Real-Time Telerehabilitation in Older Adults With Musculoskeletal Conditions: Systematic Review and Meta-analysis
Notice bibliographique
Résumé
Background Real-time telerehabilitation (TR) is a new strategy for delivering rehabilitation interventions to older adults with musculoskeletal conditions, to provide continuity to conventional services and mitigate travel-related barriers. Objective We aimed to examine the effectiveness of treatment delivered via real-time TR services compared to conventional services among older adults with musculoskeletal conditions, in terms of physical performance, treatment adherence, and cost-effectiveness. Methods A literature search of randomized controlled trials (RCTs) published from January 2000 to April 2022 was conducted in six online databases: Cochrane Library, PubMed (ie, MEDLINE), PEDro, ClinicalKey, EBSCO, and ProQuest. The main eligibility criterion for articles was the use of real-time TR among older adults with musculoskeletal conditions to improve physical performance. Two reviewers screened 2108 abstracts and found 10 studies (n=851) that met the eligibility criteria. Quality assessment was based on version 2 of Cochrane’s risk-of-bias tool for RCTs, in order to assess the methodological quality of the selected articles. Results were pooled for meta-analyses, based on the primary outcome measures, and were reported as standardized mean differences (SMDs) with 95% CIs. A fixed model was used, and subgroup analysis was performed to check for possible factors influencing TR’s effectiveness based on different treatments, controls, and outcome measures. Results The search and screening process identified 10 papers that collectively reported on three musculoskeletal conditions in older adults and three types of TR programs. Aggregate results suggested that real-time TR, compared to conventional treatment, was more effective at improving physical performance regarding balance (SMD 0.63, 95% CI 0.36-0.9; I2=58.5%). TR was slightly better than usual care at improving range of motion (SMD 0.28, 95% CI 0.1-0.46; I2=0%) and muscle strength (SMD 0.76, 95% CI 0.32-1.2; I2=59.60%), with moderate to large effects. Subgroup analyses suggested that real-time TR had medium to large effects favoring the use of smartphones or tablets (SMD 0.92, 95% CI 0.56-1.29; I2=45.8%), whereas the use of personal computers (SMD 0.25, 95% CI –0.16 to 0.66; I2=0%) had no effect on improving balance and was comparable to conventional treatment. Conclusions We found that real-time TR improved physical performance in older adults with musculoskeletal conditions, with an effectiveness level equal to that of conventional face-to-face treatment. Therefore, real-time TR services may constitute an alternative strategy for the delivery of rehabilitation services to older adults with musculoskeletal conditions to improve their physical performance. We also observed that the ideal device for delivering TR is the smartphone. Results suggested that the use of smartphones for TR is driven by ease of use among older adults. We encourage future studies in areas related to rehabilitation in older adults, in addition to examination of physical performance outcomes, to gain additional knowledge about comprehensive care. Trial Registration PROSPERO CRD42021287289; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=287289
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 | 0,015 | 0,040 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,020 | 0,030 |
| Bibliométrie | 0,007 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| 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 ».