P135 Lower limb muscle strength and balance in older adults with distal radius fracture: a systematic review
Notice bibliographique
Résumé
Abstract Background/Aims Distal radius fractures (DRF) are common fall-related fragility fractures disproportionately affecting older females. After a DRF, there is an increased risk of future fragility fractures and functional decline. Systematic review evidence shows balance and muscle strengthening exercises reduce falls in older adults. Despite this, existing DRF rehabilitation trials have mainly focused on upper limb impairments. To inform rehabilitation requirements, we aimed to 1.) compare lower limb muscle strength and balance between older adults with a DRF with age- and sex-matched controls, and 2.) synthesise lower limb muscle strength and balance outcomes in older adults with a DRF. Methods We searched Embase, MEDLINE and CINAHL (1990 to August 2021). We included randomised and non-randomised controlled trials, and observational studies, that assessed lower limb strength and/or balance in adults aged ≥50 years enrolled within one year after a DRF. Strength and balance had to be assessed using validated instrumented or physical performance measures. Two reviewers independently screened titles and abstracts, and full-text reports of potentially eligible studies. One reviewer extracted data, then checked by another. Two reviewers independently appraised studies using the Cochrane risk-of-bias tool or Newcastle-Ottawa scale. We synthesised results narratively due to heterogeneity. PROSPERO registration: CRD42020196274. Results Seventeen studies (10 case-control studies, three RCTs and four case-series) including 1112 participants (95% women) with a DRF were included. Participants’ mean age ranged from 56 to 73 years; median sample size was 80 (IQR 54-106). Eleven (65%) studies assessed lower limb muscle strength using 10 different methods. Knee extensor strength assessment was most common (5/11 studies) followed by the 30-second and five times sit-to-stand tests (3/11 studies). All studies assessed balance, using 14 different methods. Single leg balance assessment was most common (6/17 studies) followed by functional reach and postural sway (3/17 studies). 5/10 case-control studies assessed lower limb muscle strength. Two studies found cases performed worse than controls during sit-to-stand tests; three studies assessed knee extensor strength with conflicting findings. All case-control studies assessed balance, with cases demonstrating impaired balance compared to controls on some measures. 4/17 studies assessed strength and 6/17 studies assessed balance at multiple timepoints. Over time, strength progressively improved in 3/4 studies but changes in balance were inconsistent across studies. Conclusion There is some evidence that older adults with a DRF have impaired lower limb muscle strength and balance compared to age- and sex-matched controls, but findings are inconsistent. Synthesis of results was limited by heterogeneity in the design, quality, and assessment methods used in included studies. Large-scale robust case-control and/or prospective observational studies are needed to better establish the rehabilitation requirements for this population. Disclosure C. Forde: Grants/research support; CF is supported by the NIHR Biomedical Research Centre, based at Oxford University Hospitals Trust, Oxford. P.J.A. Nicolson: Grants/research support; PN is supported by a Versus Arthritis Foundation Fellowship (ref. 22428). C. Vye: None. J.C.H. Pun: Grants/research support; JP received financial support from the NMAHPs Internship Versus Arthritis (Grant reference 22082). W. Sheehan: None. M. Costa: Grants/research support; MC receives grants from NIHR and related health charities. S.E. Lamb: Grants/research support; SL receives grants from NIHR and related health charities. D.J. Keene: Grants/research support; DK is supported by a National Institute of Health Research (NIHR) Postdoctoral Fellowship (ref. PDF-2016-09-056) and by the NIHR Biomedical Research Centre, based at Oxford University Hospitals Trust.
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 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 | 0,007 | 0,038 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,009 | 0,006 |
| Bibliométrie | 0,008 | 0,010 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,001 |
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 ».