Optimizing Glycemic Control Using Continuous Glucose Monitoring: An Umbrella Review of Systematic Reviews and Meta-Analysis

Abstract

Background:

Continuous glucose monitoring (CGM) is a wearable medical device that continuously tracks blood glucose levels through a small sensor implanted typically on the abdomen, arm, or buttocks of the patient with diabetes to ensure real-time tracking of blood glucose. Many systematic literature reviews (SLRs) explored the efficacy of CGM versus self-monitoring of blood glucose (SMBG) in different patient populations.

Objective:

To conduct an umbrella review of existing SLRs and perform a meta-analysis of their underlying primary studies to compare CGM versus SMBG in terms of glycated hemoglobin (Hb1Ac).

Methods:

This is an umbrella review (overview of reviews) of studies assessing CGM efficacy compared with SMBG. PubMed, EMBASE, and Web of Science databases were searched systematically between January 2000 and February 2024. To avoid double counting of overlapping data across reviews, quantitative synthesis was conducted at the level of the primary studies identified within the SLRs. Data extracted included HbA1c measurements, population characteristics (diabetes type/age category/Insulin regimen), study characteristics (study design/follow-up duration), and device type were collected. AMSTAR-2 (A MeaSurement Tool to Assess systematic Reviews) checklist and Grading of Recommendations Assessment, Development, and Evaluation (GRADE) tool were used to appraise the quality of the included SLRs. Effect sizes with corresponding 95% confidence intervals (CIs) of the individual included studies of HbA1c were synthesized using the DerSimonian-Laird method with random effects model. Meta-regression was performed to explore the impact of different variables on the treatment effect.

Results:

Forty SLRs were included in the review with a total of 78 261 patients. Continuous glucose monitoring achieved greater reduction in HbA1c than in the control group (absolute mean difference: 0.31% (95% CI 0.26%-0.36%). While the direction of effect favored CGM across subgroups, high unexplained heterogeneity was observed (I² = 89.7%).

Conclusion:

This is the first overview of SLRs involving quantitative primary-level meta-analysis of studies of patients with diabetes of all ages and all CGM devices. While CGM devices show a significant overall benefit in reducing HbA1c, the high unexplained heterogeneity suggests that results should be generalized with caution, as the degree of benefit may vary across different clinical settings.

Keywords

continuous glucose monitoring diabetes mellitus blood glucose self-monitoring

Background

The last two decades have seen a wide use of digital health technologies for the management of Diabetes Mellitus. Devices such as insulin pumps, sensors, glucometers, and insulin pens not only help patients control their disease but also provide health care providers with accurate record of patient’s health outcomes.^1,2

Continuous glucose monitoring (CGM) is a clinical process that tracks blood glucose levels continuously through a continuous glucose monitor, a medical device consisting of a sensor implanted in the patient’s body, a transmitter, and a receiver or smartphone application. A constant stream of glucose readings is sent wirelessly from the sensor to the monitor or a compatible mobile device. There are two main types of CGM systems available: intermittently scanned CGM (isCGM)—often referred to as flash CGM—and real-time CGM (rtCGM). Flash CGM (isCGM) systems require the user to manually scan the sensor with a reader or smartphone to obtain glucose data, whereas rtCGM systems automatically transmit data at regular intervals without needing for active scanning by the patient. Compared to traditional self-monitoring of blood glucose (SMBG), both CGM systems are considered more convenient and provide continuous data streams. While both SMBG and CGM allow patients to adjust diet and insulin dosing based on current readings, CGM systems offer the added benefit of visualizing glucose trends and the speed of fluctuations (rate-of-change) enabling more proactive and precise glycemic management. Continuous glucose monitoring systems can be linked to insulin pumps as part of an automated insulin dosing systems.^3,4

