Digital Literacy and Interpersonal Trust as Predictors of Willingness to Share Patient-Generated Health Data Among Korean Internet Users: Cross-Sectional Study Using Privacy Calculus and Communication Privacy Management Theories

Clinical Snapshot

Key Findings

• P Value: Perceived risk: P<0.001; Perceived benefit: P=0.009

• Effect Size: Perceived risk: standardized β = -0.189; Perceived benefit: standardized β = 0.076

• Primary Outcome: Willingness to share patient-generated health data

• Nnt Or Sensitivity: Digital literacy understanding dimension indirectly promoted sharing through reduced perceived risk

• Confidence Interval: Not reported in abstract

Clinical Application

Findings applicable to digital health implementation strategies and patient engagement programs Relevant to My Health Record adoption and digital health initiatives; cultural differences in privacy attitudes may limit direct application Healthcare consumers considering digital health platforms and wearable device data sharing

Abstract

BACKGROUND: The proliferation of wearable devices and advances in data analytics are accelerating the adoption of personalized digital health care, relying heavily on patient-generated health data (PGHD). However, the sensitive nature of this data creates significant privacy boundaries. While previous research has focused on rational cost-benefit trade-offs, there is a limited understanding of how social and cognitive factors-specifically interpersonal trust and digital literacy (DL)-shape the data-sharing decisions of the general public. OBJECTIVE: This study aims to identify the factors predicting individuals' willingness to share health data (WS) by integrating privacy calculus and communication privacy management theories. It specifically examines the comparative influence of DL, interpersonal trust, and moral motivation on data-sharing decisions. METHODS: We analyzed data from the 2023 Korea Panel Survey on the Digital Society (n=4518), a nationwide representative sample of internet users. Survey-weighted structural equation modeling with weighted least squares mean and variance adjusted estimation was used to examine the relationships among perceived risk (PR), perceived benefit (PB), DL, interpersonal trust, and WS. RESULTS: PR was the strongest negative predictor of WS (standardized coefficient [std β]=-0.189, P<.001), whereas PB was the strongest positive predictor (std β=0.076, P=.009), followed by moral motivation (std β=0.062, P=.03) and interpersonal trust. DL showed a significant negative direct association with willingness to share (std β = -0.060, P<.001). However, subdimension analysis revealed heterogeneous mechanisms: the "understanding" dimension was associated with lower PR and indirectly promoted sharing, whereas use and engagement were associated with higher PR. Age-stratified analyses suggested potential heterogeneity in these relationships, although the overall interaction was not statistically significant. CONCLUSIONS: PBs and risks were the strongest determinants of PGHD sharing, with benefits increasing and risks decreasing willingness to share. Beyond this risk-benefit balance, DL (particularly understanding) and interpersonal trust also played important roles, highlighting the need for trust-based and user-centric strategies to promote PGHD sharing and support the expansion of digital health care.

References

1. 1.Lee, D., & Jang, W. (2026). Digital literacy and interpersonal trust as predictors of willingness to share patient-generated health data among Korean internet users: Cross-sectional study using privacy calculus and communication privacy management theories. Journal of Medical Internet Research. https://doi.org/10.2196/75448