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. 2025 Apr 18;83(1):109.
doi: 10.1186/s13690-025-01599-z.

Heterogeneity in willingness to share personal health information: a nationwide cluster analysis of 20,000 adults in Japan

Miho Sassa #  1   2 , Akifumi Eguchi #  3   4 , Keiko Maruyama-Sakurai  3 , Takanori Fujita  3   5 , Yumi Kawamura  3 , Takayuki Kawashima  3   6 , Yuta Tanoue  7 , Daisuke Yoneoka  2 , Hiroaki Miyata  3 , Takanori Yamashita  8 , Naoki Nakashima  9 , Shuhei Nomura  10   11
Affiliations

Heterogeneity in willingness to share personal health information: a nationwide cluster analysis of 20,000 adults in Japan

Miho Sassa et al. Arch Public Health. .

Abstract

Background: While Personal Health Records (PHRs) are increasingly adopted globally, understanding public attitudes toward health information sharing remains crucial for successful implementation. This study investigated patterns in willingness to share personal health information among Japanese adults and identified factors influencing their sharing decisions.

Methods: A nationwide cross-sectional web-based survey was conducted among 20,000 Japanese adults in December 2023. Participants were recruited through quota sampling based on age, gender, and prefecture population ratios from the 2020 National Census. The survey examined willingness to share personal health information with nine types of recipients (healthcare providers, ambulance crew, application providers, family members, local authorities, employers, pharmaceutical companies, government agencies, and research institutions), trust levels in these recipients, and 17 factors influencing sharing decisions across health benefits, convenience, economic incentives, social significance, information details, transparency, and privacy considerations. Clustering analysis using Uniform Manifold Approximation and Projection (UMAP) and Ordering Points to Identify the Clustering Structure (OPTICS) algorithms was performed to identify distinct patterns in sharing preferences.

Results: Despite low PHR familiarity (88.4% unfamiliar), participants showed willingness to share health information with healthcare providers (65.0%) and family members (65.6%), but expressed lower willingness toward government agencies (28.6%) and research institutions (28.8%). Five distinct clusters were identified: family-only sharers (3.9%), mixed preference sharers (47.9%), comprehensive sharers (12.9%), non-sharers (22.1%), and healthcare-selective sharers (13.2%). Trust levels were highest for family members (85.6%) and healthcare professionals (78.8%), while significantly lower for government agencies (44.2%). Higher education, income, and PHR familiarity were associated with greater willingness to share, while privacy and security concerns were universal across all clusters.

Conclusions: The heterogeneous patterns in health information sharing preferences suggest the need for tailored PHR implementation strategies that address varying privacy concerns and trust levels across different population segments. Success in PHR adoption requires balanced approaches to trust-building, robust data protection, and targeted communication strategies that acknowledge diverse user needs while promoting the benefits of health data sharing.

Keywords: Cluster analysis; Digital health; Health information sharing; Japan; Personal health records.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Ethical approval was granted by the Ethics Committees of Keio University School of Medicine under authorization number 20231130. Instead of traditional paper-based written informed consent, electronic informed consent was obtained from the participants. Only upon providing this consent, participants were allowed to proceed to the questionnaire response page. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
a Distribution of five identified clusters of Japanese adults (N = 20,000) detected by Ordering Points To Identify the Clustering Structure (OPTICS) on the two-dimensional reduced representation of personal health information sharing preferences, December 2023; b Uniform Manifold Approximation and Projection (UMAP) visualization of clusters for two-dimensional reduced representation of data annotated by the OPTICS generated clusters from a nationwide Japanese survey
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