Abstract

Objectives

Increasing demand, rising costs, and limited access pose major challenges for stroke rehabilitation. Digital interventions offer solutions, although acceptance varies. Understanding heterogeneity in preferences and decision consistency is essential.

Methods

A discrete choice experiment (N = 1055 after data cleaning) assessed preferences for 7 attributes. Latent class analysis identified preference-based subgroups; a heteroscedastic conditional logit model examined scale heterogeneity.

Results

Three classes were identified. Class 1 (21.3%) prioritized low copayment (β = −3.355; β = 3.058; P .001) corresponded to increased variability.

Conclusion

Tailored interventions should reflect distinct preference types to foster acceptance. Cost-sensitive users may need simple formats; digitally engaged individuals benefit from flexible solutions; and those with inconsistent decisions may require clearer guidance or structured onboarding. Conducted in stroke rehabilitation, the study also captured broader rehabilitation experience.