MétaCan
Menu
Retour à la cohorte
Enregistrement W4376637285 · doi:10.1093/milmed/usac406

Comparison of In-service Reduced vs. Full Torso Coverage Armor for Females

2023· article· en· W4376637285 sur OpenAlexafffundabout
Rebecca Wendland, Linda Bossi, Edward T. Nakaza, Michele Oliver

Notice bibliographique

RevueMilitary Medicine · 2023
Typearticle
Langueen
DomaineHealth Professions
ThématiqueOccupational Health and Performance
Établissements canadiensDefence Research and Development CanadaUniversity of Guelph
Organismes subventionnairesNatural Sciences and Engineering Research Council of CanadaMitacsDefence Research and Development Canada
Mots-clésTorsoMedicineBiomechanicsKinematicsVESTPhysical medicine and rehabilitationPhysical therapySimulationAnatomyEngineeringStatisticsMathematics

Résumé

récupéré en direct d'OpenAlex

INTRODUCTION: Body armor and torso-borne equipment are critical to the survivability and operational effectiveness of a soldier. Historically, in-service designs have been predominantly designed for males or unisex, which may be disadvantageous for females who are shaped differently and, on average, smaller in stature and mass than their male counterparts. This study assesses the biomechanical and performance impact of two Canadian in-service armors and fighting load conditions on females. MATERIALS AND METHODS: Four tasks (i.e., range of motion, treadmill march [×2], and a wall obstacle) were performed in a Baseline condition and two in-service torso-borne equipment conditions; the full torso coverage (FTC) condition has full upper torso soft armor with the fighting load carried in a separate vest, while the reduced coverage (RC) has a plate carrier with fighting load integrated into the armor carrier, bulk positioned higher, and less torso coverage. Both used identical combat loads and front and back armor plates. Trunk range of motion, march lower limb kinematics, march shoulder and hip skin pressures, perceived discomfort after the march, and time to traverse a wall obstacle were captured. Data were collected to assess the biomechanics and usability of the systems for eight females, representative of military recruits. Linear mixed-effects models were created, and analysis of variances (ANOVAs) were then performed on all the outcome measures (P < .05). Tukey's post-hoc procedures were performed when appropriate (P < .05). RESULTS: There were significant differences between the RC and FTC for the sit and reach test (P < .001), lateral bend test (P < .001), and wall traverse time (P < .01). In all cases, the RC outperformed FTC. There were no differences between the two in-service conditions with respect to hip, knee, and ankle flexion/extension. The RC average skin pressure was higher than the FTC at the left and right shoulders by 103% and 79%, respectively, and peak skin pressure at the left shoulder by 75%. Both in-service conditions showed decrements in performance from Baseline for sit and reach (P < .001), lateral bend (P < .001), and peak hip and knee flexion (P < .01) with the FTC showing decreases in trunk rotation (P < .001) and wall traverse time (P < .01). CONCLUSIONS: Improved outcomes for the RC can be attributed to design differences. The lower placement of bulk in FTC may act as a physical barrier during range of motion tasks and the wall obstacle. The presence of shoulder caps on FTC provides another physical barrier that likely impedes full movement through the arms and shoulders. While the narrower shoulder straps of the RC remove the barrier, it causes more concentrated skin pressures on the shoulder that can lead to injury. The results suggest that the RC offers a potential for increased operational effectiveness in females (and potentially for males) compared to the FTC system. Shoulder pressure, an important predictor of discomfort and injury, is the only measure for which FTC outperformed the RC. Future torso-borne equipment designs targeting this outcome measure could help increase the effectiveness of the RC and other similar systems that reduce torso coverage, though survivability implications must also be considered.

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 candidatesaucune
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 enseignants0,107
Score d'incertitude au seuil0,538

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
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 Opus0,229
Tête enseignante GPT0,526
Écart entre enseignants0,298 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
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

Citations4
Publié2023
Routes d'admission3
Résumé présentoui

Explorer davantage

Même revueMilitary MedicineMême sujetOccupational Health and PerformanceTravaux en français237 207