Understanding the role of lower limb kinetic adaptations in dynamic stability during novel forward walking
Notice bibliographique
Résumé
Introduction During asymmetrical gait perturbations, adaptive alterations in spatiotemporal (i.e., step width & length) and kinematic parameters (i.e., margins of stability (MoS)) have become an important means to probe the mechanisms of stability control. Recent work has linked eccentric ground reaction force (GRFnet) control to ML instability during normal and fast paced walking, potentially yielding insight into proactive and reactive mechanisms of stability control. Using a split-belt treadmill, where gait perturbations can be administered by decoupling individual belts, this study sought to examine adaptations in the kinetic mechanisms underlying stability in gait, which have yet to be examined. The timing and magnitude of the angle of GRFnet eccentricity (θd) were examined during the double support phase to better understand how younger adult individuals modulate forces in relation to situational demands to maintain stability. Objective To examine timing and magnitude of potential proactive and reactive control indices of stability (i.e., initial (P1) and later phase (P2) GRFnet eccentricities) to better understand how individuals modulate forces to maintain dynamic stability in the presence of a novel gait pattern. Methods Whole-body kinematic and kinetic data were collected from twenty-eight young adult participants. Participants completed a 15 min split-belt protocol in which the left belt (0.75 m/s) was slower than the right belt (1.5 m/s). This continuous perturbation was used to provoke instability in which adaptation in control mechanisms could be observed during early adaptation (EA) and late adaptation (LA) time points. Step width and margins of stability were calculated, and specific focus was placed on the on angle of divergence of the net ground rection force. Two-way repeated measures ANOVAs were used to assess adaptation across time points and between individual limbs to further our understanding of dynamic stability. Results During EA participants exhibited conservative control strategies as observed by increased MoS coupled with decreased initial GRFnet (P1) eccentricity and increases in later GRFnet (P2) eccentricity, while no differences in timing were observed. Additionally, step-to-step variability increases in MoS, P1, and P2 magnitude were noted during EA. During LA individuals exhibited similar control strategies relative to baseline, demonstrated by reduced MoS and increases in P1. Further, decreases in step-to-step variability of stability control parameters were also noted during LA. Discussion Findings suggest that changes in spatiotemporal and force related control mechanisms during a continuous whole-body perturbation are requirements of stability preservation. Further, our results suggest that some ML control parameters exhibit adaptive changes, that is, over time there is a lesser reliance on reactive control measures – these results may be exclusive to a population which can offset instability by allocating control appropriately between limbs to achieve suitable maintenance of dynamic stability. Further work is necessary to examine the potential for such adaptive changes among older adults.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 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) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».