Enrollment and dropout rates of individuals with chronic obstructive pulmonary disease approached for telehealth interventions: A systematic review and meta-regression analysis
Notice bibliographique
Résumé
Introduction: Telehealth interventions have the potential of improving health outcomes for individuals with chronic obstructive pulmonary disease (COPD). However, the precise impact of telehealth on exacerbation and hospital readmissions remains inconclusive. This lack of knowledge on the effectiveness of telehealth for COPD care might be due to lack of clarity regarding which variables are most strongly associated with enrolment and dropout rates. Objectives: Among individuals with COPD in telehealth studies, we aimed to: (1) estimate the extent to which trial-related variables are associated with enrolment and dropout rates, and identify reasons for dropouts; (2) estimate the extent to which patients-related and intervention-related variables are associated with dropout rates; (3) estimate the effect of enrolment rate and dropout rate on effect size; (4) estimate the effect of trial-related, patient-related, and intervention-related variables on effect size. Methods: A systematic literature search was conducted using four electronic databases. Two independent reviewers screened all retrieved titles, abstracts and full texts according to the inclusion criteria and extracted the data. A random-effect meta-regression analysis was conducted to estimate the overall enrolment and dropout rates, and estimated the different variables' effects on the enrolment rate, dropout rate, and effect sizes in the studies included in the review. Results: A total of 56 studies comprising 7530 participants were identified. The estimated enrolment and dropout rates were 50.3 % and 14.9 %, respectively. Trial-related variables influence enrollment and dropout rates, including RCT designs and the recruitments. The patient-related variables, including age and severity of the disease, and intervention-related variables, including the components of the intervention and mode of delivery, influence dropout rates. Studies with low dropout rates had a bigger effect size by 0.23. The main reported reasons for dropping out of the intervention were related to death (21 %) followed by lost to follow-up (14 %). Conclusion: Trial, patient, and intervention-related variables were found to influence the enrolment and dropout rates. This would help plan and develop a more appealing telehealth intervention that patients can easily accept and incorporate into their everyday lives. Registration information: International Prospective Register of Systematic Reviews (PROSPERO); ID: CRD42017078541.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,008 | 0,002 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».