The Accuracy, Effectiveness, and Psychosocial Benefits of Continuous Glucose Monitors as Compared to Traditional Fingerstick Blood Glucose Monitoring in Type 1 Diabetic Populations: A Systematic Review
Bibliographic record
Abstract
Type 1 diabetes (T1D) management relies on accurate and consistent glucose monitoring. While self-monitoring of blood glucose (SMBG) has long been the standard for type 1 diabetes (T1D) care, continuous glucose monitoring (CGM) technologies have gained prominence due to their ability to provide real-time glucose trends. This systematic review’s objective is to analyze seventeen studies (2015-2025) to evaluate clinical and psychosocial outcomes of CGM versus SMBG in T1D, with attention to device type, accuracy constraints (exercise, medication interference), and user factors. Previous research has demonstrated the effectiveness of CGM in lowering hemoglobin A1c (HbA1c) levels and improving glycemic control, but few have reviewed holistically comparing variations in study design, response variables, and type influence outcomes. This study addresses that gap by analyzing key response variables (HbA1c, time in range, hypoglycemia, and glycemic variability), CGM accuracy metrics, study design features, and patient-reported outcomes such as behavioral changes and quality of life. The findings show that CGM use typically increased time in range by 5-10 percentage points and reduced time below range by 2-7 percentage points. HbA1c levels ranged from no change to about -0.5% over 12-16 weeks. In children with elevated HbA1c using isCGM 2.0, time below range fell by 6.4 percentage points over 12 weeks without an HbA1c change; these time below range benefits and higher testing frequency were sustained to 24 weeks after crossover to isCGM in both arms. Psychosocially, early CGM after diagnosis was widely endorsed by parents for reduced worry, better sleep, and remote monitoring benefits. Primary limitations in this review include heterogeneity in device types, populations, durations, and co-interventions. These results highlight the clinical value of CGM in improving both physiological and psychosocial outcomes. Differences in CGM technology, calibration needs, and adherence also impacted outcomes. These results reinforce the clinical value of CGMs while highlighting the need for standardized protocols and broader inclusion across demographic groups to optimize diabetes care.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".