Data Donation in University Student Samples: A Predictive Study of Public Health Apps

Abstract

The rapid evolution of mobile health applications has generated an unprecedented volume of person-generated health data, offering significant potential for public health surveillance and epidemiological research. However, leveraging this data requires active participation from users through data donation. This study investigates the factors influencing data donation behaviors among university students using a custom-built, secure registry system. Drawing on a cohort of eight hundred and forty university students, we integrated self-reported psychometric survey data with objective system logs tracking actual data donation decisions. Our findings indicate a profound divergence between stated behavioral intentions and actual donation actions, illustrating the privacy paradox in a digital health context. Logistic regression models reveal that institutional trust and altruistic tendencies are strong positive predictors of objective data donation, whereas perceived privacy risks act as a substantial barrier. Conversely, digital health literacy and app usage frequency showed marginal significance in predicting actual donation behavior. By demonstrating the efficacy of registry analysis to monitor real-time, behavioral outcomes, this paper contributes a novel methodological framework to digital health research. The results highlight the critical necessity of establishing transparent data governance protocols and user-centric registry interfaces to foster public trust and cultivate sustainable digital donation ecosystems for public health advancements.