Step length and grade effects on energy absorption and impact attenuation in running
Bibliographic record
Abstract
Abstract We sought to examine the effect of step length manipulation on energy absorption and impact attenuation during graded running. Nineteen runners (10F, 9M) ran on an instrumented treadmill at three step lengths (preferred and ±10% preferred) at each of five grades (0°, ±5°, and ±10°) while 3D motion data were captured. Speed was held constant at 3.33 m/s and step length was manipulated by syncing cadence to a metronome. Manipulating step length altered energy absorption ( p ≤ 0.002) and impact attenuation ( p < 0.0001) across all grades. Energy absorption at the knee joint was most responsive to step length manipulations [Δ range (±10%SL‐PrefSL) = 0.076–0.126 J/kg, p < 0.0001], followed by the ankle (Δ range = 0.026–0.100 J/kg, p = 0.001) and hip (Δ range = 0.008–0.018 J/kg, p < 0.006). Shortening step length reduced knee joint energy absorption at all grades with the smallest effect observed during uphill running (Δ ≥ −0.053 J/kg), while large reductions occurred during level (Δ = −0.096 J/kg) and downhill running (Δ ≥ −0.108 J/kg). Increasing step length resulted in greater knee joint energy absorption ( p ≤ 0.037) across all grades of running. Impact attenuation was greatest at long step lengths (Δ = 2.708) and lowest at short step lengths (Δ = −2.061), compared to preferred. Overall, Step length influenced the energy absorption and impact attenuation characteristics of the lower extremity during level and graded running. Adopting a shorter step length may be a useful intervention to reduce knee joint loading, particularly during downhill or level running. Elongating step length placed a greater demand on the lower extremity joints, which may expedite the development of neuromuscular fatigue.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".