Supporting dataset for manuscript "Prioritization of Age-Friendly Health and Fitness App Features"

This study integrated a systematic review (Study 1; <i>n</i>=92 articles) and an incentive-aligned Maximum-Difference experiment with direct anchoring (Study 2; <i>n</i>=913 older adults) to prioritize age-friendly health and fitness app features.

We estimated feature priorities using hierarchical Bayes modeling, and examined user heterogeneity via k‑means clustering. Twelve core age-friendly features were identified and classified into five categories, with prior usage frequency ranging from 9 to 43 (Study 1). Study 2, however, revealed moderate congruence with Study 1 and a clear preference hierarchy, with <i>remote health and activity monitoring</i>ranked highest.