Endpoints and Algorithms: How Firmware Can Break Your Study (Part 4/8)
Why Endpoint Credibility Now Depends on Digital Traceability
Introduction
Endpoints are the lifeblood of clinical evidence. They define what a study measures, how success is determined, and ultimately, whether a therapy works.
But in the age of Digital Health Technologies (DHTs)—wearables, apps, sensors, and connected analytics—the concept of an endpoint has evolved beyond human observation or site-based measurement. Now, endpoints are generated by devices, transmitted through cloud systems, and interpreted by algorithms.
The result?
A new era of invisible risk—where a single firmware update, algorithm adjustment, or data-sync failure can invalidate months of trial data.
This fourth article in the Digital Health Under Scrutiny series explores how the FDA’s 2023–2024 DHT guidances redefined endpoint accountability and why algorithmic transparency, data traceability, and version control are now make-or-break factors in regulatory credibility.
1 The New Endpoint Paradigm
In its Digital Health Technologies for Remote Data Acquisition in Clinical Investigations guidance (2023), the FDA distinguishes between traditional endpoints and DHT-derived endpoints.
The latter are not simply “measured” — they are computed through multi-step digital systems.
A DHT-derived endpoint might be:
Average daily step count derived from accelerometer data.
Heart-rate variability calculated from optical signals.
Sleep quality inferred from combined motion and heart-rate models.
Each involves signal acquisition, data transformation, and algorithmic interpretation.
That complexity means each component—hardware, firmware, software, and analytic logic—must be validated, documented, and version-controlled.
Otherwise, your endpoint may measure not physiology, but configuration drift.
2 Firmware: The Invisible Threat to Endpoint Integrity
The FDA has quietly—and repeatedly—flagged firmware management as a critical weakness in decentralized trials.
Unlike hardware, firmware can change mid-study without physical replacement.
Common triggers include:
Manufacturer updates pushed remotely.
Device reconfigurations for connectivity or power optimization.
Bug fixes or algorithm recalibration.
Each change can alter sampling frequency, signal processing, or even data rounding.
In one recent cardiovascular study, a consumer-grade heart monitor updated its firmware during the trial, changing sampling from 100Hz to 80Hz. The result: non-comparable datasets, endpoint recalculation, and six months of statistical remediation.
Regulators don’t consider this a technical glitch—it’s a validation failure.
3 Algorithmic Transparency: The New Audit Trail
Modern DHTs rely on algorithms and AI models to transform raw sensor data into meaningful endpoints. Yet, these algorithms often operate as “black boxes” controlled by vendors.
The FDA’s Framework for the Use of DHTs in Drug and Biological Product Development (2023) demands transparency in how digital endpoints are derived.
Sponsors must document:
Algorithm design, logic, and intended function.
Version and release control.
Training data sources and validation datasets.
Performance metrics, error rates, and confidence intervals.
Without these, endpoint reliability cannot be substantiated.
The agency has already warned that algorithmic opacity can lead to endpoint rejection, even if the raw data were valid.
4 Endpoint Drift and “Silent” Non-Equivalence
When firmware or algorithm updates change how a metric is calculated, it creates endpoint drift—where data before and after the update are statistically non-equivalent.
This can happen without visible errors:
Signal filters change.
Step-count thresholds are recalibrated.
Sleep algorithms redefine REM cycles
In such cases, the endpoint appears stable, but it’s no longer measuring the same phenomenon.
A 2024 EFPIA review found that over 20% of DHT-enabled studies faced endpoint variability due to untracked device or algorithm changes.
The regulatory takeaway: version control is not optional—it’s scientific continuity.
5 ALCOA++ for Digital Endpoints
The FDA applies ALCOA++ (Attributable, Legible, Contemporaneous, Original, Accurate, Complete, Consistent, Enduring, Available) to all data captured by DHTs.
For endpoints, this translates into:
Attributable: Each endpoint value linked to a participant, device ID, and software version.
Consistent: Firmware and algorithmic configurations must remain constant throughout the collection period.
Accurate: Measurement fidelity validated under the exact conditions of use.
Available: Endpoint derivation logic accessible for regulatory review.
In decentralized trials, maintaining ALCOA++ requires system-level integration between device logs, app metadata, and study databases. Without it, data integrity collapses at the endpoint level.
6 The Intersection of QMS, Validation, and Endpoint Science
Most sponsors and CROs have Quality Management Systems (QMS) covering GCP and data management.
But traditional QMS frameworks often fail to include:
Device lifecycle documentation (ISO 13485).
Risk analysis for endpoint derivation (ISO 14971).
Software lifecycle controls (IEC 62304).
A DHT-ready QMS must ensure that endpoint calculation pipelines—not just data capture—are validated and version-tracked.
This means treating algorithms as regulated “components,” subject to the same change control and CAPA processes as physical devices.
7 Global Complications: Endpoint Equivalence Across Borders
Endpoints derived from the same DHT may behave differently across geographies.
Environmental and physiological variables—temperature, altitude, skin tone, humidity—can influence signal capture.
Moreover, device classification differs by country:
A sleep monitor used in a U.S. study may be Class I, but in the EU it may be Class IIa.
An app processing endpoint data in Europe triggers GDPR data localization requirements.
Regulators now expect country-specific validation showing that endpoint performance remains equivalent across deployment environments.
8 The Cost of Getting It Wrong
The financial and reputational impact of poor endpoint management is substantial:
Trial delays: 3–12 months to revalidate and reanalyze.
Data exclusion: endpoints deemed exploratory, not confirmatory.
Inspection findings: warning letters for insufficient change control.
Endpoints built on unvalidated firmware or undocumented algorithms are regulatory ticking bombs. They may look fine on dashboards—but collapse under scrutiny.
9 How to Future-Proof Your Endpoints
Map the Endpoint Pipeline: Identify every device, algorithm, and process step between sensor and dataset.
Version Everything: Firmware, algorithms, and data schemas must be logged and locked.
Validate in Context: Ensure analytical and clinical validity in the study’s actual conditions.
Integrate ALCOA++: Make digital provenance traceable from raw signal to statistical output.
Govern Globally: Classify devices and confirm endpoint equivalence in each jurisdiction.
Endpoint credibility is no longer about the statistic—it’s about the system that produced it.
Conclusion
In the FDA’s new digital framework, endpoints are no longer static—they are living entities shaped by technology, context, and control
A single firmware update can shatter statistical continuity.
A black-box algorithm can erase trust in your results.
And a missing audit trail can turn credible data into regulatory liabilities.
Sponsors and CROs that master endpoint governance—through validation, version control, and global equivalence—will not only survive inspection but define the new standard for digital evidence.
Because in 2025 and beyond, the question won’t be what your endpoint measures.
It will be how well you can prove it still measures the same thing.
References
FDA. Digital health technologies for remote data acquisition in clinical investigations. Silver Spring, MD: FDA; 2023.
FDA. Framework for the use of digital health technologies in drug and biological product development. Silver Spring, MD: FDA; 2023.
EFPIA. Reflection paper on integrating medical devices into medicinal product clinical trials. Brussels: EFPIA; 2025.
European Commission. Regulation (EU) 2017/745 on medical devices (MDR). Brussels: EC; 2017.
ISO 13485:2016. Medical devices – Quality management systems. Geneva: ISO; 2016.
ICH. E6(R3) Good Clinical Practice draft guideline. International Council for Harmonisation; 2023.
