OPTIMIZING MUSCULOSKELETAL PROPERTIES OF SPRINTERS
Notice bibliographique
Résumé
INTRODUCTION In sprinting events, a strong start is a major component of high-level performance. Coaches and sprinters have personal preferences on starting position and subsequent lower extremity joint angles, however there is little research on optimal start position. As the hip extensors are a key contributor to sprint performance [1], the purpose of this study was to determine the effects of altering the joint moment about the hip by manipulating hip and knee angles on block start performance. METHODS Four male sprinters (mean 100 m time 11.30 s ± 0.55 s) were recruited for this study. First, athletes performed maximal effort isometric hip extensions on a Biodex dynamometer. Hip extensor strength was tested at 90-140° of hip flexion for the front hip and 70-130° of hip flexion for the rear hip (10° increments for both). Due to biarticular muscles crossing both the hip and knee joint, the knee angle was also accounted for. Each set of hip extensor strength tests were performed at three knee angles per leg (tested in three sessions): (i) the self-selected knee angle, and the self-selected knee angle plus 10° of knee (ii) flexion and (iii) extension. Optimal joint angles were determined based on assessing the hip joint moment as a function of hip and knee angle. Participants then performed sprint starts in four positions: (i) a self-selected (control) position, (ii) a position in which only the front leg hip and knee angles were adjusted based on strength test data, (iii) a position in which only the rear leg angles were adjusted, and (iv) a position in which angles for both legs were adjusted. Five sprint starts were performed for each position. Sprint blocks were cut in half and secured to two force plates embedded in the floor to measure forces separately for each leg (2400 Hz). Kinematic data was collected using a 3D motion capture system (240 Hz). The main outcome variable for assessing performance was the total horizontal propulsive impulse, computed using Kintrak. As this was a pilot study with four subjects, no statistical analysis was performed. RESULTS Three of four athletes increased their horizontal propulsive impulse by 3.9% (9 Ns) on average compared to the control position when optimizing the front leg angles. Two of these 3 athletes also increased performance (to a lesser extent) when the angles of both legs were optimized. All athletes decreased performance when only the rear leg angle was optimized. DISCUSSION AND CONCLUSIONS Optimizing the hip and knee angles of the front leg in the starting blocks resulted in improved performance in three of the four athletes tested, while optimizing the rear leg resulted in a decrease in performance for all athletes. Previous research has shown that the front leg contributes a greater impulse during the sprint start than the rear leg [2]. As a result, we speculate that the increase in impulse from optimizing the rear leg did not compensate for the impulse lost as a result of changing the front leg. A distinguishing factor of the athlete that did not have an increased impulse when the front leg position was optimized was that he generated a much greater impulse from his rear leg when in the self-selected position. This decrease in horizontal propulsive impulse from the rear leg caused the athletes net horizontal propulsive impulse to decrease. The results of this study suggest that optimizing the sprint block positons to maximize hip extension torque based on the front leg hip torque-angle relationship is a feasible way to improve sprint start performance. Future research could assess other performance measures such as sprint times, or investigate the effect of optimizing other joint angles.
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 | 0,000 |
| 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) | 0,004 | 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 ».