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Enregistrement W2283841347

Quantification of spine stability: Assessing the role of muscles and their links to eigenvalues and stability

2011· dissertation· en· W2283841347 sur OpenAlexaboutno aff
D.M. Ikeda

Notice bibliographique

RevueUWSpace (University of Waterloo) · 2011
Typedissertation
Langueen
DomaineMedicine
ThématiqueMusculoskeletal pain and rehabilitation
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésStability (learning theory)Eigenvalues and eigenvectorsSPINE (molecular biology)Physical medicine and rehabilitationComputer scienceControl theory (sociology)MedicineBiologyPhysicsArtificial intelligenceBioinformaticsMachine learning
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Approximately 50% - 80% of the population will experience disabling low back pain at some point in their life. Assessing and developing interventions based on “lumbar stability” and/or joint stiffness to reduce low back pain has been a common research focus. Specific focus has been on identifying which muscles influence lumbar stability/stiffness, with one argument being between focusing training on the transverse abdominis and lumbar multifidus muscles versus broader training approaches involving the entire abdominal wall and erector spinae muscles. However, there has not been research on whether pain reduction was due to increased stability/stiffness or another mechanism. The main goals of this thesis were to determine the effect of individual muscles on stability/stiffness through a two phase process. In the first phase, a model sensitivity analysis was performed to assess the interactions of variables that influence the quantification of stability. Stability was quantified via the eigenvalues (EV) of the Hessian matrix of potential energies at each lumbar level and axis of rotation, for a total of 15 EVs (3 axes of rotation x 5 joints). In phase 2, assessment of clinical interventions on patients with low back pain designed to alter biomechanics was conducted to assess factors in stability/stiffness quantification and mechanisms of action in pain modulation. More detail of the study phases are described below, in order to test the following hypotheses: \n1)\tIt was hypothesized that individual muscles affect specific EVs, but no one muscle can be associated with one EV level. \n2)\tIt was hypothesized that specific muscles do affect specific planes of stability/stiffness. \n3)\tIt was hypothesized that EVs are affected by posture. \n4)\tIt was hypothesized that overactivating muscles by increasing muscle activation to 100% MVC negatively affects the EVs. \n5)\tIt was hypothesized that the relationship between muscles and specific EVs obtained during simulation remains with real subjects performing loaded tasks. \n6)\tIt was hypothesized that coaching and cueing specific movement patterns and motor patterns would alter pain in low back pain patients. \n7)\tIf hypothesis 6 is true, then it was hypothesized that changes in pain would be reflected in changes in EVs. \nMethods for Phase 1 \nThe first phase involved a sensitivity analysis using an anatomically detailed spine model. Theoretical data including posture, motion and muscle activity were synthesized to include 23 static spine postures, including neutral, 0° - 50° flexion, 0° - 30° extension, 0° - 30° right and left lateral bend, and 0° - 40° right and left axial twist, all in increments of 10°. For each posture, all eleven muscles included in the model, some with several fascicles, were artificially activated to 50% MVC. A knockout approach ensued whereby activity in single muscles were systematically reduced to 0% MVC or increased to 100% MVC. The relationships between the 15 EVs and the changes in muscle activity and posture were assessed. This muscle knockout model was repeated with actual muscle activity values obtained from electromyographic (EMG) signals and postures obtained from four subjects who performed a walking task with a 15 kg load in each hand. \nResults for Phase 1 \n\tThe sensitivity analysis showed that the abdominal muscles contribute a greater stabilizing effect on the L4 and L5 EVs, while the multifidus and erector spinae muscles contribute a greater effect on the L1, L2 and L3 EVs. When examining the effect of muscles on a specific plane in terms of influencing stability/stiffness, it was found that the abdominal muscles contribute a greater effect on the bend axis and twist axis EVs than the flexion axis EVs, while the erector spinae muscles contribute the greatest effect on the flexion axis EVs. Posture was found to have a biologically significant effect on EVs, with the 50° flexion and 30° extension postures having the most detrimental effect in terms of compromising stability/stiffness. In addition, when there was a 10° excursion in any axis, there was little change in the EVs, while postures at angles greater than this