The Efficacy of mHealth Interventions in Treatment of Gestational Diabetes Mellitus: A Research Protocol
Bibliographic record
Abstract
Introduction: Gestational diabetes mellitus (GDM) is a disease characterized by dysfunctional glucose regulation resulting from issues with insulin production and/or regulation. If not controlled, GDM can have significant impacts on fetal development and may lead to complications in pregnant women. GDM is often treated with regular glycemic monitoring, dietary and lifestyle changes, and in most cases insulin injections. As a result of the number of interventions, managing GDM can add further stress to a pregnancy. In this study, we aim to investigate the effects of mobile health (mHealth) solutions on the outcomes of pregnant women experiencing GDM, and their babies. Methods: The sample population of pregnant women with GDM will be split into two groups: the control group will receive usual care for glycemic control as outlined by their endocrinologists and/or gynecologists, while the experimental group will receive care for glycemic control using mHealth interventions. Patients will be monitored on a bi-weekly basis from the time they were diagnosed with GDM to the time of the delivery of their babies. Compliance, blood glucose levels, pregnancy and neonatal outcomes, and weight gain will be monitored. A two-sample proportion test and 95% confidence interval will be generated to compare the variables between each category. Results: We anticipate that the experimental group will have higher compliance, with less emergency outpatient visits, reduced weight gain, and higher satisfaction with their intervention method. We also anticipate the same blood glucose measurements in both pre- and post-prandial states. The same maternal and neonatal post-delivery outcomes are also expected. Discussion: This study evaluates the effectiveness of mHealth interventions on glycemic control. Future research may investigate the maternal effects of stress in conjunction with diabetes, as well as evaluating existing mHealth solutions for factors such as accessibility, and available features. Conclusion: We anticipate that mHealth interventions, used alongside traditional glycemic monitoring methods, will improve the outcomes of pregnant women with GDM by reducing stress and empowering them to take control of their own treatment.
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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 | 0.038 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.042 | 0.007 |
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".