Integrating Garmin Wearable Data into FHIR-Based Health Systems for Improved Interoperability
Bibliographic record
Abstract
As wearable technologies become more common in everyday life, integrating Patient-Generated Health Data (PGHD) into clinical systems has emerged as a critical area in digital health. This study explores how data such as heart rate, step count, sleep patterns, and activity levels (captured in this study via the Garmin Vívoactive 4 smartwatch) can be brought into FHIR-based healthcare systems through the Fitrockr platform. We explore how these data align with key Fast Healthcare Interoperability Resources (FHIR), such as Observation, Device, and Patient. Additionally, we evaluate the compatibility of collected datasets by the Modular Open Research Environment (MORE) platform with FHIR and examine the feasibility of transferring these records to FHIR servers. This level of semantic interoperability could simplify the integration of PGHD into hospital information systems or other healthcare information systems and especially EHRs, thus enhancing their contribution to care delivery, especially in medical decision making and as a source for Clinical Decision Support Systems (CDSS). The paper also discusses how standards like FHIR, openEHR, and Observational Medical Outcomes Partnership (OMOP) can work together to ensure consistent, meaningful integration of wearable data for both clinical practice and secondary analysis. In summary, we reflect on the importance of real-time wearable data availability, reliability, and privacy in supporting a more personalized, data-driven healthcare experience.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".