eHealth interventions for parents in neonatal intensive care units: a systematic review
Notice bibliographique
Résumé
BACKGROUND: As technology becomes increasingly more advanced, particularly video technology and interactive learning platforms, some neonatal intensive care units are embracing electronic health (eHealth) technologies to enhance and expand their family-centered care environments. Despite the emergence of eHealth, there has been a lack of systematic evaluation of its effectiveness thus far. OBJECTIVES: To examine the effect of eHealth interventions used in neonatal intensive care units on parent-related and infant outcomes. INCLUSION CRITERIA TYPES OF PARTICIPANTS: This review considered studies that included parents or primary caregivers of infants requiring care in a neonatal intensive care unit. TYPES OF INTERVENTION(S): This review considered studies that evaluated any eHealth interventions in neonatal intensive care units, including education (e.g. web-based platforms, mobile applications); communication (e.g. videos, SMS or text messaging), or a combination of both. Comparators included no eHealth interventions and/or standard care. TYPES OF STUDIES: Experimental and epidemiological study designs including randomized controlled trials, non-randomized controlled trials, quasi-experimental, before and after studies, prospective and retrospective cohort studies, case-control studies, and analytical cross sectional studies were considered. OUTCOMES: This review considered studies that included parent-related outcomes (use and acceptance, stress/anxiety, confidence, financial impact, satisfaction and technical issues) and neonatal outcomes (length of stay, postmenstrual age at discharge, parental presence and visits). SEARCH STRATEGY: A systematic search was undertaken across four databases to retrieve published studies in English from inception to November 18, 2016. METHODOLOGICAL QUALITY: Critical appraisal was undertaken by two independent reviewers using standardized critical appraisal instruments from the Joanna Briggs Institute System for the Unified Management, Assessment and Review of Information (JBI-SUMARI). DATA EXTRACTION: Quantitative data were extracted from included studies independently by two reviewers using the standardized data extraction tool from JBI-SUMARI. DATA SYNTHESIS: A comprehensive meta-analysis for all outcomes was not possible and data has been reported narratively for all outcomes. RESULTS: Eight studies met inclusion criteria and were included in the review. The majority of the studies were low to very low quality. The study design and type of eHealth technology examined varied greatly. There appears to be growing interest in the topic as over half of the included studies were published within the past two years. Primary findings suggest parent acceptance and use of eHealth interventions but an unclear impact on neonatal outcomes, particularly on length of stay, a commonly reported neonatal outcome. Due to the variation in eHealth interventions, and heterogeneity across studies, meta-analysis was not possible. Numerous single studies and small sample sizes limited the degree of adequate strength to determine statistical differences across outcomes. CONCLUSIONS: While heterogeneity across studies precluded meta-analysis, consistent trends across all studies examining parental acceptance of eHealth interventions indicate that parents are willing to accept eHealth interventions as part of their neonatal intensive care, suggesting that the incorporation and evaluation of eHealth interventions in the neonatal intensive care unit setting is warranted. Further high quality studies are needed with larger sample sizes to detect changes in outcomes. As eHealth intervention studies move beyond feasibility and implementation, there is a demand for randomized control trials to examine the effect of eHealth interventions on parent and neonatal outcomes compared to usual care. Future studies should consider reporting of outcomes using standardized measures which would allow comparison across eHealth interventions in subsequent reviews.
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 | 0,007 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,008 | 0,005 |
| 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) | 0,007 | 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 ».