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Enregistrement W7042760896

Predictive balance control during backward walking and effects of a haptic input based intervention on predictive balance control during walking

2022· dissertation· en· W7042760896 sur OpenAlexaboutno aff

Notice bibliographique

RevueUniversity Library (University of Saskatchewan) · 2022
Typedissertation
Langueen
DomaineMaterials Science
ThématiqueX-ray Diffraction in Crystallography
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésBalance (ability)GaitPower walkingPreferred walking speedDynamic balanceReliability (semiconductor)Model predictive control
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Background\nFalls are a leading cause of injuries and hospitalizations in individuals globally and in Canada.\nEven though falls can occur during any activity, a majority of falls occur during walking.\nUnderstanding and improving balance control during walking can help reduce falls. One\nway of improving balance control may be to add haptic input during walking and including\nbackward and tandem walking in gait training programs.\nPurpose\nThe overall purpose of this study was to examine the balance control and sensorimotor integration\nduring backward walking as well as study the effects of an intervention consisting of backward\nand tandem walking on balance control in healthy adults.\nMethods and results\nStudy one: Test-retest reliability, standard error of measurement, and minimal detectable\nchange were computed for spatiotemporal and balance control measures for forward, backward,\nand tandem walking for fifteen healthy adults. The results demonstrated moderate to excellent\nreliability for all spatiotemporal and balance measures but low to poor reliability for variability\nmeasures for forward, backward, and tandem walking.\nStudy two: Differences in spatiotemporal and balance control measures between forward\nand backward walking and the correlation of backward walking velocity with biomechanical\nbalance control measures during forward and tandem walking were examined in fifty-five\nhealthy adults. Backward walking was significantly different in terms of spatiotemporal and\nbalance control measures compared to forward walking. Participants walked significantly\nslower and with a significant reduction in relative double support time during backward walking\ncompared to forward walking. Step length and anteroposterior margin of stability were significantly\nreduced, and step width and mediolateral margin of stability were significantly increased\nduring backward walking compared to forward walking. Backward walking was also significantly\nmore variable compared to forward walking. Step length, step width, and anteroposterior and\nmediolateral margins of stability were significantly more variable during backward walking\ncompared to forward walking. Velocity during backward walking showed a significant positive\ncorrelation with anteroposterior margin of stability and velocity during forward walking and a\nsignificant negative correlation with step length variability during forward walking.\nStudy three: The effects of vision and haptic input added with haptic anchors during backward\nwalking was examined in 55 healthy adults. It was observed that walking backward with\neyes closed significantly changed spatiotemporal and balance control measures compared\nto walking with eyes open. Participants walked slower, with an increased amount of double\nsupport time, reduced step length, and increased step width when walking backward with\neyes closed compared to walking with eyes open. Variability of step width and margin of\nstability in the anteroposterior and mediolateral directions were also significantly higher when\nwalking backward with eyes closed. Margin of stability in the mediolateral direction was\nsignificantly lower when walking backward with the haptic anchors compared to walking\nwithout haptic anchors. An interaction between vision and haptic input revealed that step\nlength was significantly lower when walking backward using the haptic anchors compared\nto walking without haptic anchors in the eyes open condition.\nStudy four: This study examined the effects of a six-week (three days/week) intervention on\nbalance control during forward, backward, and tandem walking in a total of forty-five healthy\nadults. Fifteen participants completed the intervention using haptic anchors, another fifteen\ncompleted the same intervention without the haptic anchors, and a control group of fifteen\nparticipants did not complete the intervention. The intervention consisted of performing ten\ntrials each of backward and tandem walking with eyes closed over a distance of ten meters\nin random order at the participants’ preferred speed. During forward walking, change in step\nlength variability was significantly higher in the eyes closed condition compared to the eyes\nopen condition. During backward walking, velocity, %DS, and step length change scores\nwere significantly higher in the eyes closed condition compared to eyes open and the change\nscore for AP MOS was significantly higher in the eyes closed condition compared to the eyes\nopen condition only for the group that trained without the haptic anchors. During tandem\nwalking, change score for ML MOS was significantly lower in the eyes closed condition\ncompared to the eyes open condition. No significant effects of the intervention were observed\non any measures for forward, backward, and tandem walking except the AP MOS change\nscores in the group that performed the intervention without using the haptic anchors.\nConclusion\nThis thesis provided novel evidence on the reliability of spatiotemporal and balance control\nmeasures across three different walking styles. The findings provide support in favour of\nusing MOS measures as well as backward walking to assess mobility and integrity of the\nbalance control system. The insignificant effects of the haptic input based intervention warrants\nfurther research on the long-term use of haptic anchors to improve balance control.

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: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,682
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,002
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
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 Opus0,002
Tête enseignante GPT0,167
Écart entre enseignants0,165 · 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
Publié2022
Routes d'admission1
Résumé présentoui

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