Evidence assessment service

Your devices collect data.
We test whether
the measures matter.

Meaningful Measures Lab is FASTlab's evidence assessment service for organisations working with sensor-derived measures outside hospital. We tell you what your data can responsibly support — and where the evidence chain breaks.

Register interest Download data spec
5,509
Records reviewed in the evidence atlas
31
Papers meeting the evidence standard
5
Dimensions in the Evidence Lens
3–5
Founding partner places available
What the Lab is

Collecting data and having evidence-ready measures are not the same thing.

Heart rate, SpO₂, sleep, activity, falls, movement — organisations working in remote monitoring, supported living and care outside hospital are already capturing these signals. What most can't easily answer is: which of those measures are strong enough to support responsible claims? Where does the evidence chain break? What can and can't be said to a partner, an investor, a clinician or a regulator?

Meaningful Measures Lab exists to answer those questions. The public dataset testing tool below lets you see how the evidence framework handles your data locally. The full assessment — FASTlab's review, the Readiness Report and the debrief — comes through the partner pathway.

Research

What the evidence atlas established

The Meaningful Measures systematic review — 5,509 records screened, 31 papers included — identifies which sensor-derived measure families have sufficient signal clarity, threshold evidence and action pathway support to be considered meaningful in home and community care settings.

View the evidence atlas →
Lab

What the Lab adds

The Lab applies that evidence base to your specific measures, data exports and service pathways. FASTlab assesses your data against the atlas, works through the evidence chain from signal to action, and produces a Readiness Report: what is strong, where the chain breaks, which claims are premature, and what evidence to build next.

Evidence Lens

Five questions. One purpose: does this measure support responsible action?

A measure becomes meaningful only when it can travel from reliable signal through contextual interpretation, a defined threshold, and a clear response pathway.

01 · Signal
Reliable detection
Is something consistently captured?
A value, change, pattern or event that is technically trustworthy and consistently collected.
02 · Context
Interpretability
Can the signal be read for this person?
The same reading means different things depending on the individual, setting, condition and baseline.
03 · Threshold
A trigger exists
Is there a meaningful cut-off?
A rule, risk state or decision point that explains why a given reading matters — not just a number.
04 · Validation
Evidence tested
Has it been shown to work?
Evidence that the measure relates to outcomes, detects meaningful change, or improves decisions in the intended setting.
05 · Action
Response pathway
Does it change what someone does?
A clear and documented response: review, triage, reassurance, escalation or appropriate non-action.
How it works

Four steps from first contact to readiness report.

The Lab is a FASTlab-managed assessment service. Partners don't assess themselves — we do the assessment. Data is shared only after an agreed, secure submission pathway is in place.

01

Discovery call

We confirm the device, setting, population, measures and the specific evidence question — whether for product development, pilot design, commercialisation, investor due diligence or implementation planning.

02

Data specification

FASTlab provides a short Partner Data Specification explaining what de-identified sample export, data dictionary, threshold logic, alert records and action evidence would be useful. Partners review this before agreeing the secure submission pathway.

03

Lab assessment

FASTlab maps the partner measures against the Evidence Lens — signal reliability, contextual interpretability, threshold clarity, validation confidence and action pathway evidence — and compares findings against the Meaningful Measures evidence atlas.

04

Readiness report and debrief

The partner receives a Meaningful Measures Readiness Report and a structured debrief session with FASTlab. The report sets out what the data currently supports, where the evidence chain breaks, which claims are premature, and what evidence work is needed next.

What you receive

A Readiness Report. Useful for product teams, leadership and responsible claims.

The report is FASTlab-authored, not generated. Clear enough for a board conversation, specific enough for a product team, and cautious enough to support responsible digital health claims.

FASTlab · Meaningful Measures Lab

Meaningful Measures Readiness Report

Example Partner — Home Monitoring Pilot
78
Readiness score / 100
Evidence strengths
Heart rate variability — explicit threshold evidence, documented alert and action pathway
Sleep disruption pattern — contextual interpretation supported, review trigger present
Activity trend — validated against deterioration in three included evidence papers
Evidence gaps and next moves
SpO₂ — threshold present but highly context-dependent; baseline window not defined
Falls detection — signal reliable; action pathway evidence absent from current export
Recommended: define baseline windows, document escalation logic, capture response records
Illustrative example only. Partner reports are produced by FASTlab following the agreed assessment pathway.
Dataset testing lab

Test your data against the evidence atlas — locally, in this browser.

Load a synthetic demo or upload a de-identified CSV or JSON file to see how the Evidence Lens framework handles your measures. Files are read only in your browser — nothing is submitted to FASTlab. The full partner assessment happens through the pathway below.

Evidence + real-world data

Load a dataset

Use the synthetic demonstrators, upload a CSV/JSON file, or paste rows directly. Analysis happens locally in your browser.

