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)
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".