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Enregistrement W2789034717

Exploring the Utility of Inter-Segmental Coordination to Assess Movement Competency During Lifting Tasks

2018· dissertation· en· W2789034717 sur OpenAlexaboutno aff
Claragh E. E. Pegg

Notice bibliographique

RevueUWSpace (University of Waterloo) · 2018
Typedissertation
Langueen
DomaineNeuroscience
ThématiqueMotor Control and Adaptation
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMovement (music)PsychologyCognitive psychologyPhysical medicine and rehabilitationMedicineArtAesthetics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Pre-employment screens are used within the hiring process to determine the hiring or placement of employees in the workplace. It is important that such screens adequately replicate or generalize to the work of interest. The objective of this study was to determine if individuals move similarly in the Epic Lift Capacity (ELC) test, a common pre-employment screen, compared to how they move when lifting during a long-duration work simulation, where movement was characterized as inter-segmental coordination. Twenty participants (7 males, 13 females) performed the ELC test, which uses a psychophysical approach to determine a participant’s perceived maximum lift capacity, proceeded by a 90-minute work simulation. Using motion capture data lumbopelvic, hip, and knee Relative Phase Angles (RPAs) were calculated using trunk and lower limb segment angles and velocities. The Mean Absolute Relative Phase (MARP) was calculated to quantify the overall coordination pattern of each joint in each trial, while the deviation phase (DP) was calculated to quantify variability in joint coordination within a trial. Measures of coordination were calculated and averaged over the first three lifts (initial lifts) and last three lifts (final lifts) of the 90-minute work simulation and were compared to the coordination measures associated with the lifts in the ELC test. Height (floor-shoulder, floor-knuckle, and knuckle-shoulder) and load (4.54 kg, and 75% of a participant’s maximum) were controlled across conditions. Results from this study show that when considering coordination broadly across all joints, coordination was most in-phase and least variable at the lumbopelvic joint relative to the more distal joints. Also, no differences were found between the ELC test and work trials at the lumbopelvic joint, suggesting that movement, at least about the lumbopelvic joint, was controlled similarly in the ELC test and simulated work trials. Considering the high incidence rate of lower back injuries in the workplace (Statistics Canada, 2014), investigation into the stability and coordination of movements at the lumbopelvic joint is of interest, and it is reassuring the lumbopelvic motion is similarly controlled in work as it is when performing the ELC. In contrast, at the hip and knee the coordination patterns were generally less in-phase (higher MARP) and showed more variability (higher DP) during the ELC test compared to both the initial and final lifts; however, differences at the knee appeared to be modulated by both height and load. In contrast, lumbopelvic joint coordination only changed between the initial and final lifts within the 90-minute simulation, as the final lifts were less in-phase than in the initial lifts. The coordinative changes seen in this study may reflect functional organismic and task constraint differences between the tasks. Functional organismic constraints, such as fatigue or boredom may have resulted in coordinative changes over time in the work simulation, whereas task changes, such as task goal or objective, may have resulted in coordinative changes in the ELC test compared to the work simulation. Due to these apparent coordinative differences with changes in the participant and task, movement may be influenced by psychological, physical, and environmental factors, acting as constraints by altering movement outcomes (Glazier, 2017; Newell, 1986). These constraints may be important to incorporate into the future design and use of pre-employment screens when movement strategy is of importance, as changes in coordination did occur in this study, with small changes in objectives or over time, despite identical structural environmental design.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut 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 enseignants0,002
Score d'incertitude au seuil0,005

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
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)0,0010,000

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 Opus0,058
Tête enseignante GPT0,239
Écart entre enseignants0,181 · 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 source (Gemma direct ou Codex distillé), 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

Citations0
Publié2018
Routes d'admission1
Résumé présentoui

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