Does the device measure what it claims to measure?
Does the raw signal reflect the physiological quantity the device claims to capture.
Metriqx
Your numbers are going to be tested.Better it happens here.
For teams building wearables, sensors, and software as a medical device. The same read investors and buyers commission, early enough that it still changes what you ship.
An invalid measurement is an unlogged hazard: under DCB0129 that is a clinical safety obligation, not a quality nice-to-have. The same logic binds any number that drives a decision. Metriqx evaluates human-state technologies across health and high-performance settings, from elite sport to defence: whether a device measures what it claims, whether its outputs are valid, and whether it holds up inside the decisions it informs.
The obligation
DCB0129 makes the health case explicit: a measurement that can mislead a clinical decision is a hazard, and an unlogged hazard is a gap in your safety case. The same evidence logic sits behind every regulatory shape it takes: a Clinical Evaluation Report for CE-Mark, an FDA submission for Software as a Medical Device (SaMD), a safety case for the MHRA. Different documents, one obligation. Outside healthcare there is no equivalent standard, only a number that drives a selection, training, or deployment decision. The obligation to know it is real does not change.
Clearing a regulator is not the same as making a sale. In the UK the gates that actually decide whether an NHS organisation can buy you are the NICE Evidence Standards Framework for digital health technologies, which asks what your evidence proves and at what tier, and the Digital Technology Assessment Criteria (DTAC), which asks whether you can be adopted safely at all. Both are answered with the same evidence, and both are where products with a CE-Mark still stall.
The approach
One framework, three layers.
One framework runs across health and performance, because the failure modes (and the obligation to catch them) are the same. This is the logic, not the full methodology.
Are the outputs it derives from that signal valid?
Do the metrics computed from that signal actually hold.
Does it hold up inside the decisions it is meant to inform?
Does it work inside real clinical or performance practice, with the people who rely on it.
The framework is domain-agnostic. That is why a single engine serves clinical and high-performance work, from elite sport to defence.
Provenance
Layers 01 and 02 are the V3 framework (verification and analytical validation), the field's standard reference for whether a biometric monitoring technology is fit for purpose. We did not invent it and we use it because it is right. Layer 03 is our extension: V3 asks whether a metric tracks a meaningful clinical state, and we push that further into whether it survives the decision and the workflow it is built for, in high-performance settings as well as clinical ones.
Goldsack et al. (2020), Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs). npj Digital Medicine 3:55. doi:10.1038/s41746-020-0260-4 ↗
Who it is for
Two domains, one framework.
Builders and backers
For teams shipping a clinical product and the investors funding them, who need to know the evidence is real before launch or capital goes in.
Teams and vendors
For teams in elite sport, defence, and other high-performance settings relying on a number to drive a decision, and the vendors selling it, who need to know the number means what the dashboard says.
Building, buying, or backing it?
Evidence review and technical advisory for the teams building it, and an independent read for the people committing money.