Perception Of Footwear Comfort And Oxygen Consumption Of Female Runners In Marathon Racing Shoes
Bibliographic record
Abstract
Comfort filter paradigm emphasizes: 1) runners prefer different shoes for comfort, and 2) more comfortable running shoes correlate with reduced oxygen consumption (VO2). Super shoes, designed for racing with unique features, have emerged as an advantage, improved energetic cost and comfort. However, no study has examined the perception of comfort in marathon racing shoes among female runners. PURPOSE: To examine the correlation between VO2 and perception of footwear comfort among four marathon racing shoes. METHODS: Elite female runners who ran a sub-40:00-minute 10 km race in the previous 12 months were recruited. Participants ran on a treadmill at 14 km/h with four marathon racing shoes in a randomized and mirrored order. The Running Footwear Comfort Assessment Tool (RUN-CAT) was conducted prior to the running trials for initial comfort during walking as well as following trials. Pearson correlation coefficients was used to identify relationships between VO2 and comfort scores in each RUN-CAT item for all shoes. RESULTS: 22 female runners (Age: 29.69 ± 7.09 years) participated in this study. Shoe B exhibited a moderate positive correlation (r = 0.455; p < 0.05) between VO2 and the heel cushioning score, indicating improved running economy in shoes perceived to have less cushioning, in the initial comfort test. Shoe C showed a moderate positive correlation (r = 0.523; p < 0.05) between VO2 and the overall footwear comfort score following running. CONCLUSION: While our findings suggest that relying solely on the comfort filter paradigm may not be sufficient for female runners to predict running economy, footwear designers should consider the effect on shoe comfort when designing marathon shoes to improve performance. Table 1. - Running Footwear Comfort Assessment Tool (RUN-CAT) scores (0 to 100) before and after running trials and the average energetic cost of each marathon racing shoe. Shoe A Shoe B Shoe C Shoe D Run-CATbefore running trials (1) Heel cushioning 45.91 ± 20.23 66.09 ± 13.35* 60.27 ± 17.53 64.14 ± 11.87 (2) Forefoot cushioning 64.77 ± 13.13 59.45 ± 15.82 65.95 ± 12.12 45.77 ± 18.22 (3) Shoe stability 45.27 ± 14.76 58.59 ± 13.57 56.91 ± 13.95 52.32 ± 17.61 (4) Forefoot flexibility 47.50 ± 13.63 50.77 ± 15.57 44.86 ± 14.57 45.09 ± 19.64 (5) Shoe comfort 70.45 ± 15.70 60.05 ± 18.81 70.68 ± 15.93 49.27 ± 21.81 Run-CATafterrunning trials (1) Heel cushioning 63.27 ± 14.52 54.09 ± 15.41 62.23 ± 16.40 33.27 ± 17.53 (2) Forefoot cushioning 63.32 ± 12.56 58.91 ± 18.76 65.32 ± 17.55 34.05 ± 18.64 (3) Shoe stability 50.91 ± 14.41 55.86 ± 12.87 57.64 ± 14.63 56.00 ± 18.67 (4) Forefoot flexibility 50.09 ± 15.27 53.55 ± 12.22 52.41 ± 12.67 43.91 ± 20.96 (5) Shoe comfort 76.73 ± 12.17 60.64 ± 18.56 67.32 ± 21.09* 35.50 ± 23.28 Energetic Cost (mL*min-1 *kg-1) 45.70 ± 2.72 45.57 ± 2.68 45.89 ± 2.43 46.73 ± 2.71 Mitacs Accelerate IT24201
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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 | 0.000 | 0.001 |
| 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) | 0.003 | 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".