Notice bibliographique
Résumé
Eighty-two high performance young male fast bowlers (mean age 16.8 years) were tested immediately prior to the season for selected kinanthropometric and physiological data. Subjects were also filmed both laterally (200 Hz) and from above (100 Hz) while bowling so that their front foot impacted a force platform during the delivery stride. The players then completed a log book over the ensuing season that detailed their training and playing programs. All cricket related injuries over this season were assessed by a sports physician who used computerized tomography to assist in the diagnosis of spinal injuries. At the completion of this season the players were grouped according to their injury status (Group 1--bony injury to a vertebra; Group 2--soft tissue injury to the back that caused the player to miss at least one game, and Group 3--no injuries). A one-way analysis of variance was used to identify if any variables were significantly (P less than 0.05) different between the three groups, and a Scheffe post hoc comparison was used to determine which groups were significantly different. Eleven percent of the players sustained a stress fracture to a vertebra (e) (L4 to S1), while 27% sustained a soft tissue injury to the back. Bowlers with a low longitudinal foot arch were more likely to develop a stress fracture than those with a high arch. Shoulder depression and horizontal flexion strength for the preferred limb and quadriceps power in the non-preferred limb were also significantly related to back injuries. Bowlers who rotated the trunk to re-align the shoulders by more than 40o to a more side-on position between back foot impact and front foot impact, in the delivery stride were more likely to sustain back injuries. No significant relationship was determined between peak vertical (5.4 BW) or horizontal (-2.5 BW) ground reaction forces at front foot impact, however, these forces may predispose a bowler to back injuries if the person is required to bowl for long periods. Nineteen of 32 players (59%), who bowled in excess of the mean number of matches for the group, suffered a stress fracture or soft tissue injury to the back compared to the 38% injury frequency for the total group. A greater release height when expressed as a percentage of standing height was also significantly related to back injuries. Results suggest that players with the above physical characteristics, who bowl with these biomechanical techniques for extended periods, may pre-dispose the bowler to back injuries. Despite its long history and global appeal, relatively little is known about the physiological and other requirements of cricket. It has been suggested that the physiological demands of cricket are relatively mild, except in fast bowlers during prolonged bowling spells in warm conditions. However, the physiological demands of cricket may be underestimated because of the intermittent nature of the activity and the generally inadequate understanding of the physiological demands of intermittent activity. Here, we review published studies of the physiology of cricket. We propose that no current model used to analyses the nature of exercise fatigue (i.e. the cardiovascular–anaerobic model, the energy supply–energy depletion model, the muscle power–muscle recruitment model) can adequately explain the fatigue experienced during cricket. A study of players in the South African national cricket team competing in the 1999 Cricket World Cup revealed that, in a variety of measures of explosive ('anaerobic') power and aerobic endurance capacity, they were as 'fit' as South African national rugby players competing in the 1999 Rugby World Cup. Yet, outwardly, the physiological demands of rugby would seem to be far greater than those of cricket. This paper provides an in-depth analysis of the prevalence, causes, mechanisms, and prevention strategies for back injuries in fast bowlers in cricket. It reviews existing literature, highlights risk factors, biomechanical considerations, and rehabilitation techniques, aiming to enhance the understanding and management of this prevalent issue in the sport
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) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».