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Enregistrement W4416206749 · doi:10.1302/1358-992x.2025.13.123

FEMALE PATIENTS WITH END-STAGE KNEE OSTEOARTHRITIS ENGAGE IN LESS DAILY PHYSICAL ACTIVITY IN THE PRE-ARTHROPLASTY WAIT PERIOD

2025· article· en· W4416206749 sur OpenAlexaff
N. Ammoury, Stephanie Civiero, Annemarie F. Laudanski, K. Genge, Jason M. Leighton, Glen Richardson, Michael Dunbar, Janie L. Astephen Wilson

Notice bibliographique

RevueOrthopaedic Proceedings · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueTotal Knee Arthroplasty Outcomes
Établissements canadiensDalhousie University
Organismes subventionnairesnon disponible
Mots-clésOsteoarthritisKnee JointGaitActivities of daily livingKnee painRange of motionKnee flexionPhysical activityGait analysis

Résumé

récupéré en direct d'OpenAlex

Knee osteoarthritis (OA) clinical symptoms and poor joint function reduce patients’ engagement in social and physical activities (PA). Following severe knee OA treatment with knee arthroplasty (KA) surgery, most patients’ PA levels remain unchanged or decrease1. Moreover, female patients with knee OA often experience more OA progression, increased pain and reduced knee function2,3. We aimed to examine the associations between PA, knee joint gait kinematics, and pain scores in patients with end-stage knee OA pre-surgery and sex-specific differences in these outcomes. Patients with end-stage knee OA on the arthroplasty waitlist were recruited. An inertial measurement unit sensor (AX6, Axivity) was anatomically aligned and fixed to the tibia of the OA-affected limb for one week to monitor patients in vivo activity. Mean amplitude deviation calculations4 were used to define average daily step counts, and percentages of time spent sedentary, doing light PA (LPA), and doing moderate-to-vigorous PA (MVPA). A 10-camera (Sony) markerless motion capture system (Theia3D Markerless) was used to capture and model the 3D limb segment poses during walking to define 3D joint kinematics (Visual3D; C-motion). The Oxford-Knee-Score-Pain-Component Score5 was collected to classify self-reported pain. Pearson's correlations were used to examine associations between gait kinematic outcomes (speed, knee flexion and adduction angle ranges of motion (KFROM, KAROM) and averages (meanKF, meanKA)) with PA outcomes (average daily step count, %sedentary, %LPA, %MVPA). T-tests were used to examine sex differences in PA levels and pain. Longitudinal data was collected for eight participants approximately three months later. Changes in PA outcomes were examined with paired t-tests, as were correlations with baseline metrics. There were no significant correlations between baseline or change in PA outcomes with knee kinematic gait outcomes (p>0.05). Self-selected gait speed was negatively correlated with pain (i.e. lower OKS pain scores) and %sedentary, and positively correlated with %MVPA. Higher baseline pain was correlated with less increase in %MVPA from time 1 to 2 (Table 1). Female patients had worse PA outcomes and higher pain at baseline than males (p Higher average self-selected walking speed and male sex were associated with less time spent sedentary and more MVPA in pre-arthroplasty patients. Higher pain was associated with lower gait speed and the female sex, consistent with literature2,5. Surprisingly, baseline pain did not impact baseline PA outcomes, but was associated with less increase in %MVPA,with a trend to less increase in daily step counts. This suggests that patients with lower pain continue to engage more in MVPA in daily living while awaiting arthroplasty than those with higher pain. Our results did not support any significant associations between knee joint kinematics during gait and PA levels during the pre-arthroplasty period despite earlier work linking gait biomechanics to PA outcomes in a moderate knee OA population6. The differences identified between male and female patients PA outcomes are important considerations for sex-specific approaches to arthroplasty management. For any figures or tables, please contact the authors directly.

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: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,057
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
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 Opus0,009
Tête enseignante GPT0,246
Écart entre enseignants0,237 · 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
Publié2025
Routes d'admission1
Résumé présentoui

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