Changes in Frequency Domain Accelerations During Prolonged Running on Different Surfaces
Bibliographic record
Abstract
Curved non-motorized treadmills (cNMTs) have been demonstrated to reduce impact accelerations in comparison with motorized treadmills (MTs). Most studies have analyzed impacts in the time domain, but analysis in the frequency domain can provide useful information associated with the increase in the running risk of injury. The purpose of this study was to analyze the frequency components (low- and high-frequency bands) of impact accelerations, countermovement jump (CMJ) height, and perceived comfort during a prolonged run on different surfaces: MT, cNMT, and overground (OVG). Twenty-one recreational runners completed three randomized prolonged running tests on cNMT, MT, and OVG for 30 min (80% of the individual maximal aerobic speed). Impact accelerations were registered at minutes 5 and 30 of the test, the countermovement jump test (CMJ) was performed before (PreTest) and after (PostTest) the test, and perceived comfort was determined at the end of each test. A two-way repeated-measures analysis of variance (significance at p < 0.05) showed a reduction on cNMT in both low- and high-frequency bands of impact accelerations, such as head power (p < 0.001, ESd = 3.0) on the cNMT vs. the MT and tibia peak power (p = 0.001, ESd = 2.2) on the cNMT vs. OVG. However, cNMT was perceived as the least comfortable surface by runners. The prolonged running effect decreased impact accelerations during the treadmill running test (MT and cNMT) in the low-frequency band, while CMJ height decreased (p = 0.024, ESd = 1.4) during the PostTest vs. PreTest with the cNMT. Using a cNMT could be an interesting strategy for load reduction in long-distance runners or in return-to-play rehabilitation protocols.
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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".