Your members are already generating health data. Now you can use it.
The trust layer that turns consumer wearables, connected devices, and home monitoring into clinically-valid, governance-ready insight.
Every day, millions of people generate health data — heart rate from their Apple Watch, sleep from their Oura ring, blood pressure from their Bluetooth monitor, glucose from their CGM. This data could transform chronic disease management, risk stratification, and early intervention.
But it sits unused. Because consumer devices aren't medical devices. Because data quality is inconsistent. Because governance is unclear. Because when regulators ask how you're using health data for clinical or commercial decisions, "we pulled it from Apple Health" isn't an answer.
SteadyTrace changes that. It's the trust layer between the device on someone's wrist and the clinical decision that affects their care.
The data exists. The trust doesn't.
The Quality Gap
Consumer devices optimise for battery life and user experience, not clinical accuracy. Readings vary by skin tone, fit, motion, temperature. Some data points are estimates, not measurements. You can't tell which is which.
The Governance Gap
Where did this data come from? When was it captured? Did the patient consent to this use? Can you prove it? When the FCA, ICO, or CQC asks, you need answers — not assumptions.
The Context Gap
A heart rate of 95 bpm means nothing on its own. Is the person exercising? Sleeping? Anxious? Unwell? Without clinical context and protocol, raw data is just noise.
The Integration Gap
Apple Health doesn't talk to Google Fit. Garmin doesn't talk to Withings. Your clinical systems speak FHIR. Someone has to bridge these worlds — securely, at scale, without custom integration for every device.
The Liability Gap
When you act on device data — or fail to act — who's responsible? If the reading was wrong, if the alert didn't fire, if the patient didn't comply... the liability has to sit somewhere. Right now, it's unclear.
From raw signal to trusted clinical data
SteadyTrace is a four-stage pipeline that transforms device data into clinically-usable, governance-ready observations.
Universal Device Ingestion
Any device. Any ecosystem. One integration.
| Category | Devices Supported |
|---|---|
| Smartphone Health Platforms | Apple Health, Google Fit, Samsung Health |
| Fitness Wearables | Fitbit, Garmin, Whoop, Oura, Polar, Amazfit |
| Smart Watches | Apple Watch, Samsung Galaxy Watch, Wear OS devices |
| BLE Home Devices | Withings (scales, BP monitors), Omron, iHealth, Beurer, Kinetik |
| Continuous Glucose Monitors | Abbott Libre, Dexcom, Medtronic Guardian |
| Cardiac Monitors | AliveCor Kardia, Biobeat, Frontier X |
| Pulse Oximeters | Masimo, Nonin, Wellue, consumer devices |
| Sleep Trackers | Withings Sleep, Eight Sleep, dedicated sleep devices |
| Medical-Grade RPM | Current Health, Biofourmis, Huma, Luscii |
| Custom Integrations | Any device via FHIR, HL7, or REST API |
One connection to SteadyTrace. Access to the entire ecosystem.
Quality Validation
Trust the data before you act on it.
Every data point passes through validation before it reaches your systems:
Physiological Plausibility
Heart rate of 250 bpm? Filtered. Blood pressure of 40/20? Flagged. Weight change of 10kg overnight? Questioned.
Sampling Integrity
Was the device worn continuously or intermittently? Are there gaps that invalidate the trend? Is the sampling frequency sufficient for clinical use?
Device Fingerprinting
Which device generated this reading? What's its known accuracy profile? Is it consumer-grade, clinical-grade, or unknown?
Completeness Assessment
Does this dataset meet minimum thresholds for the intended use? Are there enough readings to calculate a meaningful trend? What's the confidence level on this data?
Output: Every data point carries a quality score and validation metadata.
Clinical Contextualisation
Raw data becomes meaningful observation.
Data without context is noise. SteadyTrace adds the clinical layer:
Protocol-Driven Collection
What data should be collected? How often? For how long? Under what conditions? Clinicians define the protocol. SteadyTrace enforces it.
Threshold & Alert Configuration
What's normal for this patient? What triggers a flag? What triggers an escalation? Who gets notified? Clinical judgement sets the rules. The system executes them.
Observation Mapping: Raw accelerometer data → Activity level. Optical sensor data → Heart rate / HRV. Impedance data → Body composition estimate. Multi-signal fusion → Sleep staging.
Structured to UK Core FHIR R4 Observation resources.
Governance & Provenance
Evidence that stands up to scrutiny.
Every data point in SteadyTrace carries:
Provenance:
- Source device (make, model, firmware)
- Capture timestamp (device clock and server clock)
- Transmission pathway (how it reached SteadyTrace)
- Processing history (what validation was applied)
Consent Linkage:
- Which consent authorised this collection?
- What purpose was specified?
- When does the consent expire?
- Has it been withdrawn?
See Patient Consent Architecture →
Audit Trail:
- Who accessed this data?
- When?
- For what purpose?
- What decisions were informed by it?
