{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T11:24:48Z","timestamp":1778066688835,"version":"3.51.4"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T00:00:00Z","timestamp":1759708800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Patient-Generated Health Data (PGHD) collected through wearable devices such as smartwatches offers new opportunities for personalized care, chronic disease management, and preventive health. Despite this potential, technical, regulatory, and interoperability challenges still limit the integration of PGHD into healthcare systems, especially in relation to standards such as Fast Healthcare Interoperability Resources (FHIR) and the European Health Data Space (EHDS).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This study used the Garmin V\u00edvoactive 4 smartwatch to collect PGHD and integrate it into a FHIR server via the Fitrockr hub and API. The Kodjin FHIR server was deployed to enable standardized data storage and transfer. In parallel, data from the Modular Open Research Platform (MORE) were examined for compatibility with FHIR resources. The process included device enrolment, data collection, mapping to FHIR specifications, and evaluation of compliance with General Data Protection Regulation (GDPR) requirements.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The prototype demonstrated that data from Garmin devices could be securely collected, mapped, and transferred into a FHIR environment. Integration through the Fitrockr hub ensured structured data formatting and reliability. The analysis of PGHD from the MORE platform confirmed that heterogeneous data types, including physiological measures and survey responses, could be represented with appropriate FHIR resources. These findings highlight the technical feasibility and scalability of PGHD integration.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>The results confirm that PGHD from wearable devices can be standardized and transferred into healthcare systems in compliance with international standards and European regulations. This approach contributes to bridging the gap between personal health data and medical decision-making, supporting the objectives of the EHDS and enabling further use of PGHD in research and innovation.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fdgth.2025.1636775","type":"journal-article","created":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T12:28:00Z","timestamp":1759753680000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Streamlining wearable data integration for EHDS: a case study on advancing healthcare interoperability using Garmin devices and 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