Digital health interventions to improve recovery for intensive care unit survivors: A systematic review
Notice bibliographique
Résumé
OBJECTIVE: Recovery models of care for intensive care unit (ICU) survivors are limited by availability, accessibility, and efficacy. Digital health interventions represent an alternative mode of service delivery. The primary aim of this systematic review was to describe implementation factors (Reach, Effectiveness, Adoption, Implementation, and Maintenance) for digital health interventions for ICU survivors. The secondary aim was to describe any effect on patient-reported health outcomes. DATA SOURCES: A systematic search of Medical Literature Analysis and Retrieval System Online (MEDLINE), Excertpa Medica Database (EMBASE), Cumulative Index of Nursing and Allied Health Literature (CINAHL), and Cochrane Central Register of Systematic Reviews (CENTRAL) databases was undertaken in March 2023. STUDY SELECTION: Two independent reviewers screened abstracts and full texts against eligibility criteria. Studies of adult survivors with any post-ICU discharge care, delivered via a digital mode, were included. Studies were excluded if published before 1990 or not in English. DATA EXTRACTION: Quantitative data were extracted using predefined data fields. Risk of bias was assessed using the Newcastle-Ottawa Scale and Cochrane Risk of Bias Tool 2.0. Implementation factors were reported according to the Reach, Effectiveness, Adoption, Implementation and Maintenance framework. DATA SYNTHESIS: A total of 6482 studies were screened. Ten studies, with 686 participants, were included. Implementation factors were reported in all studies. Acceptability (reported in six studies) was high, with high satisfaction and usability scores, defined a priori by investigators. Eight studies reported intervention adherence rates between 46% and 100%. Nine studies report final outcome measurement retention rates up to 12 months, between 52% and 100%. Five studies included the primary outcome as the difference in a patient-reported health outcome. Appraisal of efficacy and digital health literacy was limited due to substantial methodological variation and a lack of reporting in included studies. There was some risk of bias in 50% of studies. CONCLUSIONS: Digital health interventions can be successfully implemented for critical care survivors and have varying intervention adherence and retention rate success. To broaden reach, future research should include cultural diversity and investigate digital health access, literacy, and cost-effectiveness. INTERNATIONAL PROSPECTIVE REGISTER OF SYSTEMATIC REVIEWS REGISTRATION: CRD42022348252.
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 | 0,000 | 0,076 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,006 | 0,005 |
| Bibliométrie | 0,001 | 0,001 |
| 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 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 ».