Evidence map›Paper›PMID 37808976›Full record

ArticleFrontiers in public health2023

Co-designing an interactive artificial intelligent system with post-stroke patients and caregivers to augment the lost abilities and improve their quality of life: a human-centric approach.

Sara Ventura, Giovanni Ottoboni, Giada Lullini, Rabih Chattat, Laura Simoncini, Elisabetta Magni, Roberto Piperno, Fabio La Porta, Alessia Tessari

Abstract read
In one paragraph

Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

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

  1. Pooled it
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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

9 authors.

Sara VenturaDepartment of Psychology, University of Bologna, Bologna, Italy.
Giovanni OttoboniDepartment of Psychology, University of Bologna, Bologna, Italy.
Giada LulliniIRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy.
Rabih ChattatDepartment of Psychology, University of Bologna, Bologna, Italy.
Laura SimonciniMontecatone Rehabilitation Institute, Imola, Italy.
Elisabetta MagniIRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy.
Roberto PipernoIRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy.
Fabio La PortaIRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy.
Alessia TessariDepartment of Psychology, University of Bologna, Bologna, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: The motor disability due to stroke compromises the autonomy of patients and caregivers. To support autonomy and other personal and social needs, trustworthy, multifunctional, adaptive, and interactive assistive devices represent optimal solutions. To fulfill this aim, an artificial intelligence system named MAIA would aim to interpret users' intentions and translate them into actions performed by assistive devices. Analyzing their perspectives is essential to develop the MAIA system operating in harmony with patients' and caregivers' needs as much as possible. Methods: Post-stroke patients and caregivers were interviewed to explore the impact of motor disability on their lives, previous experiences with assistive technologies, opinions, and attitudes about MAIA and their needs. Interview transcripts were analyzed using inductive thematic analysis. Results: Sixteen interviews were conducted with 12 post-stroke patients and four caregivers. Three themes emerged: (1) Needs to be satisfied, (2) MAIA technology acceptance, and (3) Perceived trustfulness. Overall, patients are seeking rehabilitative technology, contrary to caregivers needing assistive technology to help them daily. An easy-to-use and ergonomic technology is preferable. However, a few participants trust a system based on artificial intelligence. Conclusion: An interactive artificial intelligence technology could help post-stroke patients and their caregivers to restore motor autonomy. The insights from participants to develop the system depends on their motor ability and the role of patients or caregiver. Although technology grows exponentially, more efforts are needed to strengthen people's trust in advanced technology.

Indexed as

Motor DisordersPersons with DisabilitiesStrokeArtificial IntelligenceCaregiversHumansQuality of Lifecaregivershuman-centric designintelligent assistive technologystroke patientstechnology acceptance

Identifiers

PMID37808976
PMCPMC10551166

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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.