Systematic literature review (SLR) is a type of research that involves systematic rigorous synthesis of evidence from published randomized controlled trials (RCTs). It is widely used in health care to assess the benefits of health care interventions and to inform reimbursement and policy decisions.^5,6 Several SLRs over the past two decades attempted to compare various CGM systems to current practice in different patient groups and settings with inconsistent results. Therefore, there is a need for an overview of reviews to systematically aggregate all the existing evidence previously generated from SLRs exploring the benefits of CGM systems in diabetes.⁷ In addition to summarizing review-level conclusions, we synthesized data from primary studies of the included SLRs. By performing a de novo meta-analysis of the original studies using uniform outcomes—an approach previously validated in similar overviews—we aim to provide a more robust and granular estimate of efficacy while avoiding the statistical bias inherent in overlapping review populations.⁸ The gold standard in assessing long-term glycemic control is glycated hemoglobin (HbA1c) level. Therefore, in this analysis, HbA1c will be the main outcome of efficacy.⁹ The specific objective of this overview is to aggregate the results of existing SLRs comparing the efficacy of CGM systems to SMBG approach in patients with diabetes of different characteristics.

Methods

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Preferred Reporting Items for Overviews of Reviews (PRIOR) statement are followed.^10,11 The study was conducted based on a protocol that was previously registered and published.^12,13

Data Search

A systematic literature search was conducted between January 2000 and February 2024 using three databases: PubMed, EMBASE, and Web of Science. Our search focused on full-text systematic reviews of comparative studies that assess the efficacy of CGM compared with SMBG in patients with diabetes mellitus. In addition, we searched systematic reviews registries and databases such as PROSPERO, the Cochrane Database of Systematic Reviews, and the JBI database to identify additional reviews. In addition, we searched the bibliographies of the included systematic reviews for additional records. Our search strategy excluded narrative and scoping reviews, and primary research. Gray literature (such as conference abstracts, dissertations, and government reports) was not searched. Details of the search are available in Supplemental Table A.

Inclusion/Exclusion Criteria

Patients with diabetes mellitus (type 1 diabetes and type 2 diabetes) involved in the primary studies of the identified SLRs, age, and gender were included. Gestational diabetes or diabetes developed during pregnancy were excluded. The SLRs must include comparative studies (RCTs and non-randomized controlled trials (NRCTs)) with two arms: CGM and SMBG. The primary comparative studies should include HbA1c as an outcome. Studies assessing practice-based professional CGM or with follow-up time less than eight weeks were excluded. In addition, studies published in languages other than English, single-arm studies or studies with different treatment regimens in each arm were excluded.

Two authors (HA and MA) independently screened the title/abstract and the full text of the retrieved papers to identify the eligible SLRs based on predetermined inclusion/exclusion criteria. Disagreements over the inclusion of studies were resolved by consensus after discussion.

Quality Assessment

Two reviewers (HA and HM) independently used the AMSTAR-2 (A MeaSurement Tool to Assess systematic Reviews) checklist to assess the methodological quality of the included SLRs.¹⁴ In addition, the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) system was used to assess the quality of evidence.¹⁵ GRADE system uses five domains to appraise the quality of evidence: risk of bias (RoB), inconsistency, imprecision, publication bias, and indirectness. The overall quality of evidence was downgraded in the RoB domain if it was more than 75% following the methodology of Motahari-Nezhad et al.¹⁶ Since our study is attempting to pool the evidence from the individual primary studies, it was essential to evaluate the quality of the included studies. Therefore, we performed a de novo quality assessment of the underlying primary studies to ensure the rigor of our quantitative synthesis. If RoB was not reported, it was assessed for the primary studies using the Cochrane Risk of Bias tool (version 2) (RoB-2) for RCTs and ROBINS-1 tool for NRCTs.^17,18 Potential publication bias was assessed through a visual inspection of the funnel plot to evaluate the symmetry of the distribution of primary studies. Recognizing the inherent subjectivity of visual inspection, we supplemented this with the Egger regression asymmetry test to provide an objective statistical measure of small-study effects (P < .10 indicating significant bias).

