Effect of mHealth on Blood Glucose Control in Pregnancies Complicated by Diabetes: A Systematic Review
Bibliographic record
Abstract
Introduction: For women with diabetes, optimizing blood glucose is critical during pregnancy to reduce the risk of complications. Mobile health interventions contribute to improved blood glucose control among non-pregnant adults with diabetes, but their effect during pregnancy is not known. Methods: We conducted a systematic review to determine the effect of mobile health interventions on blood glucose control among women with type 1 diabetes, type 2 diabetes, and gestational diabetes mellitus during pregnancy. We searched the databases Ovid Medline, Ovid Embase, The Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov from inception to August 2020. We did not apply limitations to our search. We also examined grey literature and reviewed the reference lists of relevant articles. Studies were eligible for inclusion if they used a randomized controlled trial to determine the effect of mobile health on blood glucose control among women with type 1 diabetes, type 2 diabetes, or gestational diabetes mellitus during pregnancy. A modified version of the Cochrane Randomized Control Trial data collection form and the Template for Intervention Description and Replication checklist guided data collection. We used the Cochrane Risk of Bias 2.0 tool and the Grading of Recommendations Assessment, Development, and Evaluation approach to assess the risk of bias and certainty of the evidence, respectively. Cochrane guidelines for Synthesis Without Meta-analysis informed data analysis. Results: We included four randomized controlled trials on the effect of mobile health as compared to usual care on blood glucose control among women with gestational diabetes mellitus. Discussion: Only one of the four trials reported a positive effect direction, while the remaining studies reported negative or conflicting/unclear effects. The certainty of the evidence was low. Conclusion: Mobile health may have little to no effect on blood glucose control among women with gestational diabetes mellitus. Our synthesis revealed non-significant results and the certainty of evidence was low. However, as there is a current scarcity of randomized controlled trials, future studies are warranted to explore this topic, particularly given the emphasis on virtual healthcare as a result of the COVID-19 pandemic.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".