Trust, perceived accuracy, and continued use of patient-facing symptom monitoring applications: Cross-sectional study

Patient-facing symptom monitoring applications are increasingly used in digital health to support preliminary symptom assessment and health-related decision-making. While perceived accuracy and trust are recognized as important determinants of mHealth adoption, their joint role in shaping users' continued engagement with symptom monitoring applications remains insufficiently understood.

This study examined the relationships among perceived accuracy, trust, and continued use intention of symptom monitoring applications and assessed whether trust mediates the association between perceived accuracy and continued use intention.

A cross-sectional survey was conducted among 300 adult outpatients recruited from three affiliated hospitals in China. Participants evaluated a debranded symptom monitoring application scenario and completed validated measures of perceived accuracy, trust, continued use intention, and eHealth literacy. Multiple linear regression analyses were performed with adjustment for demographic and health-related covariates. Mediation analysis with bootstrap resampling was used to estimate indirect effects.

Perceived accuracy was positively associated with continued use intention (B=0.52, p<.001) and trust (B=0.48, p<.001). Trust was strongly associated with continued use intention (B=0.47, p<.001) and showed a larger effect size than perceived accuracy in the full model. Mediation analysis indicated that trust partially mediated the relationship between perceived accuracy and continued use intention. Findings were consistent across sensitivity and subgroup analyses.

Perceived accuracy and trust jointly influence continued use intention of symptom monitoring applications, with trust acting as a key mechanism linking performance perceptions to sustained engagement. Promoting trust alongside technical performance may support more sustainable use of patient-facing digital health technologies.