What you get
The published human-subjects evidence for a design decision: what has been measured, in whom, under what conditions, with what effect size — and where the evidence simply does not exist for your population.Who it’s for
Product designers, human factors and ergonomics engineers, sports-science and footwear R&D teams, and occupational health groups. Corporate science teams doing kinesiology and biomechanics research sit squarely here.Why it belongs to human-subjects rules
This is the corner of applied R&D where the evidence is about people, so the filters that matter are the clinical ones:human, controlled, and sample_size_min do more for result quality here than any materials or engineering filter.
How it works
1
State the decision as an outcome question
Not “what does the research say about midsole foam” but “does increased midsole stiffness reduce metabolic cost in recreational runners”. Named population, named intervention, named measurable outcome.
2
Demand human, controlled evidence
Set
human=true, controlled=true, and a realistic sample_size_min. Biomechanics studies are often very small; a threshold around 15 to 20 filters out the least informative work without emptying the result set.3
Match the population to your users
Trained athletes are not recreational users. Young male university students are not the general population. This literature has a well-known sampling skew, so read the population column before the finding.
4
Separate lab measures from field outcomes
A change in a gait parameter on a treadmill is not a change in injury rate in the world. Track which you have — this is where most overclaiming in this field starts.
5
Search the null result
Run the negative phrasing. In biomechanics, small studies with positive findings publish more readily than small studies without.
The API call
domain=psych,eng,med covers most of this literature — human factors work is split across psychology, engineering, and sports medicine, and restricting to one loses a third of it.
What to check before you trust it
- Read the sample description on every study. Population mismatch is the dominant failure mode in this literature, and it is rarely visible in the abstract’s conclusion.
- Check effect size, not significance. A statistically significant 1% change in a lab measure may be irrelevant to a design decision.
- Watch for surrogate outcomes. Comfort ratings, gait parameters, and injury rates are three different claims, and only the last supports an injury-reduction statement.
- Small samples dominate. Treat any single study as provisional; look for replication before it drives a design commitment.
Related
Substantiate a product claim with published evidence
Turn the evidence into a claim you can defend publicly.
Grade a set of papers with your own evidence rubric
Score the studies consistently before deciding.
Best practices
Filter ladder and the rest of the primitives.
All use cases
Browse the gallery by persona.