Effectiveness of Self-Management of Blood Glucose in Improving Glycemic Control in Patients With Diabetes: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Self-monitoring of blood glucose (SMBG) is a widely used strategy in diabetes management, allowing patients to track their glucose levels and make informed decisions regarding diet, medication, and lifestyle adjustments. The effectiveness of SMBG remains debated. Structured SMBG, which involves systematic monitoring with clear guidance, has been suggested to provide greater benefits compared to unstructured SMBG. We aimed to evaluate the effectiveness of SMBG in improving glycemic control among diabetic patients. A systematic search was conducted in PubMed, the Cochrane Library, and Google Scholar. Studies were included if they examined SMBG interventions and reported glycated hemoglobin (HbA1c) as an outcome. Randomized controlled trials (RCTs) and observational studies were assessed for quality using the Cochrane Risk of Bias Tool and the Newcastle-Ottawa Scale. Meta-analysis was performed using Review Manager (RevMan, The Cochrane Collaboration, Copenhagen, Denmark) to calculate mean differences (MD) and 95% confidence intervals (CI). The comprehensive database search yielded 7,667 records, of which 22 articles were selected for review and analysis. The meta-analysis of 22 studies showed that SMBG significantly reduced HbA1c levels compared to no SMBG (MD = -0.32%, 95% CI: -0.44% to -0.20%). Structured SMBG resulted in a greater reduction (MD = -0.25%, 95% CI: -0.41% to -0.09%) compared to unstructured SMBG. In summary, we showed that SMBG is effective in lowering HbA1c, particularly when structured protocols are followed. Healthcare providers should promote structured SMBG, along with patient education, to enhance adherence and optimize glycemic control. Further long-term studies are necessary to evaluate long-term benefits.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.037 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.003 | 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".