Effectiveness of continuous glucose monitoring systems on glycemic control in adults with type 1 diabetes: A systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: Continuous glucose monitoring (CGM) technologies have been advancing rapidly, but evidence on their comparative effectiveness stills limited to date yet. We conducted a systematic review and meta-analysis to evaluate and investigate the impact of CGM systems on glycemic control in adults with type 1 diabetes. Methods: We searched electronic literature databases from inception through April 30, 2025, for comparative studies investigating CGM systems with standard monitoring or different CGM technologies in adults with type 1 diabetes. Primary outcomes included HbA1c reduction, time in range (TIR), and hypoglycemia reduction. We performed random-effects meta-analyses, network meta-analysis, and subgroup analyses by baseline HbA1c and intervention duration. Evidence quality was assessed using GRADE methodology. Results: Twenty-seven studies with total of 2975 participants were included. CGM significantly reduced HbA1c compared to standard monitoring (mean difference: 0.38 %, 95 % CI: 0.49 to -0.27 %). TIR increased by 7.9 % (95 % CI: 5.8-10.0 %), representing 114 additional minutes daily in best range. Real-time CGM showed advantages over intermittently scanned CGM for TIR (+5.63 %, P-value<0.001) and hypoglycemia reduction (-1.28 %, P-value<0.001). Automated closed-loop systems achieved the highest ranking in network meta-analysis (SUCRA = 0.92). Benefits were greater among patients with higher baseline HbA1c (>8.5 %: 0.68 % reduction in HbA1c vs. <7.5 %: 0.24 % reduction in HbA1c, P-value = 0.009). Conclusions: CGM technologies significantly improve glycemic control in adults with type 1 diabetes, with greater benefits for those with higher baseline HbA1c. Advanced systems demonstrate progressively greater improvements, with automated closed-loop systems showing the strongest evidence of effectiveness. These findings support broader implementation of CGM technologies, with selection tailored to individual patient needs.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.021 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".