USER PREFERENCES FOR HEALTH APPLICATIONS: POPULATION-REPRESENTATIVE CONJOINT EXPERIMENTS IN AUSTRIA AND ITALY
Author(s)
Nguyen L. Truong, MSc, Erika Mosor, PhD, Valentin Ritschl, PhD, Tanja Stamm, PhD.
Medical University of Vienna, Vienna, Austria.
Medical University of Vienna, Vienna, Austria.
OBJECTIVES: Although digital health applications have considerable potential to support disease management and promote well-being, adoption rates remain relatively low. In order to support design, implementation, and dissemination of health applications, this study aimed to identify factors influencing individuals' preferences for using them.
METHODS: We conducted a survey with an embedded conjoint experiment among population-representative samples of 3444 Austrians and 2994 Italians in second half of 2025. We tested attributes such as who recommended the App, content, functionality, developer, data usage and cost. Average marginal component effects (AMCE) with 95% confidence intervals (95%CIs) were calculated.
RESULTS: In both Austria and Italy, participants strongly preferred health applications recommended by a healthcare professional rather than a friend (Austria: AMCE=0.466, 95% CI 0.334-0.598; Italy: AMCE=0.423, 95% CI 0.291-0.555), whereas AI-generated recommendations reduced preferences (Austria: AMCE=-0.310, 95% CI -0.441 to -0.179; Italy: AMCE=-0.181, 95% CI -0.313 to -0.049). Compared with peer-to-peer chat functions, Austrian participants preferred applications that enabled booking appointments with healthcare professionals (AMCE=0.321, 95% CI 0.188-0.455) and asking health-related questions (AMCE=0.268, 95% CI 0.137-0.399), while in Italy only appointment booking increased preferences (AMCE=0.169, 95% CI 0.042-0.297). Applications developed by university hospitals were preferred over national and international companies in Austria (AMCE=0.213, 95% CI 0.096-0.330), but not in Italy. Compared with personal data use only, Austrian participants were less likely to prefer applications that used their data to train AI models (AMCE=-0.153, 95% CI -0.282 to -0.023), whereas no such effect was observed in Italy. In both countries, applications covered by health insurance were preferred over self-funded applications.
CONCLUSIONS: Preferences for health applications were primarily driven by trust-related factors, including recommendations from healthcare professionals, health insurance coverage, and, in Austria, development by a university hospital.
METHODS: We conducted a survey with an embedded conjoint experiment among population-representative samples of 3444 Austrians and 2994 Italians in second half of 2025. We tested attributes such as who recommended the App, content, functionality, developer, data usage and cost. Average marginal component effects (AMCE) with 95% confidence intervals (95%CIs) were calculated.
RESULTS: In both Austria and Italy, participants strongly preferred health applications recommended by a healthcare professional rather than a friend (Austria: AMCE=0.466, 95% CI 0.334-0.598; Italy: AMCE=0.423, 95% CI 0.291-0.555), whereas AI-generated recommendations reduced preferences (Austria: AMCE=-0.310, 95% CI -0.441 to -0.179; Italy: AMCE=-0.181, 95% CI -0.313 to -0.049). Compared with peer-to-peer chat functions, Austrian participants preferred applications that enabled booking appointments with healthcare professionals (AMCE=0.321, 95% CI 0.188-0.455) and asking health-related questions (AMCE=0.268, 95% CI 0.137-0.399), while in Italy only appointment booking increased preferences (AMCE=0.169, 95% CI 0.042-0.297). Applications developed by university hospitals were preferred over national and international companies in Austria (AMCE=0.213, 95% CI 0.096-0.330), but not in Italy. Compared with personal data use only, Austrian participants were less likely to prefer applications that used their data to train AI models (AMCE=-0.153, 95% CI -0.282 to -0.023), whereas no such effect was observed in Italy. In both countries, applications covered by health insurance were preferred over self-funded applications.
CONCLUSIONS: Preferences for health applications were primarily driven by trust-related factors, including recommendations from healthcare professionals, health insurance coverage, and, in Austria, development by a university hospital.
Conference/Value in Health Info
2026-11, ISPOR Europe 2026, Vienna, Austria
Value in Health, Volume 29, Issue 12S
Code
P32
Topic
Epidemiology & Public Health, Medical Technologies, Patient-Centered Research
Topic Subcategory
Patient Behavior and Incentives
Disease
No Additional Disease & Conditions/Specialized Treatment Areas