Data Extraction

Several SLRs were found to overlap in terms of the included primary studies which may lead to double counting and overestimating the statistical weight of the overlapped primary studies. To eliminate this kind of bias, it was necessary to collect data directly from individual primary studies identified within the reviews. To assess the extent of overlap between primary studies included in the SLRs, a citation matrix was created to map out underlying primary studies to the systematic reviews (Supplemental Table B). For this reason, data extraction was done in two steps: one for the SLRs variables and one for the unique primary studies included in the SLRs. For the rest of this article, we refer to systematic reviews as “SLRs” or “reviews” while we refer to primary studies as “studies” or “primary studies.”

For SLRs, data extraction included the following information: PICOT (Population: type of diabetes/age category, Intervention: CGM type/insulin regimen, Comparator: SMBG/insulin regimen, Outcome: HbA1c change, Time: follow-up duration). For primary studies, extracted data included: author, year, PICOT, study design, sample size, mean baseline data (HbA1c, age, male/female ratio), study duration, and insulin regimens used. Whenever a predictor had a missing value, the value was imputed, and sensitivity analysis was conducted to evaluate the significance of the missing values on the study findings.

Effect size is the difference between groups in change of HbA1c from baseline to endpoint. Therefore, the absolute mean difference of HbA1c (with corresponding 95% CI) was extracted from the primary studies. When reported in mmol/mol, values were converted to percentage of total hemoglobin using the standard International Federation of Clinical Chemistry and Laboratory Medicine-to-National Glycohemoglobin Standardization Program master equation.¹⁹ To calculate the absolute mean difference—when it was not reported—authors extracted raw data from the included studies including number of participants, baseline and endpoint mean readings of HbA1c, and standard deviation (SD) in each arm.

When SD for changes from baseline measurements was not reported, it was calculated from the reported standard error, CI, and P-value. However, if the information reported was not sufficient, authors of the studies were emailed. If data were not obtained, we followed the Cochrane Handbook for Systematic Reviews of Interventions, calculating the change-from-baseline SD using pooled correlation coefficients derived from the studies that provide complete data.²⁰ To address potential uncertainty associated with these estimations, a sensitivity analysis was performed using a range of correlation coefficient values. This was done to ensure that the imputed values did not significantly shift the pooled effect size or alter the robustness of our conclusions.

Data Synthesis

The unit of analysis for this meta-analysis was the primary study. Given the diversity of the studies enrolled in the review, random-effects meta-analysis approach using the DerSimonian-Laird method was selected to analyze the change in HbA1c.²¹ Stata 17.0 software package (Stata Corporation, College Station, TX, USA) was used to conduct data analysis and management. Cochran’s Q test and the I² statistic were used as measures of heterogeneity between individual studies.²⁰ Egger’s regression asymmetry test was used to test small-study effect (P ≤ .10).²²

Subgroup Analysis

Univariable and multivariable meta-regression analyses were performed to explore the effect of the following explanatory variables on the effect size: diabetes type, type of CGM used (rtCGM/isCGM), different follow-up periods, insulin dosing, and patient’s age. Understanding the magnitude of differences of the response of different patient groups to CGM may inform reimbursement decision-making and guide policy development by identifying the characteristics of those who will benefit the most from using CGM systems.

Results

Study Selection

As shown on PRISMA chart (Figure 1), a total of 660 papers obtained through database search. The two investigators screened the title/abstract of the retrieved papers and agreed to exclude 249 papers due to duplication, 351 for not meeting PICOT criteria. After full-text screening by both reviewers, 20 papers were excluded because the intervention, population, or study design did not meet the inclusion criteria, or the underlying primary studies were ineligible (Supplemental Table C). Finally, 40 systematic reviews met the predefined inclusion criteria. From these 40 SLRs, we identified the underlying primary studies for inclusion in our quantitative synthesis.

