Barriers and Facilitators of Using eHealth to Support Gestational Diabetes Mellitus Self-Management (GDM): A Systematic Literature Review of Perceptions of Healthcare Professionals and Women with GDM (Preprint)
Notice bibliographique
Résumé
BACKGROUND Gestational diabetes mellitus (GDM) is one of the most common medical complications of pregnancy. eHealth technologies are proving to be successful in supporting the self-management of medical conditions. Digital technologies have the potential to improve GDM self-management. OBJECTIVE The primary objective of this systematic literature review was to identify the views of health professionals (HPs) and women with GDM about using eHealth regarding GDM self-management. The secondary objective was to investigate the usability and user satisfaction levels of using these technologies. METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses approach (PRISMA), the search included primary papers in English on the evaluation of technology to support self-management of GDM from January 2008 to September 2021 using Medline, Cinahl, Embase, ACM and IEEE databases. The lists of references from previous systematic literature reviews, which were related to technology and GDM, were also examined for primary studies. Papers with qualitative, quantitative, and mixed methodologies were included and evaluated. The selected papers were assessed for quality using the Cochrane Collaboration’s tool, NICE clinical guidelines, the CASP Qualitative Checklist and the McGill University Mixed Methods Appraisal Tool. NVivo was employed to extract qualitative data, which was subjected to thematic analysis. Narrative synthesis was used to analyze quantitative data. RESULTS A total of 26 papers were included in the review. Six of these papers used quantitative research methodologies, 5 used qualitative, and 15 used mixed methods. Four themes were identified from qualitative data: (1) Benefits of using technology, (2) Engagement with people via technology, (3) Usability of technology, and (4) Discouragement factors for the use of technology. Thematic analysis revealed a vast scope of challenges and facilitators in the use of GDM self-management systems. The challenges included usability aspects of the system, technical problems, data privacy, lack of emotional support, the accuracy of reported data, and adoption of the system by HPs. Convenience, improved GDM self-management, peer support, increased motivation, increased independence, and consistent monitoring were facilitators to use these technologies. Quantitative data showed that there is potential for improving the usability of the GDM self-management systems. Quantitative data also showed convenience, usefulness, increasing motivation for GDM self-management, helping with GDM self-management, and being monitored by HPs were facilitators to use the GDM self-management. CONCLUSIONS This novel systematic literature review shows women with GDM and HPs encountered some challenges in using GDM self-management systems. Usability of the GDM systems was the primary challenge derived from qualitative and quantitative results, with convenience, consistent monitoring, and optimization of GDM self-management emerging as important facilitators.
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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 | 0,023 | 0,078 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,007 | 0,009 |
| Bibliométrie | 0,011 | 0,010 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».