Curved non-motorized treadmills do not biomechanically replicate overground running better than motorized treadmills
Bibliographic record
Abstract
The purpose of this study was to determine if curved non-motorized treadmills can reproduce overground running better than motorized treadmills by analysing the differences in joint kinematics (hip, knee, and ankle) using SPM. Nineteen recreational runners completed three randomized running tests on these surfaces. Kinematic data from the hip, knee, and ankle joints were collected. Two-tailed SPM t-tests were performed to analyse time-continuous gait cycles in three anatomical planes of each joint. Higher within-subject variability was observed in the frontal and transverse planes during curved non-motorized treadmill running. SPM analysis showed more significant differences (p < 0.05) between curved non-motorized treadmill and overground than between motorized treadmill and overground, mainly in knee (from 12% to 30% and 93% to 99% of the gait cycle) and ankle (from 19% to 23% of the gait cycle) in the sagittal plane. Therefore, running on curved non-motorized treadmills is more biomechanically different compared to overground than motorized treadmills, and might not be the best strategy to replicate overground running in terms of joint kinematics during highly controlled research studies. However, they could be an interesting tool in rehabilitation or training environments since the changes observed in joint kinematics were likely not functionally relevant.
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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 | 0.001 | 0.003 |
| 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".