Water treadmill exercise reduces equine limb segmental accelerations and increases shock attenuation
Bibliographic record
Abstract
BACKGROUND: Equine water treadmills (WTs) are growing in popularity because they are believed to allow for high resistance, low impact exercise. However, little is known about the effect of water height on limb loading. The aim of this study was to evaluate the effect of water height and speed on segmental acceleration and impact attenuation during WT exercise in horses. Three uniaxial accelerometers (sampling rate: 2500 Hz) were secured on the left forelimb (hoof, mid-cannon, mid-radius). Horses walked at two speeds (S1: 0.83 m/s, S2: 1.39 m/s) and three water heights (mid-cannon, carpus, stifle), with a dry WT control. Peak acceleration of each segment was averaged over five strides, attenuation was calculated, and stride frequency was estimated by the time between successive hoof contacts. Linear mixed effects models were used to examine the effects of water height, speed, and accelerometer location on peak acceleration, attenuation and stride frequency (p < 0.05). RESULTS: Peak acceleration at all locations was lower with water of any height compared to the dry control (p < 0.0001). Acceleration was reduced with water at the height of the stifle compared to mid-cannon water height (p = 0.02). Water at the height of the stifle attenuated more impact than water at the height of the cannon (p = 0.0001). CONCLUSIONS: Water immersion during treadmill exercise reduced segmental accelerations and increased attenuation in horses. WT exercise may be beneficial in the rehabilitation of lower limb injuries in horses.
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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 | 0.000 |
| 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".