Evidence of variable performance responses to the Nike 4% shoe: Definitely not a game-changer for all recreational runners
Bibliographic record
Abstract
Purpose: We compared running economy (RE) and 3-km time-trial (TT) performances of male recreational runners wearing the Nike Vaporfly 4% (NIKE), lightweight racing flats (FLAT), and their habitual footwear (OWN). Methods: Eighteen male recreational runners [age: 33.5 (11.9) y, V̇O2peak: 55.8 (4.4) mL·kg-1·min-1] attended 4 sessions ~7 days apart. The first session consisted of a V̇O2peak test to inform subsequent RE speeds set at 60, 70, and 80% of the speed eliciting V̇O2peak. In subsequent sessions, treadmill RE and 3-km TT were assessed in the three footwear in a randomised, counterbalanced crossover design. Results: RE was improved in NIKE (3.6 to 4.5%, p ≤ 0.002) and FLAT (2.4 to 4.0%, p ≤ 0.042) versus OWN across intensities, with a trivial difference between NIKE and FLAT (1.0 to 1.6%, p ≥ 0.325). NIKE 3-km TT (11:07.6 ± 0:56.6 mm:ss) was superior to OWN by 16.6 s (2.4%, p = 0.005) and FLAT by 13.0 s (1.8%, , p = 0.032), with similar times between OWN and FLAT (0.5%, , p = 0.747). Only 29% of runners were more economical across intensities and faster in NIKE. Conclusions: Overall, our findings indicate that NIKE could benefit RE in male recreational runners at relative speeds when compared to OWN, but not when compared to FLAT. More runners exhibited better TT performances in NIKE (61%) versus FLAT (22%) and OWN (17%). The high variability in individual RE (-3.1 to 12.1%) and TT (-3.8 to 8.2%) shoe-responses suggests that individualisation of running footwear prescription is warranted.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".