Metabolic and performance responses of male runners wearing 3 types of footwear: Nike Vaporfly 4%, Saucony Endorphin racing flats, and their own shoes
Bibliographic record
Abstract
PURPOSE: We compared running economy (RE) and 3-km time-trial (TT) variables of runners wearing Nike Vaporfly 4% (VP4), Saucony Endorphin lightweight racing flats (FLAT), and their habitual running (OWN) footwear. METHODS: . In subsequent sessions, treadmill RE and 3-km TTs were assessed in the 3 footwear conditions in a randomized, counterbalanced crossover design. RESULTS: Oxygen consumption (mL/kg·min) was less in VP4 (from 4.3% to 4.4%, p ≤ 0.002) and FLAT (from 2.7% to 3.4%, p ≤ 0.092) vs. OWN across intensities, with a non-significant difference between VP4 and FLAT (1.0%-1.7%, p ≥ 0.292). Findings related to energy cost (W/kg) and energetics cost of transport (J/kg·m) were comparable. VP4 3-km TT performance (11:07.6 ± 0:56.6 mm:ss) was enhanced vs. OWN by 16.6 s (2.4%, p = 0.005) and vs. FLAT by 13.0 s (1.8%, p = 0.032). The 3-km times between OWN and FLAT (0.5%, p = 0.747) were similar. Most runners (n = 11, 61%) ran their fastest TT in VP4. CONCLUSION: Overall, VP4 improved laboratory-based RE measures in male recreational runners at relative speeds compared to OWN, but the RE improvements in VP4 were not significant vs. FLAT. More runners exhibited better treadmill TT performances in VP4 (61%) vs. FLAT (22%) and OWN (17%). The variability in RE (-10.3% to 13.3%) and TT (-4.7% to 9.3%) improvements suggests that responses to different types of shoes are individualized and warrant further investigation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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".