Immediate Effects of Manipulating Footwear or Cadence on the Lower Limb Biomechanics of Female Masters Runners
Bibliographic record
Abstract
The objective of this study was to compare the immediate effects of modifications to footwear or cadence on lower limb biomechanics of female Masters runners. After analyzing habitual treadmill running biomechanics in 20 female runners (52.4 [8.3] y), we assessed the effects of 5 conditions: (1) barefoot running, (2) Merrell Vapor Glove, (3) Merrell Bare Access, (4) Brooks Pure Flow, and (5) increasing cadence by 10%. In comparison with habitual biomechanics, greater vertical loading rates of the ground reaction force were observed during running barefoot or with a Merrell Vapor Glove or Bare Access. There was high variability among participants as to changes in foot kinematics during the conditions. Running barefoot (-26.0%) and with a Merrell Vapor Glove (-12.5%) reduced sagittal plane knee moments, but increased sagittal plane ankle moments (both 6.1%). Increasing cadence by 10% resulted in a more modest decrease in knee flexion moments (-7.7%) without increasing peak external ankle dorsiflexion moments. When asked if they would prefer minimalist shoes or increasing cadence, 11 participants (55%) chose cadence and 9 (45%) chose footwear. Minimalist footwear decreased sagittal knee moments, but increased vertical loading rate and sagittal ankle moments. Increasing cadence may be useful to lower sagittal knee moments without increasing ankle moments.
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 | 0.000 | 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".