Biomechanics and subjective measures of recreational male runners in three shoes running outdoors: a randomised crossover study
Bibliographic record
Abstract
We compared biomechanical and subjective data from outdoor running in: habitual (OWN), Saucony Endorphin Racer 2 minimal (FLAT) and Nike Vaporfly 4% (VP4) shoes. We also explored relationships between comfort measures and the collected data. Eighteen male recreational runners ran three 1.5-km trials outdoors, once per shoe. The first 1.1 km was run at a self-selected comfortable (slower) speed, and last 400 m at perceived 5-km race pace (faster). A GPS-enabled smartwatch, 15-m Optojump system, high-speed camera and tibial accelerometer collected biomechanical data. Subjective data on comfort, shoe properties and overall running experience were collected using visual analogue scales (VAS) and rankings. Cadence, leg stiffness and vertical stiffness were greater in FLAT than both OWN and VP4 at the slower speed (trivial to small ES). At both speeds, footstrike angles were smaller in FLAT (small to large ES), while propulsion phase was shorter in VP4 (moderate to large ES). FLAT was ranked as the least comfortable at the slower speed and most likely to cause injury, whereas OWN as the most comfortable and least likely to cause injury. Comfort was not significantly different at the faster speed between shoes. Comfort measures were more strongly correlated to subjective than biomechanical data. The two experimental shoes generally had non-significant or small effects on running biomechanics versus OWN. As VP4 are more like traditional than minimal shoes, these were perceived as more comfortable. Running speed appeared to affect subjective measures. Speed should be considered when prescribing and selecting shoes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".