Regular changes in foot strike pattern during prolonged downhill running do not influence neuromuscular, energetics, or biomechanical parameters
Bibliographic record
Abstract
Abstract Research has suggested that a high variability in foot strike pattern during downhill running is associated with lower neuromuscular fatigue of the plantar flexors (PF). Given the popularity of trail running, we designed an intervention study to investigate whether a strategy with regular changes in foot strike pattern during downhill running could reduce the extent of fatigue on neuromuscular, energetics and biomechanical parameters as well as increase an uphill time‐to‐exhaustion trial (TTE) performance. Fourteen experienced trail runners completed two interventional conditions (separated by 15 days) in a pseudo‐randomised and counter‐balanced order that consisted of 2.5‐h of treadmill graded running with (switch condition) or without (control condition) a change between fore‐ and rear‐foot strike pattern every 30 s during the downhill sections. Pre and Post, neuromuscular tests were performed to assess PF central and peripheral fatigue. Energy cost of running was assessed using an indirect calorimetry system and biomechanical gait parameters were acquired with an instrumented treadmill. TTE was performed after both the graded running conditions. There were not significant condition × time interactions (p ≥ .085) for any of the variables considered, and TTE was not different between the two conditions (p = .755). A deliberate strategy to alternate between foot strike patterns did not reduce the extent of fatigue during prolonged graded running. We suggest that it is not the ability to switch between foot strike patterns that minimises fatigue; rather the ability to adapt foot strike pattern to the terrain and therefore a better running technique.
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 | 0.000 |
| 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) | 0.002 | 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".