Evidence map›Paper›PMID 38814854›Full record

ArticlePLOS digital health2024

Patient and public involvement workshop to shape artificial intelligence-supported connected asthma self-management research.

Chi Yan Hui, Ann Victoria Shenton, Claire Martin, David Weatherill, Dianna Moylan, Morag Hayes, Laura Gonzalez Rienda, Emma Kinley, Stefanie Eck, Hilary Pinnock

Abstract read
In one paragraph

Article in PLOS digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. 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

10 authors.

Chi Yan HuiAsthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, United Kingdom.ORCID https://orcid.org/0000-0002-6375-653X
Ann Victoria ShentonAsthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, United Kingdom.
Claire MartinAsthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, United Kingdom.
David WeatherillAsthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, United Kingdom.
Dianna MoylanAsthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, United Kingdom.
Morag HayesAsthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, United Kingdom.
Laura Gonzalez RiendaAsthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, United Kingdom.
Emma KinleySchool of Psychology, Faculty of Health, Liverpool John Moore's University, United Kingdom.
Stefanie EckInstitute of General Practice and Health Services Research, TUM School of Medicine, Technical University of Munich (TUM), Germany.ORCID https://orcid.org/0000-0002-4708-9001
Hilary PinnockAsthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digital interventions with artificial intelligence (AI) can potentially support people with asthma to reduce the risk of exacerbation. Engaging patients throughout the development process is essential to ensure usability of the intervention for the end-users. Using our Connected for Asthma (C4A) intervention as an exemplar, we explore how patient involvement can shape a digital intervention. Seven Patient and Public Involvement (PPI) colleagues from the Asthma UK Centre for Applied Research participated in four advisory workshops to discuss how they would prefer to use/interact with AI to support living with their asthma, the benefit and caveats to use the AI that incorporated asthma monitoring and indoor/outdoor environmental data. Discussion focussed on the three most wanted use cases identified in our previous studies. PPI colleagues wanted AI to support data collection, remind them about self-management tasks, teach them about asthma environmental triggers, identify risk, and empower them to confidently look after their asthma whilst emphasising that AI does not replace clinicians. The discussion informed the key components in the next C4A interventions, including the approach to interacting with AI, the technology features and the research topics. Attendees highlighted the importance of considering health inequities, the presentation of data, and concerns about data accuracy, data privacy, security and ownership. We have demonstrated how patient roles can shift from that of 'user' (the traditional 'tester' of a digital intervention), to a co-design partner who shapes the next iteration of the intervention. Technology innovators should seek practical and feasible strategies to involve PPI colleagues throughout the development cycle of a digital intervention; supporting researchers to explore the barriers, concerns, enablers and advantages of implementing digital healthcare.

Identifiers

PMID38814854
PMCPMC11139256

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LicenceCC BY
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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.