Ready. The current view uses the remote vitals demonstrator until a real dataset is loaded.
FASTlab IP notice This dataset testing lab — including the upload/paste workflow, evidence overlay, detected-measure ranking and downloadable lab-result format — is FASTlab research-translation IP. Use of the demonstrator does not transfer ownership of uploaded or pasted source data.
0

Dataset not loaded

Load a demonstrator or upload a file to see how the framework handles it.

Pathway-ledAction exists, but the signal still needs work.
Candidate measuresEvidence and dataset behaviour align.
Explore carefullyEarly signal, low confidence or limited coverage.
Reported signalClear variable, but weak action pathway.
Threshold clarity →
Clinical actionability →

Detected measures

Each card combines the current evidence prior with what the uploaded dataset can actually support: coverage, threshold behaviour and outcome association where an outcome field is present.

How the framework handles new data

Generate end-user output Generate a polished, printable Meaningful Measures Dataset Report for non-technical readers. The report uses the current on-screen analysis and keeps uploaded or pasted data local to the browser.
Dataset testing rubric
1. Evidence priorStarts with the current review synthesis: threshold clarity, actionability, validation confidence and evidence breadth.
2. Data coverageChecks whether the measure is present often enough, clean enough and complete enough to be interpretable.
3. Signal behaviourLooks for abnormal readings, trends, variability and, where possible, association with escalation or deterioration.
4. Validation next stepSeparates measures that can be used now for exploration from those that need formal validation before clinical claims.
Founding cohort

Three to five founding partners. A structured first cohort.

We are inviting a small, selected group of organisations to work through the Lab assessment process as founding partners — getting a rigorous first assessment while helping shape how the service develops.

Founding Partner Diagnostic

Free or low-cost first assessment · selected organisations · limited places
3 places remaining

What's included

  • Discovery call with FASTlab
  • Partner Data Specification document
  • Full Lab assessment against the evidence atlas
  • Meaningful Measures Readiness Report
  • Evidence gap analysis and prioritised next moves
  • Debrief session with FASTlab
Register interest Data spec

Who this is for

  • Device and wearable companies with existing field data
  • Care providers implementing or reviewing remote monitoring
  • Health technology organisations preparing for pilots or investment
  • Organisations reviewing claims before commercialisation or partnership
What we need first: a de-identified sample export or data dictionary, threshold or alert logic where documented, and any existing action or outcome records. Full guidance is in the data specification.
What happens next

From first contact to readiness report — in four steps.

No data leaves your organisation until FASTlab has confirmed the secure submission route. The process is structured so partners know exactly what to prepare and what to expect at each stage.

After you get in touch

01
Within 48 hours

Paul responds

A short reply confirming receipt and proposing a 30-minute discovery call. No preparation needed at this stage — just a conversation about your device, data and the evidence question you're trying to answer.

02
Discovery call

We agree the scope

FASTlab confirms the measures, setting, population and primary evidence question. You receive the Partner Data Specification — a short document explaining exactly what de-identified data and supporting information would be useful.

03
Secure submission

Data shared on agreed terms

FASTlab confirms the secure route for your sample export — never a public web form. You share a de-identified sample, data dictionary and any threshold or action-pathway documentation you have.

04
Within agreed timescale

Report and debrief

You receive a Meaningful Measures Readiness Report — what the data supports, where the evidence chain breaks, which claims are premature, and what evidence to build next. Followed by a structured debrief with FASTlab.

What to prepare

You don't need everything below to get started — but the more of this you have, the stronger the assessment. If you're unsure what you have, the discovery call will sort that out.

A de-identified sample export — CSV or JSON, even a small one
A data dictionary or variable list (column names, units, collection method)
Any threshold or alert logic your platform uses (even informal rules)
Action or outcome records where they exist (review notes, escalation logs, care responses)
A brief description of the setting and population (home, post-discharge, residential care, etc.)
The specific evidence question — what you're trying to claim, support or understand
The public dataset testing tool on this page lets you try the framework with your own file before reaching out. It can help you see which measures detect and what questions to bring to the discovery call.

Ready to get started?

Send a structured intake note — it opens a pre-filled email with the key questions. Paul will respond within 48 hours to arrange a discovery call.

Send intake note → Data spec PDF
Underpinned by

The Meaningful Measures evidence atlas

The Lab is a FASTlab assessment service grounded in a completed systematic evidence review: 5,509 records screened, 31 papers included, measure families evidence-weighted by threshold clarity, clinical actionability and validation confidence. The research is separate — publicly available, developed with the University of Newcastle, UTS Institute for Sustainable Futures, and enabled by the NSSN Grand Challenge Fund. The Lab is where that evidence is applied to your data.

View the research →