Barriers to and Facilitators of Using eHealth to Support Gestational Diabetes Mellitus Self-management: Systematic Literature Review of Perceptions of Health Care Professionals and Women With Gestational Diabetes Mellitus
Bibliographic record
Abstract
BACKGROUND: Gestational diabetes mellitus (GDM) is one of the most common medical complications during 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 regarding the use of eHealth for GDM self-management. The secondary objective was to investigate the usability and user satisfaction levels when using these technologies. METHODS: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach, 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 tool, National Institute for Health and Care Excellence clinical guidelines, Critical Appraisal Skills Programme Qualitative Checklist, and McGill University Mixed Methods Appraisal Tool. NVivo (QSR International) was used to extract qualitative data, which were subjected to thematic analysis. Narrative synthesis was used to analyze the quantitative data. RESULTS: A total of 26 papers were included in the review. Of these, 19% (5/26) of studies used quantitative research methodologies, 19% (5/26) used qualitative methods, and 62% (16/26) used mixed methods. In all, 4 themes were identified from the qualitative data: the benefits of using technology, engagement with people via technology, the usability of technology, and discouragement factors for the use of technology. The 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. It also showed that 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 systems. CONCLUSIONS: This novel systematic literature review shows that HPs and women with GDM encountered some challenges in using GDM self-management systems. The usability of 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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".