Regulatory Alignment:
- ALCOA+ compliant (Attributable, Legible, Contemporaneous, Original, Accurate)
- FCA-ready for insurers using health data commercially
- ICO-defensible for GDPR data subject requests
- CQC-aligned for clinical governance
What trusted device data enables
Chronic Disease Management
Scenario: An insurer wants to offer a diabetes management programme using CGM data from members' own Libre devices, combined with activity data from their smartphones.
Challenge: CGM readings vary by sensor placement, calibration, and skin type. Activity data from phones is inconsistent. The insurer needs to prove clinical validity and governance for any programme that influences care or costs.
How SteadyTrace helps:
- Ingests CGM data via LibreLink and activity via Apple Health / Google Fit
- Validates glucose readings against physiological norms
- Correlates glucose trends with activity patterns
- Enables clinician-defined alert thresholds (e.g., time-in-range below 70%)
- Generates evidence packages showing data quality and consent for each member
Outcome: A scalable diabetes programme using devices members already own — with the governance to satisfy regulators and the clinical validity to improve outcomes.
Cardiac Rehabilitation
Scenario: A private hospital offers post-discharge cardiac rehab with remote monitoring. Patients use their own Apple Watch or Fitbit to track heart rate, HRV, and activity during recovery.
Challenge: Consumer devices weren't designed for clinical cardiac monitoring. Data quality varies. The hospital needs to know which readings to trust and which to ignore.
How SteadyTrace helps:
- Validates resting heart rate readings (filters motion artefacts, incomplete measurements)
- Tracks HRV trends with confidence scoring
- Monitors activity levels against rehab protocol targets
- Alerts clinical team to concerning patterns (rising RHR, falling HRV, activity decline)
- Documents the monitoring protocol and every clinical decision informed by device data
Outcome: Earlier detection of post-discharge complications. Fewer unplanned readmissions. Better patient experience. Defensible clinical governance.
Risk Stratification for Underwriting
Scenario: A life insurer wants to use wearable data for more accurate risk stratification — resting heart rate, activity levels, sleep quality — to offer better pricing to healthier applicants.
Challenge: The FCA will scrutinise any use of health data for pricing. The insurer needs to prove the data is valid, the methodology is sound, and the consent is explicit.
How SteadyTrace helps:
- Ingests 30/60/90 days of wearable data from applicant devices
- Validates data quality and flags incomplete or unreliable datasets
- Calculates clinically-meaningful metrics (not just step counts)
- Generates evidence packages documenting data provenance, quality, and consent
- Provides audit trail for every underwriting decision informed by device data
Outcome: Defensible dynamic underwriting using real health behaviours — with the governance trail to satisfy regulators.
Post-Operative Recovery Monitoring
Scenario: A private clinic wants to monitor patients recovering from joint replacement surgery. Patients use a Withings scale and blood pressure monitor at home, plus their smartphone for activity tracking.
Challenge: Multiple devices, multiple data streams, multiple failure modes. The clinic needs a single, trusted view of recovery progress — and clear escalation when something's wrong.
How SteadyTrace helps:
- Ingests weight, blood pressure, and activity from patient devices
- Monitors for warning signs: weight gain (fluid retention), BP spikes, activity decline
- Protocol-driven: expected recovery trajectory defined by surgeon
- Alerts to clinic when patient deviates from expected pathway
- Documents the entire monitoring episode for clinical record
Outcome: Complications caught before they become emergencies. Patients feel supported. Clinicians have visibility without manual check-ins.
Wellness Programme Validation
Scenario: A corporate insurer runs a wellness programme for employer clients. They want to prove ROI — that the programme actually improves employee health metrics.
Challenge: Step-count gamification isn't clinically meaningful. The insurer needs real health metrics, aggregated appropriately, with privacy protection.
How SteadyTrace helps:
- Ingests wearable data from participating employees (with explicit consent)
- Validates data quality before aggregation
- Calculates population-level metrics: average RHR trends, activity levels, sleep quality
- Anonymises and aggregates while maintaining statistical validity
- Generates outcomes reports showing programme impact over time
Outcome: Proof that the wellness programme works — or evidence to redesign it if it doesn't.
Connects to your existing systems
Output formats:
- UK Core FHIR R4 Observation resources
- FHIR Device resources with provenance
- FHIR Consent resources linked to each data flow
- REST APIs for custom integration
- Webhooks for real-time alerting
Connects to:
- Clinical systems (EPRs, care management platforms)
- Insurer platforms (claims, underwriting, member engagement)
- Analytics and population health tools
- Your existing data warehouse
No proprietary lock-in. Your data stays portable. SteadyTrace is infrastructure, not a platform.
Built on trusted foundations
Ready to unlock your device data?
Whether you're an insurer exploring wearable data for risk stratification, a digital health provider scaling remote monitoring, or a private hospital implementing post-discharge programmes — SteadyTrace is the trust layer that makes it possible.
Book a Technical Discovery Call