Evidence map›Paper›PMID 42763734›Full record

ArticleJournal of multidisciplinary healthcare2026

User Needs for an Artificial Intelligence-Empowered Basic Activities of Daily Living Training System After Stroke: A Qualitative Study.

Qing Wang, Jing Xiong, Suzhu Lin, Xiujiao Lin, Feifei Lin, Yating Xu, Baoyuan Xie

Abstract read
In one paragraph

Article in Journal of multidisciplinary healthcare, 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

7 authors.

Qing Wang *School of Nursing, Fujian Medical University, Fuzhou, Fujian, People's Republic of China.ORCID 0009-0004-7096-2157
Jing Xiong *Operating Room, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, People's Republic of China.
Suzhu LinDepartment of Neurology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, People's Republic of China.
Xiujiao LinDepartment of Neurology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, People's Republic of China.ORCID 0009-0007-3068-9127
Feifei LinDepartment of Neurology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, People's Republic of China.
Yating XuIntensive Care Unit, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, People's Republic of China.
Baoyuan XieNursing Teaching and Research Office, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To explore patients' and primary caregivers' willingness to use an artificial intelligence (AI)-empowered basic activities of daily living (BADL) training system and their needs, concerns, and expectations regarding the system. Patients and Methods: A descriptive qualitative study was conducted in the Department of Neurology at the Second Affiliated Hospital of Fujian Medical University, Fujian, China. Patients with poststroke hemiplegia and primary caregivers were recruited using purposive sampling. Semistructured, face-to-face interviews were conducted with 11 patients with poststroke hemiplegia and 13 primary caregivers to capture both patient and caregiver perspectives. Data were collected on participant-reported BADL difficulties, willingness to use the AI-empowered BADL training system, perceived needs and concerns, and expectations for system design. Data were analyzed using inductive thematic analysis. Results: Four themes were identified: 1) BADL limitations created shared difficulties for patients and caregivers; 2) acceptance of the AI-empowered BADL training system depended on usefulness and functional fit; 3) participants expected low-barrier, understandable, and sustainable training support; and 4) the system was viewed as a supplement to professional rehabilitation and family caregiving. Conclusion: An AI-empowered BADL training system may be acceptable to patients with poststroke hemiplegia and their primary caregivers if it is safe, reliable, easy to use, and appropriate to patients' functional status. These findings may guide the future development of task-oriented AI-empowered BADL training systems that support patient-caregiver collaboration, safety assurance, sustained engagement, and human-AI collaboration in rehabilitation.

Indexed as

activities of daily livingartificial intelligencecaregivershemiplegiaqualitative researchstroke

Identifiers

PMID42763734
PMCPMC13589525

What Socratic holds

Textmetadata
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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 Socratic graph.