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Enregistrement W2775584748

Functional Motor Competence, Health-Related Fitness, and Injury in Youth Sport

2017· article· en· W2775584748 sur OpenAlexaboutno aff
Craig Elliott Pfeifer

Notice bibliographique

RevueScholar Commons (University of South Carolina) · 2017
Typearticle
Langueen
DomaineHealth Professions
ThématiqueSports and Physical Education Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCompetence (human resources)PsychologyMotor skillInjury preventionPhysical medicine and rehabilitationOccupational safety and healthPoison controlApplied psychologyDevelopmental psychologyMedical emergencySocial psychologyMedicine
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

In the United States there millions of youth who participate in sport.1-3 Unfortunately there is also a high rate of musculoskeletal injury in sport,4,5 accompanied by millions of dollar in medical cost.6 The development of functional motor competence and health-related fitness (HRF) is important as these two constructs are related to health, performance, and injury incidence in youth sport.7,8 It is assumed that children develop their movement ability and physical fitness as they age, however recent evidence suggests that youth functional motor competence and HRF decrease across childhood.9-13 An evaluation of functional motor competence gaining popularity among health and strength and conditioning professionals, is the Functional Movement Screen (FMS™). The FMS™ has been utilized as a screening tool to evaluate individuals at risk from dysfunctional movement.14-16 The evaluation and modification of risk factors and mechanisms for injury incidence in youth sport is critical to aid in the reduction of injury. Therefore, the following three studies were conducted. The first study evaluated the mean and distribution of the FMS™ in youth sport (age 11-18), and if there was a composite FMS™ score which was predictive of increased injury risk. Results indicated that youth sport participants have a mean composite FMS™ score of 13.54 + 2.66, revealing that these individuals demonstrated some level of dysfunctional movement. There were two composite FMS™ scores which were predictive of increased risk of injury (FMS™ < 14, < 15), however when adjusting for sport, there were no significant composite FMS™ scores that were predictive of increased risk of injury. The second study evaluated the HRF of youth sport participants (age 11-18), and provided a comparison between Canadian youth normative data and youth in sport. The results revealed that HRF in youth sport participants needs improvement, and that on several measures of HRF there were no differences between the Canadian youth normative data and youth in sport. Furthermore, this study highlights the need to evaluate and address HRF in youth as these measures may related to future health, sport performance, and risk of injury. The final study evaluated the relationship between HRF and the FMS™ in youth sport (age 11-18), and evaluated if the combination of both HRF and the FMS™ has utility for prediction of injury in youth sport. Results indicated that there are variable relationships between the FMS™ tasks and multiple measures of HRF, with not overall relationship noted. The combination of the FMS™ and HRF for the prediction of injury in sport revealed that the three salient factors for increased odds of injury risk an individual’s sex, cardiorespiratory endurance, and muscular power. The relationship between the inline lunge task of the FMS™ and HRF variables may provide insight for strength and conditioning professionals to re-evaluation their selection of training tasks based on the importance of developing both functional motor coordination and HRF.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,015
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0030,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,064
Tête enseignante GPT0,347
Écart entre enseignants0,282 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2017
Routes d'admission1
Résumé présentoui

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