VA in health behaviour change is understudied. There is evidence that people tend to change their intentions towards health behaviour when the persuasion message is linked to their mental state. Thus, this study explores this link by introducing an explainable VA that provides explanation according to the user's mental state (beliefs and goals) rather than the agent's mental state as commonly utilised in explainable agents. It further explores the influence of different explanation patterns that refer to beliefs, goals, or beliefs&goals on the user's behaviour change. An explainable VA was designed to advise undergraduate students how to manage their study-related stress by motivating them to change certain behaviours. With 91 participants, the VA was evaluated and the results revealed that user-specific explanation could significantly encourage behaviour change intentions and build good user-agent relationship. Small differences were found between the three types of explanation patterns.

Indexed as

Behaviour change intentionExplainable agentsPersonal virtual advisorReason explanationTrustWorking alliance

Identifiers

PMID35401031
PMCPMC8977831

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.