were often associated with decreases in stability/stiffness in some EVs. Increasing the muscle activation from 50% MVC to 100% MVC did not have a large effect on most EVs, but when there was a meaningful change, as defined by a change of 10% or greater in the EV, the 100% MVC activation level always resulted in more stability/stiffness at that particular EV. Finally, using actual EMG and lumbar angle patterns resulted in similar results as the theoretical data, as expected. Interpretation of these findings is limited by the following. Even though EVs changed, there is no guarantee that the magnitude of change in one EV could be interpreted to equal a similar magnitude of change in another EV, nor may it be assumed that EVs have a linear relationship with stability/stiffness. These results suggest that when the goal is to increase lumbar stability, a neutral spine should be maintained and activating the larger abdominal muscles is more important than activating the transverse abdominis or multifidus, as proposed by some clinical groups. \nMethods for Phase 2 \n\t Four case studies of individuals with chronic low back pain were recruited from whom kinematic, kinetic and EMG data were collected in addition to a measure of pain intensity using an 11-point verbal numerical rating scale. Pain provocation tests were performed by a clinician (professor Stuart McGill) to identify the motions, postures and loads that exacerbated their pain. Then these tasks were repeated while the motion and EMG data was collected. This was followed by interventions coached by the clinician that could include the abdominal brace (stiffening the abdominal wall), latissimus dorsi stiffening, incorporating a hip-hinge motion rather than spine bending, or any combination of these. The intention of the intervention was to immediately reduce pain intensity. These tasks arranged in a repeated measures design were assessed with the anatomically detailed spine model to calculate stability/stiffness from evaluation of the 15 EVs, lumbar compression and lumbar shear forces. \nResults for Phase 2 \n\tThe results from phase 2 suggest that pain was sometimes reduced by altering motions, postures and load, but the mechanism of what proved effective and the degree of success was variable from patient to patient. In most situations, the EVs, lumbar compression forces and lumbar shear forces increased due to the intervention that was chosen. In addition, the lumbar flexion angle typically trended to a more neutral posture and in tasks where spine motion occurred, there was less spine motion when using the suggested intervention. Further, the biomechanical variable that would be expected to change based on clinical assessment did not always react in the expected way (i.e. a compression intolerant individual would be expected to have decreased compression linked with decreased pain, but this did not occur). While the stability/stiffness increased, the associated compression was tolerated suggesting that the increase in concomitant stiffness enhanced the compression load bearing tolerance. \nOverall Conclusions \n\tThis thesis showed that careful examination of the EVs did not offer substantial insight into links between changes in individual EVs and individual muscles, as muscle activity was not reflected in the EVs. Specifically, single muscles contributions were not reflected in specific EVs as was hypothesized. Further, it was difficult to interpret the EVs collectively because of the inherent non-linearity between EV magnitude and changes in muscle activation/stiffness; it can only be said that there was more or less stability/stiffness with each change in an EV, not how much. In addition, pain reduction appeared to be due to a combination of altered motions, postures and loads, but this did not result in systematic EV changes. Globally, the present work provides evidence supporting the idea that maintaining a neutral posture and activating the abdominal muscles results in less pain and larger EVs, suggesting an increase in stability/stiffness. This work has potential for informing clinicians on possible options for immediate reduction in low back pain.

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)0,002
score de la tête « metaresearch » (Gemma)0,008
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,004
Score d'incertitude au seuil0,012

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,008
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,001
Communication savante0,0010,001
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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 Opus0,019
Tête enseignante GPT0,247
Écart entre enseignants0,227 · 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'étudeSimulation ou modélisation
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
Publié2011
Routes d'admission1
Résumé présentoui

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