Timing Differences in Stride Cycle Phases in Retired Racehorses Ridden in Rising and Two-Point Seat Positions at Trot on Turf, Artificial and Tarmac Surfaces
Notice bibliographique
Résumé
Injuries to racehorses and their jockeys are not limited to the racetrack and high-speed work. To optimise racehorse-jockey dyads’ health, well-being, and safety, it is important to understand their kinematics under the various exercise conditions they are exposed to. This includes trot work on roads, turf and artificial surfaces when accessing gallop tracks and warming up. This study quantified the forelimb hoof kinematics of racehorses trotting over tarmac, turf and artificial surfaces as their jockey adopted rising and two-point seat positions. A convenience sample of six horses was recruited from the British Racing School, Newmarket, and the horses were all ridden by the same jockey. Inertial measurement units (HoofBeat) were secured to the forelimb hooves of the horses and enabled landing, mid-stance, breakover, swing and stride durations, plus stride length, to be quantified via an in-built algorithm. Data were collected at a frequency of 1140 Hz. Linear Mixed Models were used to test for significant differences in the timing of these stride phases and stride length amongst the different surface and jockey positions. Speed was included as a covariate. Significance was set at p < 0.05. Hoof landing and mid-stance durations were negatively correlated, with approximately a 0.5 ms decrease in mid-stance duration for every 1 ms increase in landing duration (r2 = 0.5, p < 0.001). Hoof landing duration was significantly affected by surface (p < 0.001) and an interaction between jockey position and surface (p = 0.035). Landing duration was approximately 4.4 times shorter on tarmac compared to grass and artificial surfaces. Mid-stance duration was significantly affected by jockey position (p < 0.001) and surface (p = 0.001), speed (p < 0.001) and jockey position*speed (p < 0.001). Mean values for mid-stance increased by 13 ms with the jockey in the two-point seat position, and mid-stance was 19 ms longer on the tarmac than on the artificial surface. There was no significant difference in the breakover duration amongst surfaces or jockey positions (p ≥ 0.076) for the ridden dataset. However, the mean breakover duration on tarmac in the presence of a rider decreased by 21 ms compared to the in-hand dataset. Swing was significantly affected by surface (p = 0.039) and speed (p = 0.001), with a mean swing phase 20 ms longer on turf than on the artificial surface. Total stride duration was affected by surface only (p = 0.011). Tarmac was associated with a mean stride time that was significantly reduced, by 49 ms, compared to the turf, and this effect may be related to the shorter landing times on turf. Mean stride length was 14 cm shorter on tarmac than on grass, and stride length showed a strong positive correlation with speed, with a 71 cm increase in stride length for every 1 m s−1 increase in speed (r2 = 0.8, p < 0.001). In summary, this study demonstrated that the durations of the different stride cycle phases and stride length can be sensitive to surface type and jockey riding position. Further work is required to establish links between altered stride time variables and the risk of musculoskeletal injury.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».