Biomechanics of graded running: Part I ‐ Stride parameters, external forces, muscle activations
Bibliographic record
Abstract
Biomechanical alterations with graded running have only been partially quantified, and the potential interactions with running speed remain unclear. We measured spatiotemporal parameters, ground reaction forces, and leg muscle activations (EMG) in nineteen adults (10F/9M) running on an instrumented treadmills at 2.50, 3.33, and 4.17 m·s−1 and 0, ±5°, and ±10°. Step frequency illustrated a significant speed × grade interaction (P < .001) and was highest (+3%) at the steepest grade (+10°) and fastest speed (4.17 m·s−1) when compared to level running (LR) at the same speed. Significant interaction was also observed for ground reaction forces (all P ≤ .047). Peak ground reaction forces in the normal direction increased with running speed during downhill running (DR) only (+9% at −10° and 4.17 m·s−1). Impulse in the normal direction decreased at fastest speed and steepest DR (−9%) and uphill running (UR) (−17%) grades. Average normal loading rate increased and decreased at fastest speed and steepest DR (+52%) and UR (−28%) grades, respectively. Negative parallel impulse increased and decreased at fastest speed and steepest DR (+166%) and UR (−90%), respectively. Positive parallel impulse decreased and increased at fastest speed and steepest DR (−75%) and UR (+111%), respectively. EMG showed comparable u‐shaped curves across the grades investigated, although only a change in vastus lateralis and tibilias anterior activity was detectable at the steepest grades and fastest speed. Overall, running grade and speed significantly influences spatiotemporal parameters, ground reaction forces, and muscle activations.
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) | 0.001 | 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".