Effects of changing speed on knee and ankle joint load during walking and running
Bibliographic record
Abstract
Joint moments can be used as an indicator of joint loading and have potential application for sports performance and injury prevention. The effects of changing walking and running speeds on joint moments for the different planes of motion still are debatable. Here, we compared knee and ankle moments during walking and running at different speeds. Data were collected from 11 recreational male runners to determine knee and ankle joint moments during different conditions. Conditions include walking at a comfortable speed (self-selected pacing), fast walking (fastest speed possible), slow running (speed corresponding to 30% slower than running) and running (at 4 m · s(-1) ± 10%). A different joint moment pattern was observed between walking and running. We observed a general increase in joint load for sagittal and frontal planes as speed increased, while the effects of speed were not clear in the transverse plane moments. Although differences tend to be more pronounced when gait changed from walking to running, the peak moments, in general, increased when speed increased from comfortable walking to fast walking and from slow running to running mainly in the sagittal and frontal planes. Knee flexion moment was higher in walking than in running due to larger knee extension. Results suggest caution when recommending walking over running in an attempt to reduce knee joint loading. The different effects of speed increments during walking and running should be considered with regard to the prevention of injuries and for rehabilitation purposes.
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 | 0.002 |
| 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) | 0.001 | 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".