Evidence map›Paper›PMID 35632061›Full record

ArticleSensors (Basel, Switzerland)2022

A Mental Health Chatbot with Cognitive Skills for Personalised Behavioural Activation and Remote Health Monitoring.

Prabod Rathnayaka, Nishan Mills, Donna Burnett, Daswin De Silva, Damminda Alahakoon, Richard Gray

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 4 pooled it
–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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

28 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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  10. Digital Psychiatry with Chatbot: Recent Advances and Limitations.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2025
    Review
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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

6 authors.

Prabod RathnayakaCentre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3086, Australia.ORCID 0000-0003-2078-057X
Nishan MillsCentre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3086, Australia.ORCID 0000-0003-2157-3767
Donna BurnettCentre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3086, Australia.ORCID 0000-0003-2972-2552
Daswin De SilvaCentre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3086, Australia.ORCID 0000-0003-3878-5969
Damminda AlahakoonCentre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3086, Australia.ORCID 0000-0003-3291-888X
Richard GrayCentre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3086, Australia.ORCID 0000-0001-9694-4206

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mental health issues are at the forefront of healthcare challenges facing contemporary human society. These issues are most prevalent among working-age people, impacting negatively on the individual, his/her family, workplace, community, and the economy. Conventional mental healthcare services, although highly effective, cannot be scaled up to address the increasing demand from affected individuals, as evidenced in the first two years of the COVID-19 pandemic. Conversational agents, or chatbots, are a recent technological innovation that has been successfully adapted for mental healthcare as a scalable platform of cross-platform smartphone applications that provides first-level support for such individuals. Despite this disposition, mental health chatbots in the extant literature and practice are limited in terms of the therapy provided and the level of personalisation. For instance, most chatbots extend Cognitive Behavioural Therapy (CBT) into predefined conversational pathways that are generic and ineffective in recurrent use. In this paper, we postulate that Behavioural Activation (BA) therapy and Artificial Intelligence (AI) are more effectively materialised in a chatbot setting to provide recurrent emotional support, personalised assistance, and remote mental health monitoring. We present the design and development of our BA-based AI chatbot, followed by its participatory evaluation in a pilot study setting that confirmed its effectiveness in providing support for individuals with mental health issues.

Indexed as

COVID-19Mobile ApplicationsArtificial IntelligenceCognitionFemaleHumansMaleMental HealthPandemicsPilot Projectsartificial intelligencebehavioural activationchatbotconversational agentsemotional supportmental health monitoringmental health supportpersonalised assistance

Identifiers

PMID35632061
PMCPMC9148050

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.