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

Stroke 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. This study aimed to develop a patient-informed preference elicitation instrument for a discrete choice experiment evaluating digital rehabilitation technologies in neurorehabilitation. A 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. Patients 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. The study provides a content-valid preference elicitation instrument for a DCE investigating patient preferences for digitally supported stroke rehabilitation.