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Journal of Medical Internet Research
Article . 2025 . Peer-reviewed
Data sources: Crossref
https://doi.org/10.2196/prepri...
Article . 2025 . Peer-reviewed
Data sources: Crossref
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Digital Literacy and Interpersonal Trust as Predictors of Willingness to Share Patient-Generated Health Data Among Korean Internet Users: A Cross-sectional Study Using Privacy Calculus and Communication Privacy Management Theories (Preprint)

Authors: dongsu lee; Wonseuk Jang;

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

Abstract

BACKGROUND The proliferation of wearable devices and advances in data analytics are accelerating the adoption of personalized digital healthcare. Patient-Generated Health Data (PGHD), created and recorded directly by individuals, plays a critical role in this transformation. However, willingness to share such sensitive data remains limited due to privacy concerns, perceived risks, and uncertainty about data use. While previous studies have examined factors influencing health data sharing, most focused on specific patient populations and lacked a comprehensive analysis of psychological and social determinants among the general public. OBJECTIVE This study aimed to examine factors influencing individuals' willingness to share health data by applying Privacy Calculus and Communication Privacy Management (CPM) theories, with a particular focus on the role of digital literacy and interpersonal trust. METHODS We conducted a cross-sectional analysis using data from the 2023 Intelligent Information Society User Panel Survey, a nationally representative sample of 4,518 Internet users in Korea aged 15–69. Key variables included willingness to share health data (dependent), perceived risk and benefit, digital literacy (use, understanding, engagement), interpersonal trust, and control variables. Digital literacy was measured using a media literacy framework and modeled as a latent construct in structural equation modeling (SEM). SEM was performed using the lavaan and lavaan.survey packages in R with WLSMV estimation and population weights. Hypothesis 9 was tested via Wald tests to assess differential effects of digital literacy subcomponents, and mediation effects were also estimated. RESULTS Of the 4,518 respondents (weighted N = 38.4 million), 55.8% were female, and the largest age group was aged ≤20. The average willingness to share health data was 2.73 on a 5-point scale. SEM revealed that perceived risk negatively affected willingness to share (β = –0.045, P = .049), while perceived benefit (β = 0.046, P = .024), interpersonal trust (β = 0.073, P < .001), and moral motivation (β = 0.309, P < .001) had significant positive effects. Digital literacy showed no significant direct effect (β = –0.001, P = .945), but had a positive indirect effect via perceived benefit (β = 0.035, P = .038). Interpersonal trust also indirectly increased willingness to share by reducing perceived risk (β = 0.045, P < .001). Wald tests indicated significant differences among digital literacy subcomponents in their indirect effects via perceived risk (χ² = 54.496, P < .001); “understanding” had the strongest indirect effect. No subcomponent had a significant total effect. CONCLUSIONS Willingness to share health data is influenced by a combination of perceived risk, benefit, moral motivation, digital literacy, and interpersonal trust. Digital literacy of understanding and interpersonal trust emerged as key drivers through indirect paths. These findings highlight the importance of trust-based governance, context-sensitive consent, and digital literacy education. Future research should incorporate health-specific literacy tools and examine how contextual factors, such as data sensitivity and purpose of use, influence health data sharing decisions.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average