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Enregistrement W4416209166 · doi:10.1302/1358-992x.2025.13.105

JOINT STIFFNESS AND MUSCLE ACTIVATION PATTERNS DURING JUMP-LANDING AND SIDE-CUTTING IN ADOLESCENT MALES AND FEMALES WITH AND WITHOUT ANTERIOR CRUCIATE LIGAMENT INJURIES: ADOLESCENTS ARE NOT JUST SMALL ADULTS

2025· article· en· W4416209166 sur OpenAlexaff
Michael Del Bel, Daniel L. Benoit, Brandon A. Miller, Sasha Carsen

Notice bibliographique

RevueOrthopaedic Proceedings · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueKnee injuries and reconstruction techniques
Établissements canadiensChildren's Hospital of Eastern Ontario
Organismes subventionnairesnon disponible
Mots-clésAnterior cruciate ligamentSagittal planeACL injuryKnee JointElectromyographyCohortBiomechanicsJoint stiffnessJoint hypermobility

Résumé

récupéré en direct d'OpenAlex

Adolescent females are the most likely cohort to sustain non-contact ACL injuries, which primarily occur during multi-planar loading tasks. Despite the sex-bias and the injury's impact on quality of life and long-term health consequences, there is limited research investigating knee stabilisation strategies among healthy and ACL-injured adolescent males and females. The purpose of this study was to compare and contrast adolescents with and without ACL injury in their muscle activation patterns during higher risk tasks. Thirty-six (females, n=20) adolescents with confirmed ACL ruptures (ACL-deficiency) and 36 (females, n=20) matched controls with no history of lower extremity injuries were included in this analysis. All ACL-deficient participants were scheduled to have ACL-reconstruction surgery. Participants provided consent and completed a set of self-assessments and surveys in their preferred language (English or French: the Pedi-IKDC, the HSS Pedi-Fabs, Tegner activity scale, and the Tanner Stages Classification form. Surface electromyography of eight lower-limb muscles in both limbs and full-body kinematics were captured during countermovement jumps (CMJs) and side-cuts. Knee stability was evaluated using co-activation indices (CIs), full muscle waveforms, knee joint flexion stiffness (KJFS) and sagittal and frontal excursions (KJSE and KJFE). KJFS was calculated as the ratio of knee joint flexion moment to the knee joint flexion angle during the impact of CMJs and side-cuts, while joint angle excursions were calculated as the difference between the peak angle during the task and the angle at impact/landing. Comparisons were determined a priori to evaluate each sex independently while evaluating for an injury effect, with a set significance level at 0.05. Statistical parametric mapping (SPM) was used to compare continuous waveform data, while discrete variables were assessed using SPSS Statistics. No significant effect of injury was observed in KJFS, KJSE, or KJFE metrics in either male or female cohorts during CMJs or side-cuts. Additionally, no significant injury effect was observed in females for individual muscles throughout either task. Only one injury effect was identified in males; males with ACL injuries had higher vastus medialis activity during the preparatory phase of the CMJ compared to their matched controls. Our results indicate that in adolescents with and without ACL injuries who can perform CMJs and side-cuts, there are limited systemic differences in knee stabilization metrics between the injured and non-injured groups. Specifically, only one difference was identified in the vastus medialis of males with ACL injuries. The higher activation levels in the medial quadriceps muscle may be a result of a compensatory strategy to generate an extensor force to perform the task. The limited effect of injury in this study may in part be due to the high level of variability in strategies used to perform tasks in this maturing population. Our findings not only challenge the reported differences related to an ACL injury among adults, but also warrant further investigations on knee stabilisation strategies in adolescents. Future research should consider larger sample sizes and analyze intra-participant variability during repeated and unanticipated movements to explore the relationship between variability and risk of initial and secondary non-contact ACL injuries among adolescent males and females.

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)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
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 enseignants0,058
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
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 Opus0,018
Tête enseignante GPT0,258
Écart entre enseignants0,240 · 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

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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