ArticleThe European journal of health economics : HEPAC : health economics in prevention and care2026
Unraveling acceptance of healthcare innovations in neurorehabilitation: results from a discrete choice experiment.
Article in The European journal of health economics : HEPAC : health economics in prevention and care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.
What it found
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Who cites it
1 citing paper in PubMed.
- Patient Priorities for Coverage of New Antihypertensive Drugs: A Discrete Choice Experiment in China.Patient preference and adherence · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aimed to quantify patient preferences and assess how therapy success, copayment, and specific technical aspects influence acceptance of digital neurorehabilitation technologies.
methodsA Discrete Choice Experiment (DCE) was conducted among 1,259 individuals, including stroke survivors and members of the general population, to evaluate preferences for seven attributes of digital neurorehabilitation technologies: therapy success (within 6 months), monthly copayment, and five technical aspects (e.g., contact with professionals, therapy location, information provision, explanation format, and data processing). A fractional factorial design was applied, and data were analyzed using a Mixed Logit Model. Willingness to Pay (WTP) and predicted uptake probabilities (UP) were calculated based on individual preferences.
resultsTherapy success had the greatest influence on decision-making (β₁₀₀% = 1.47; p < .001), followed by monthly copayment (β₀€ = 0.86; p < .001). Relevant technical aspects included direct contact with professionals (β_direct = 0.54; p < .001) and choice of therapy location (β_place = 0.33; p < .001). Multimedia-based explanations were preferred over text (β_video = 0.26; β_move = 0.13; both p < .001). Despite equal therapy success, UP varied substantially across modeled interventions (44%, 65%, and 84%) due to differences in technical aspects alone, as calculated from estimated WTP values.
conclusionAcceptance of digital neurorehabilitation technologies can be systematically assessed using stated preference methods. The findings demonstrate that specific technical aspects can decisively influence acceptance, even when therapy success remains unchanged. This underscores the importance of incorporating patient-valued features into digital health design and provides actionable insights for policy makers and payers aiming to support effective and accepted digital care models.
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Registered trials
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