Understanding Preference Heterogeneity in Digital Neurorehabilitation: Results of a Discrete Choice Experiment

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: = 3.909; P < .001) and higher digital affinity. Class 3 (46.5%) valued professional contact (β = 0.941; P < .001), location flexibility (β = 0.632; P < .001), and progress feedback (β = 0.558; P < .001), reflecting differentiated engagement preferences. The heteroscedastic conditional logit model showed that satisfaction with health (γ = 0.114; P < .001) and quality of life (γ = 0.043; P < .001) were linked to greater consistency, whereas (very) good health status (γ = -0.177; P < .001) and therapy experience (γ = -0.014; 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.