Evidence map›Paper›PMID 40931352›Full record

ArticleBMC nursing2025

Designing an AI companion to support informal caregivers in role transition: insights from a design science approach.

David Walter, Jennifer Pengel, Paul-Ferdinand Steuck, Marco Di Maria, Ralf Knackstedt, Anne Meissner

Abstract read
In one paragraph

Article in BMC nursing, 2025. 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

6 authors.

David WalterInstitute of Business Administration and Business Informatics, Information Systems and Enterprise Modeling, University of Hildesheim, Hildesheim, Germany. walter002@uni-hildesheim.de.
Jennifer PengelInstitute of Business Administration and Business Informatics, IT for the Caring Society, University of Hildesheim, Hildesheim, Germany.
Paul-Ferdinand SteuckInstitute of Business Administration and Business Informatics, Information Systems and Enterprise Modeling, University of Hildesheim, Hildesheim, Germany.
Marco Di MariaInstitute of Business Administration and Business Informatics, Information Systems and Enterprise Modeling, University of Hildesheim, Hildesheim, Germany.
Ralf KnackstedtInstitute of Business Administration and Business Informatics, Information Systems and Enterprise Modeling, University of Hildesheim, Hildesheim, Germany.
Anne MeissnerInstitute of Business Administration and Business Informatics, IT for the Caring Society, University of Hildesheim, Hildesheim, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAs populations age, informal caregivers play an increasingly vital role in long-term care, with 80% of care provided by family members in Europe. However, many individuals do not immediately recognize themselves as caregivers, especially in the early stages. This lack of awareness can increase physical and emotional stress and delay access to support services. The phenomenon of hidden care, where substantial care is provided without formally acknowledging the role, further exacerbates these issues. To address this, we developed an AI-driven chatbot designed to support informal caregivers recognize their role, reflect on their situation, and identify relevant support options. This paper explores how an AI-based chatbot can be designed to support informal caregivers in reflecting on and re-evaluating their caregiving roles.

methodsFollowing a design science research approach, we evaluate the chatbot design via focused semistructured interviews and think-aloud sessions with informal caregivers to assess its utility, completeness and potential for supporting role transitions through the lens of unlearning. The data were analyzed via Braun and Clarke's thematic analysis.

resultsThe chatbot has the potential to support caregivers in recognizing their role and reflecting on their experiences, with participants reporting increased self-awareness triggered by reflective prompts and recommendations of useful personalized support resources. Seven initial design principles for AI-based chatbot development in transitional informal care contexts were identified. These principles emphasize personalized assessment, transparent information, role awareness support, accessibility, and continuous companionship.

conclusionsThis study demonstrates the potential of AI-driven chatbots to support informal caregivers during critical role transitions. Future research should build on these insights to design context-aware solutions that responsibly embed AI into caregiving realities. CLINICAL TRIAL: No clinical trial.

Indexed as

Artificial intelligenceChatbotDesign science researchGenerative AIInformal careRole awareness

Identifiers

PMID40931352
PMCPMC12424210

What OpenQuestion holds

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LicenceCC BY
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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.