Primary Studies and Patient Characteristics

Thirty-three of these reviews included meta-analysis while seven reviews did not include meta-analysis. The SLRs were published between 2008 and 2024. Seventeen reviews involved type 1 diabetes mellitus patients only, nine SLRs involved type 2 diabetes mellitus only and 14 SLRs involved both type 1 diabetes/type 2 diabetes. The number of primary studies included within each SLR ranged between 5 and 34 studies. Most SLRs received no funding (n = 28), some received funding from research institutes (n = 10), and only two SLRs were funded by industry. Of 40 SLRs, 34 SLRs concluded that CGM system is more effective in reducing HbA1c compared with SMBG among patients with diabetes. The characteristics and the primary findings of the included SLRs are summarized in Table 1. Following the removal of overlapping records and studies that did not meet our primary-level inclusion criteria (eg, single-arm or unavailability of full text), a total of 78 unique primary studies were included in the meta-analysis. These included 70 RCTs and eight observational studies. Included primary studies were published between 2001 and 2023. The unit of analysis for all subsequent results refers to these 78 primary studies, representing a total of 78 261 participants. The participants in ten studies were pediatric patients only, five studies included only adolescents, in 54 studies, the participants were adults, two studies included only elderly patients, and seven studies included all age categories. The type of CGM was rtCGM in 56 studies and isCGM in 22 studies. Majority of the studies (n = 47) included only type 1 diabetes, some involved only type 2 diabetes (n = 22), and the remaining studies included both type 1 diabetes and type 2 diabetes (n = 9). The follow-up duration of the studies ranged from 7 to 96 weeks. Treatment regimens included continuous subcutaneous insulin infusion (CSII) (n = 10), multiple daily injection insulin (MDI) (n = 24), both CSII and MDI (n = 38), non-insulin regimens (n = 3) and not specified (n = 3). Continuous glucose monitoring devices used in the primary studies were Dexcom G4 CGM System (Platinum), Dexcom G5 CGM System (mobile), Medtronics Enilite Sensor, Medtronic MiniMed CGM system, Medtronics Guardian REAL-Time, Medtronics MiniMed 640G with SmartGuard, FreeStyle Libre, and FreeStyle Navigator.

Table 1.

Basic Characteristics of Included Systematic Reviews.

