OBJECTIVES: Strokes present a challenge to the healthcare system, as the absolute number of strokes has increased despite a decline in stroke-related mortality. The demand for neurorehabilitation is growing, necessitating innovative solutions due to a shortage of professionals. The utilization of digital technologies offers the potential for increased efficiency and effectiveness. However, patient acceptance plays a crucial role in the successful adoption. Therefore, this study aimed to systematically analyze patients acceptance for digital technologies in neurorehabilitation.

METHODS: A discrete choice experiment (DCE) was conducted, involving a total of 1259 respondents. The DCE encompassed five technical aspects: explanation and presentation of therapy exercise, information in therapy, contact to healthcare professionals, patients’ choice in therapy process, and data processing. Additionally, therapy success within 6 months and a cost-attribute copayment per month were added. A fractional-factorial efficient Bayesian design (D-error) was used. The collected data were analyzed using a mixed logit model, and willingness-to-pay and uptake probabilities were calculated.

RESULTS: The analysis revealed that therapy success within 6 months was the most influential criterion, followed by the monthly copayment cost. However, the results also indicated that technical aspects, in addition to clinical and cost factors, significantly influenced the probability of uptake. Three alternative digital technologies were examined, assuming constant therapy success: a prototype of a humanoid robot, further development of a robot, and a digital application. Uptake probabilities were calculated based on willingness-to-pay results, and significant differences in uptake probabilities were observed among the alternatives (44% vs. 65% vs. 84%).

CONCLUSIONS: This study demonstrates a systematic approach to understanding the acceptance of healthcare interventions, particularly in the context of digital technologies for neurorehabilitation. The findings highlight the significant impact of changes in technical characteristics on the acceptance of these technologies. By incorporating this information into healthcare decision-making processes, the development, implementation, and patient engagement can be enhanced.