Implementation of Telerehabilitation Interventions for the Self-Management of Cardiovascular Disease: Systematic Review
Notice bibliographique
Résumé
BACKGROUND: Coronary heart disease (CHD) is a leading cause of disability and deaths worldwide. Secondary prevention, including cardiac rehabilitation (CR), is crucial to improve risk factors and to reduce disease burden and disability. Accessibility barriers contribute to underutilization of traditional center-based CR programs; therefore, alternative delivery models, including cardiac telerehabilitation (ie, delivery via mobile, smartphone, and/or web-based apps), have been tested. Experimental studies have shown cardiac telerehabilitation to be effective and cost-effective, but there is inadequate evidence about how to translate this research into routine clinical practice. OBJECTIVE: This systematic review aimed to synthesize research evaluating the effectiveness of implementing cardiac telerehabilitation interventions at scale in routine clinical practice, including factors underlying successful implementation processes, and experimental research evaluating implementation-related outcomes. METHODS: MEDLINE, Embase, PsycINFO, and Global Health databases were searched from 1990 through November 9, 2018, for studies evaluating the implementation of telerehabilitation for the self-management of CHD. Reference lists of included studies and relevant systematic reviews were hand searched to identify additional studies. Implementation outcomes of interest included acceptability, appropriateness, adoption, feasibility, fidelity, implementation cost, penetration, and sustainability. A narrative synthesis of results was carried out. RESULTS: No included studies evaluated the implementation of cardiac telerehabilitation in routine clinical practice. A total of 10 studies of 2250 participants evaluated implementation outcomes, including acceptability (8/10, 80%), appropriateness (9/10, 90%), adoption (6/10, 60%), feasibility (6/10, 60%), fidelity (7/10, 70%), and implementation cost (4/10, 40%), predominantly from the participant perspective. Cardiac telerehabilitation interventions had high acceptance among the majority of participants, but technical challenges such as reliable broadband internet connectivity can impact acceptability and feasibility. Many participants considered telerehabilitation to be an appropriate alternative CR delivery model, as it was convenient, flexible, and easy to access. Participants valued interactive intervention components, such as real-time exercise monitoring and feedback as well as individualized support. The penetration and sustainability of cardiac telerehabilitation, as well as the perspectives of CR practitioners and health care organizations, have received little attention in existing cardiac telerehabilitation research. CONCLUSIONS: Experimental trials suggest that participants perceive cardiac telerehabilitation to be an acceptable and appropriate approach to improve the reach and utilization of CR, but pragmatic implementation studies are needed to understand how interventions can be sustainably translated from research into clinical practice. Addressing this gap could help realize the potential impact of telerehabilitation on CR accessibility and participation as well as person-centered, health, and economic outcomes. TRIAL REGISTRATION: International Prospective Register of Systematic Reviews (PROSPERO) CRD42019124254; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=124254.
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 | 0,011 | 0,050 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,008 | 0,008 |
| Bibliométrie | 0,006 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| 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 ».