Evidence map›Paper›PMID 41325605›Full record

ArticleJMIR formative research2025

A Rule-Based Conversational Agent for Mental Health and Well-Being in Young People: Formative Case Series During the Rise of Generative AI.

Aimee-Rose Wrightson-Hester, Gee Anderson, Joel Dunstan, Peter M McEvoy, Christopher J Sutton, Bronwyn Myers, Sarah J Egan, Sara Jane Tai, Melanie Johnston-Hollitt, Wai Chen and 3 more

Abstract read
In one paragraph

Article in JMIR formative research, 2025. 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

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

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

13 authors.

Aimee-Rose Wrightson-HesterCurtin enAble Institute, School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia.ORCID 0000-0001-9601-0745
Gee AndersonCurtin enAble Institute, School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia.ORCID 0000-0002-7592-6241
Joel DunstanCurtin Institute for Data Science, Curtin University, Perth, Western Australia, Australia.ORCID 0009-0004-0120-3464
Peter M McEvoyCurtin enAble Institute, School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia.ORCID 0000-0003-2924-6760
Christopher J SuttonLancashire Clinical Trials Unit,, University of Lancashire, Lancaster, United Kingdom.ORCID 0000-0002-6406-1318
Bronwyn MyersCurtin enAble Institute, School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia.ORCID 0000-0003-0235-6716
Sarah J EganCurtin enAble Institute, School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia.ORCID 0000-0002-3715-4009
Sara Jane TaiSchool of Health Sciences; Division of Psychology and Mental Health, Faculty of Medical and Health Sciences, University of Manchester, Manchester, United Kingdom.ORCID 0000-0002-8316-5796
Melanie Johnston-HollittCurtin Institute for Data Science, Curtin University, Perth, Western Australia, Australia.ORCID 0000-0003-2756-8301
Wai ChenCurtin enAble Institute, School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia.ORCID 0000-0002-0477-7883
Tom GedeonSchool of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth, Western Australia, Australia.ORCID 0000-0001-8356-4909
Joanna C MoullinCurtin enAble Institute, School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia.ORCID 0000-0002-4103-7569
Warren MansellCurtin enAble Institute, School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia.ORCID 0000-0002-5697-1784

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThere is a shortage of services available to address the growing demand for mental health support in Australia and worldwide. Digital interventions, including conversational agents, can overcome barriers to accessing mental health support. The recent advances in large language models have led to an improvement in the perceived human-like naturalness of chatbot conversations, but there is little research on the experience of chatbots to support mental health. Manage your life online (MYLO) is a rule-based chatbot that was co-designed with young people and uses questions to help users explore their problems. In a case series conducted before the release of ChatGPT (OpenAI), users rated a new smartphone interface for MYLO as acceptable, and there was a large effect size for reduction in problem-related distress.

objectiveThis study aimed to evaluate an improved version of MYLO and compare the user experience of MYLO in this case series to the previous version that was completed in November 2022.

methodsWe replicated and extended the previous 2-week case-series, conducted from September to November 2022, by testing 4-week usage of MYLO with a larger sample between October and December 2023. We recruited 24 young people living in Western Australia who self-described as having a lived experience of anxiety or depression. Participants had access to and used MYLO over a 4-week period while completing online weekly surveys that included a range of health and psychological questionnaires. After the 4-week testing phase, participants were invited to provide feedback on their experience of using MYLO through an interview or focus group discussion.

resultsIn total, 13 of the 24 participants were retained throughout the study and took part in interviews. On average, participants had around 4 conversations with MYLO. They experienced both benefits and limitations of these conversations. They spoke about their recent experiences with ChatGPT (released in November 2022 after the previous case-series concluded) and other generative artificial intelligence (AI) tools, stating that they had expected MYLO to possess similar functionality, which it did not. Nonetheless, we found moderate to large effect sizes for improvements in problem-related distress (Cohen d=-1.07), anxiety (Cohen d=-0.41), and psychiatric impairment (Cohen d=-0.60) and some evidence of reliable improvement in clinical outcomes.

conclusionsThese findings have implications for mental health chatbots in the age of ChatGPT and highlight a need for researchers to engage with new technologies to improve user experience, while maintaining the necessary safety and ethical standards that can be a significant challenge for generative AI.

Indexed as

Artificial IntelligenceMental HealthAdolescentAdultFemaleHumansMaleMental Health ServicesWestern AustraliaYoung Adultacceptabilityartificial therapistchatbotschildco-designconversational agentsfeasibilityManage Your Life Onlinemental healthyouth

Identifiers

PMID41325605
PMCPMC12706453

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.