The Value of Strength as a Predictor of Musculoskeletal Injury in Canadian Armed Forces Basic Infantry Candidates
Notice bibliographique
Résumé
INTRODUCTION: Musculoskeletal injuries (MSKI) impact military organizations by threatening their operational readiness, warranting investigation into relevant factors to inform risk reduction strategies. While several self-reported and physical performance measures have been associated with MSKI among military personnel, few have been validated and none have been reported in Canadian basic infantry candidates. The purpose of this study was to investigate associations between self-reported and physical performance measures and MSKI, and determine their validity as predictors of MSKI, in Canadian basic infantry candidates. METHODS: This was a planned secondary analysis of a study tracking MSKI at a basic infantry training facility in Ontario, Canada approved by Defence Research & Development Canada. Before the basic infantry training, consenting candidates completed a baseline testing session including self-reported questionnaires, measures of anthropometry, and physical performance previously associated with MSKI (ankle dorsiflexion test, Y-Balance Test, Isometric Mid-Thigh Pull, and the Fitness for Operational Requirements of CAF Employment (FORCE) evaluation). All MSKI reported by candidates were diagnosed by licensed healthcare providers. From a total sample of 129 candidates, 76% (n = 98) were used to determine any associations between baseline testing variables and MSKI and to develop a predictive model (Development Sample), while 24% (n = 33) were used to offer preliminary validation of the same predictive model (Validation Sample). The binary logistic regression and independent sample t-testing determined independent associations with MSKI in the Development Sample. All continuous variables and dichotomous variables previously associated with MSKI risk (Smoker Yes/No, previous history of MSKI, and physical inactivity) were entered into a backward stepwise logistic regression analysis to assess the predictive association with MSKI incidence in the Development Sample. The regression model was then applied to the Validation Sample. RESULTS: A total of 35 MSKI were diagnosed by Health Services Centre staff. The majority of the MSKI were acute (63%), sustained to the hip, knee, and ankle (74%). The most common diagnoses were strains and sprains (71%). Uninjured participants performed significantly better on the Relative Isometric Mid-Thigh Pull, FORCE 20 mR, FORCE ILS, and FORCE Estimated VO2peak compared to injured participants. Logistic regression analysis showed that the only variable with significant independent association with diagnosed MSKI incidence was self-reported previous history of MSKI. However, the backward stepwise logistic regression analysis retained self-reported previous history of MSKI, FORCE SBD, FORCE Estimated VO2peak, and Isometric Mid-Thigh Pull Peak Force as predictors of MKSI. The logistic regression model including these variables could predict MSKI with an accuracy of 79% in the Development Sample and 67% in the Validation Sample. CONCLUSION: This study provides preliminary support for the value of measures of absolute muscular strength and cardiorespiratory fitness as predictors of MSKI in Canadian basic infantry candidates. Given the associations between physical performance measures and MSKI, and their necessity during occupational tasks, it is recommended that Canadian basic infantry training facilities integrate resistance training with external loads to best prepare their candidates to meet their occupational demands and potentially minimize MSKI. Further investigations to confirm the predictive capacity of these variables in a larger sample across additional facilities are warranted.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 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) | 0,000 | 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 tête enseignante, 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 ».