Evidence map›Paper›PMID 42814379›Full record

ArticleThe patient2026

Identifying Decision-Relevant Attributes of Digital Technologies in Neurorehabilitation: Qualitative Interviews with Stroke Patients and Experts to Inform a Discrete Choice Experiment.

Ann-Kathrin Fischer, Axel Mühlbacher

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

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Ann-Kathrin FischerHealth Economics and Health Care Management, Hochschule Neubrandenburg, Neubrandenburg, Germany. akfischer@hs-nb.de.ORCID http://orcid.org/0000-0001-9306-9020
Axel MühlbacherHealth Economics and Health Care Management, Hochschule Neubrandenburg, Neubrandenburg, Germany.ORCID http://orcid.org/0000-0003-4402-9211

Funding

Europäischer Sozialfonds ESF/14-BM-A55-0001/19-A02
6 · The paper itself

Abstract

backgroundStroke is a leading cause of long-term disability and requires sustained rehabilitation to support functional recovery. Digital health interventions offer new opportunities to complement conventional rehabilitation services. However, understanding how patients evaluate and value digital technologies is essential for their successful implementation.

objectiveThis study aimed to develop a patient-informed preference elicitation instrument for a discrete choice experiment evaluating digital rehabilitation technologies in neurorehabilitation.

methodsA multistage qualitative approach was applied. First, a targeted literature review identified 17 preliminary attributes describing technological and contextual characteristics of digital neurorehabilitation. Second, semi-structured interviews with stroke patients (n = 14) and neurorehabilitation experts (n = 5) were conducted to evaluate and prioritize these attributes. Interview transcripts were analyzed using directed content analysis combining deductive and inductive coding. Overlapping attributes were consolidated through iterative comparison and conceptual clustering.

resultsPatients described structural limitations in conventional rehabilitation, including therapy interruptions and limited resources, which may lead to uncertainty, demotivation, and loss of progress. Digital technologies were perceived as complementary tools that could support more continuous and individualized rehabilitation. The initial 17 attributes were consolidated into five technology-related attributes: explanation and presentation of therapy exercises, information in therapy, contact with healthcare professionals, patients' choice in the therapy process, and data processing. Two additional attributes reflecting clinical and economic considerations, Therapy success within 6 months and copayment per month, were included to reflect realistic decision contexts.

conclusionsThe study provides a content-valid preference elicitation instrument for a DCE investigating patient preferences for digitally supported stroke rehabilitation.

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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 Socratic graph.