Influence of reactive balance training program characteristics on reactive balance control and fall risk: a systematic review and meta-analysis
Notice bibliographique
Résumé
ABSTRACT Introduction : Diverse Reactive Balance Training (RBT) programs have been developed to address age-related deterioration in reactive balance control and increased fall risk. Despite the demonstrated effectiveness of those programs, there is significant variability in intervention characteristics (e.g., type of perturbations, total volume and intensity of training) and in study findings. It is likely that intervention effectiveness depends on features of the intervention; however, little is known about the optimal way to deliver RBT. The purpose of this systematic review and meta-analysis is to determine the optimal intervention characteristics for RBT for improving reactive balance control and preventing falls in daily life. Methods : We searched MEDLINE ALL (July 2023), Embase (July 2023), Physiotherapy Evidence Database (August 2023) and Cochrane (July 2023) for randomized controlled trials of RBT that reported on a measures of reactive balance control and/or falls in daily life. Results were screened by two reviewers independently to determine eligibility. The following details were extracted: study population; intervention characteristics (number of sessions in total, duration, and frequency of sessions; type, intensity and number of perturbations; description of the control intervention; and program duration), number of participants in each group; reactive balance outcomes pre- and post-intervention, and number of falls in daily life post-intervention. Risk of bias (RoB) and certainty of evidence (GRADE) were assessed. Meta-regressions were performed to explore the influence of different study components on reactive balance control and falls in daily life. Results: After screening 7,677 records, 32 studies were included; 25 reported a reactive balance outcome, and 19 reported falls in daily life. RoB of reactive balance control revealed main concerns arising from selection of reported results (20/25). RoB of falls in daily life had high or some concerns in the measurements of the outcome (12/19) and selection of reported results (15/19). RBT programs that included manual perturbations were associated with reduced fall rates compared to the reference (waist pull perturbations; relative risk: 0.45; 95% confidence interval: [0.22, 0.91], p=0.042). There were no other significant relationships between any other training parameters and falls in daily life or reactive balance control. Quality of evidence (GRADE) was low for both reactive balance control and falls in daily life. Discussion: While there was some evidence for superiority of manual perturbations over other perturbation types for fall prevention, we were unable make any definitive conclusions regarding optimal training RBT characteristics. High variability in training protocols between studies and under-reporting of intervention characteristics prevented us from making a meaningful analysis of the existing studies. Future RBT studies should provide more detailed descriptions of training protocols and include head-to-head comparisons of different training parameters (e.g., perturbation types or intensities). RBT studies should also include outcomes for both reactive balance control and falls in daily life.
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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 | 0,019 | 0,044 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,022 | 0,039 |
| Bibliométrie | 0,007 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».