Evidence map›Paper›PMID 42721450›Full record

ArticleJMIR formative research2026

An Evidence-Based AI Virtual Assistant for Young People With Attention Deficit Hyperactivity Disorder: Co-Design and Prototype Development.

Eleanor F Bryant, David Hallett, Emily Nielsen, Tali Evans, Jacqueline Rees-Lee, Nicole Riley, Tamsin Newlove-Delgado, Anna Price

Abstract read
In one paragraph

Article in JMIR formative research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Eleanor F BryantUniversity of Exeter Medical School, University of Exeter, Exeter, United Kingdom.ORCID https://orcid.org/0009-0003-3885-5311
David HallettDigital Futures, Torbay and South Devon NHS Foundation Trust, Devon, United Kingdom.ORCID https://orcid.org/0009-0002-1962-4932
Emily NielsenSchool of Engineering Mathematics and Technology, University of Bristol, Bristol, United Kingdom.ORCID https://orcid.org/0000-0003-2389-541X
Tali EvansUniversity of Exeter Medical School, University of Exeter, Exeter, United Kingdom.ORCID https://orcid.org/0009-0008-8177-5874
Jacqueline Rees-LeeDigital Futures, Torbay and South Devon NHS Foundation Trust, Devon, United Kingdom.ORCID https://orcid.org/0009-0000-6083-6748
Nicole RileySmartADHD Research Advisory Group, University of Exeter, Devon, United Kingdom.ORCID https://orcid.org/0009-0002-2800-9157
Tamsin Newlove-DelgadoUniversity of Exeter Medical School, University of Exeter, Exeter, United Kingdom.ORCID https://orcid.org/0000-0002-5192-3724
Anna PriceUniversity of Exeter Medical School, University of Exeter, Exeter, United Kingdom.ORCID https://orcid.org/0000-0001-9147-1876

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThough attention deficit hyperactivity disorder (ADHD) is thought to be the most prevalent neurodevelopmental disorder in young people worldwide, there are inequalities in access to psychoeducation and health care support. One way to improve access, potentially increase engagement, reduce health care inequalities, and enhance care is by co-developing digital responsive interventions. These have the potential to support long-term condition management and to act as an adjunct to usual care. Virtual assistants that use large language models can provide information in response to questions and learn to tailor communication to suit an individual user's needs. This can be especially valuable for people with ADHD who often struggle to regulate attention and can experience communication challenges. Involving people with lived experience in the co-design process is crucial for the development of effective digital interventions. Therefore, this article explores the views and preferences of young people with ADHD and their supporters from the United Kingdom who collaborated with researchers to co-design a prototype chatbot.

objectiveThis study aimed to co-develop an evidence-based chatbot prototype, intended to help young people with ADHD thrive through improved access to health care information, psychoeducation, and self-management strategies.

methodsAn interdisciplinary team was established, including researchers, software developers, clinicians, and lived experience collaborators. Research advisory and working groups were set up in ways that facilitated flexible involvement. Following the person-based approach, guiding principles were established, and workshops were held with young people with ADHD and supporters of young people with ADHD to co-develop an early prototype. Feedback was sought via think-aloud interviews with lived experience collaborators.

resultsIn total, 9 experts by lived experience and 3 health care professionals chose to engage in workshops, and this feedback informed the development of a SmartADHD chatbot prototype. An off-the-shelf chatbot (GPT-4o hosted on Convai) was trained using resources from the National Health Service (NHS). Overall, 6 experts by lived experience engaged with think-aloud interviews, providing feedback on the prototype conversational flow and feel, the avatar, the text-to-speech, the chatbox feature, and the content of the messages. Seven recommendations are made for future development, which will inform the SmartADHD program of work.

conclusionsThese findings provide rich data on the preferences of people with ADHD. Specific recommendations for a chatbot for young adults with ADHD have not been investigated before with young people, making this study a novel contribution to the field. These findings provide an excellent foundation for chatbot development for this group and may be relevant for those developing digital tools for people with ADHD across the lifespan and other neurodevelopmental conditions. Further work is required to elucidate the views of health care professionals and identify the limits of the technology before subsequent evaluation.

Indexed as

Artificial IntelligenceAttention Deficit Disorder with HyperactivityAdolescentEvidence-Based PracticeFemaleHumansMaleQualitative ResearchUnited Kingdomartificial intelligenceattention deficit hyperactivity disordergenerative AIhealth equityhealth services accessibilityinnovationinterdisciplinary researchneurodevelopmental disorderspatient participationunderserved population

Identifiers

PMID42721450
PMCPMC13613043

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.