Harmonization layer
Vendor-agnostic
Apple, Fitbit, Garmin, Withings, Oura, Polar, ActiGraph
Latency
Real-time
Normalized as the signal arrives
Output
Endpoint-ready
Aligned to protocol windows and study logic
Harmonization Workflow
Ingest → Normalize → QC → Endpoint
In clinical trials, signal harmonization is the layer that transforms raw wearable telemetry into a clinically consistent dataset. It is what allows a study to treat heart-rate data from a Garmin and an Apple Watch as the same measurement type — without losing the device-specific context that affects how that measurement should be interpreted.
Harmonization is what turns multi-vendor wearable data into a single analyzable stream — without erasing the provenance regulators expect to see.
Most trials don't notice harmonization is broken until analysis. By then, the dataset already has problems that can't be undone — only documented.
Beats per minute, milliseconds, percent, and steps from different vendors look the same column but mean different things.
Devices and apps use different clocks. Without time normalization, comparing across devices produces ghost trends.
One device emits a sample per minute, another per second. Without harmonization, summaries become apples-to-oranges.
Vendor flags for motion artifact, low confidence, or sensor disconnect get dropped during ingestion.
Algorithm updates from a vendor change how a metric is computed — silently, mid-study, without versioning in the data.
Harmonized output that doesn't match how the protocol defines a valid day or valid window forces post-hoc analysis.
Harmonization isn't a one-time ETL job. It's an operational layer that has to be versioned and monitored over the life of the study.
Harmonization is what makes a multi-vendor wearable trial behave like a single coherent dataset.
Harmonization that survives a multi-year, multi-device trial is built around the lifecycle of the signal — not just the day-one connection.
A strong harmonization layer means the same patient signal can be interpreted the same way from day one through the last visit — across whatever devices the protocol allows.
Digital endpoints can only be trusted when the underlying signals are comparable across patients, devices, and time. Delve builds harmonization around endpoint validity — not just data transport.
Harmonized outputs map to the study's own definitions of valid measurements, windows, and endpoints.
Two patients on two different wearables can still be analyzed against the same digital endpoint.
Every endpoint is tied to a specific algorithm version, so results stay reproducible.
Downstream dashboards and analyses know which datapoints carried vendor-flagged QC issues.
When a harmonized signal drifts or stops, the operations team sees it within hours — not at next interim analysis.
Harmonization documentation supports the lineage and provenance regulators expect for digital endpoints.
Harmonization is one of the core infrastructure layers behind scalable, defensible digital endpoint execution.
No. An API gives you the raw stream. Harmonization is the additional layer that aligns units, time, sampling, QC, and endpoint definitions so the data is comparable across vendors and patients.
Yes. The model is vendor-agnostic and supports the major wearable platforms used in clinical research. New device types are added through the same harmonization pipeline.
Harmonized outputs preserve provenance — device, firmware, algorithm version, QC flags — so the dataset is defensible in a regulatory review and can be re-analyzed if needed.
Delve combines real-time harmonization, signal QC, analytics, and participant support into one operational model designed to protect digital endpoint integrity across devices and time.
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