Greater foot and footwear mechanical work associated with less ankle joint work during running
Bibliographic record
Abstract
Footwear energy storage and return is often suggested as one explanation for metabolic energy savings when running in Advanced Athletic Footwear. However, there is no common understanding of how footwear energy storage and return facilitates changes in muscle and joint kinetics. The purpose of this study was to evaluate the magnitude and timing of foot, footwear and lower limb joint powers and work while running in Advanced and Traditional Athletic Footwear. Fifteen runners participated in an overground motion analysis study. Since footwear kinetics are methodologically challenging to quantify, we leveraged distal rearfoot power analyses (‘foot + footwear’ power) and evaluated changes in the magnitude and timing of foot + footwear power and lower limb joint powers. Running in Advanced Footwear resulted in greater foot + footwear work, compared to Traditional Shoes, and lower positive ankle work, potentially reducing the muscular demand on the runner. The timing of foot + footwear power varied only slightly across footwear. There are exciting innovation opportunities to manipulate the timing of footwear energy and return. This study demonstrates the research value of quantifying time-series foot + footwear power, and points industry developers towards footwear innovation opportunities.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".