Author	Studies and participants	PICOT	AMSTAR-2 score	GRADE score	Funding	Findings of meta-analysis	Author’s conclusion
Wang et al²³	10 RCTs/5 crossover studies n = 2071	P: type 1 diabetes adults (on intensive insulin therapy) I: CGM C: SMBG O: HbA1c, hypoglycemia, and diabetic ketoacidosis T: 8 weeks or more	Critically low	Moderate	No	WMD = −2:69 (95% CI −4.25 to −1.14) and P < .001	CGM is associated with lower blood glucose levels than the traditional SMBG
Aggarwal et al²⁴	13 RCTs/2 NRCTs n = 65 402	P DM I: CGM C: SMBG O: HbA1c T: NR	Critically low	Moderate	No	No MA	CGMS is associated with a valuable benefit in clinical outcomes and cost management
Teo et al²⁵	15 RCTs/7 cros-sover n = 2149	P: type 1 diabetes -All ages (on intensive insulin therapy) I: CGM C: SMBG O: HbA1c, hypoglycemia, and diabetic ketoacidosis T: Any	Critically low	High	No	MD = −2.46 mmol/mol (−0.23%) (95% CI −3.83, −1.08), Z = 3.50, P = .0005)	CGM is superior to SMBG among individuals with type 1 diabetes, especially in those with uncontrolled glycemia
Thomas et al²⁶	5 RCTs n = 735	P: type 1 diabetes adults (on intensive insulin therapy) I: rtCGM C: SMBG O: HbA1c T: NR	Critically low	Low	NIHR	SMD: 0.62 (95% CI 0.79-0.45)	CGM and CSII are superior to SMBG and MDI in adults with type 1 diabetes
Kłak et al²⁷	11 RCTs n = 1228	P: type 1 diabetes Adults (with an HbA1c level of at least 7.5%) I: rtCGM C: SMBG O: emotional well-being, HbA1c T: 8 weeks or more	Low	Moderate	No	Cohen’s d: –0.24 (95% CI −0.57 to 0.09).	CGM has advantage over SMBG in adults with type 1 diabetes and improve HbA1c levels
Dicembrini et al²⁸	23 RCTs n = 2252	P: type 1 diabetes I: CGM or CGM + CSII C: SMBG or SMBG + MDI O: HbA1c T: 12 weeks or more	Critically low	Moderate	No	MD= −0.24 (− 0.34 to 0.13)	CGM is superior to SMBG in glycemic control and severe hypoglycemia in patients with type 1 diabetes
Castellana et al²⁹	12 RCTs n = 2252	P: type 1 diabetes/type 2 diabetes I: rtCGM C: SMBG O: HbA1c T: 8 weeks or more	Critically low	Moderate	No	WMD= −0.26% (95% CI −0.43 to −0.09; I² = 78%)	FGM is effective in achieving glycemic control
Gordon et al³⁰	2 RCTs/32 NRCTs n = 5466	P: type 1 diabetes I: rtCGM C: SMBG O: HbA1c T: 8-104 weeks	Critically low	Low	Abbott Diabetes Care	MD: 0.41% (95% CI 0.51-0.31]; P < .001.	FGM is effective in reducing HbA1c patients with type 1 diabetes
Maiorino et al³¹	15 RCTs n = 2460	P: type 1 diabetes/type 2 diabetes (treated with an intensive insulin regimen) I: CGM C: SMBG O: HbA1c and TIR T: 12 weeks	Low	Moderate	No	WMD: 20.17% (95% CI 20.29-20.06), I² = 596.2%	CGM is effective in achieving targets for all included diabetes outcomes in both type 1 diabetes and type 2 diabetes
Dicembrini et al³²	5 RCTs n = 439	P: type 2 diabetes adults I: CGM or CSII C: SMBG or MDI O: HbA1c T: at least 12 weeks	Critically low	Moderate	No	MD: (−0.24 (95% CI −0.49-0.00); P = .05)	CSII, CGM, and FGM provide limited benefits compared with SMBG/MDI in insulin-treated people with type 2 diabetes
Ida et al³³	7 RCTs n = 669	P: type 2 diabetes adults I: rtCGM C: SMBG O: HbA1c, blood glucose, body weight, and hypoglycemic frequency T: NR	Critically low	Low	No	SMD, −0.35; (95% CI, −0.59 to −0.10); P = .006	CGM is effective in reducing HbA1c and hypoglycemia in patients with type 2 diabetes
Park and Le³⁴	7 RCTs/3 NRCT n = 6286	P: type 2 diabetes adults I: CGM C: SMBG O: HbA1c T: NR	Critically low	Moderate	No	0.20% (95% CI −0.31 to −0.09)	CGM is effective in achieving glycemic control in adults with type 2 diabetes
Floyd et al³⁵	14 RCTs n = 1188	P: type 1 diabetes I: CGM C: SMBG O: HbA1c T: NR	Critically low	Moderate	NIH	–0.3% (95% CI 0.4, –0.2); P < .0001]	CGM is effective in HbA1c control in type 1 diabetes
Golden and Sapir³⁶	7 RCTs n = NR	P: type 1 diabetes/type 2 diabetes (any age) I: rtCGM or CSII C: SMBG or MDI O: HbA1c and any other DM outcomes T: NR	High	Low	AHRQ	0.3 (0.22-0.0.37)	rtCGM/CSII is more effective than MDI/SMBG in HbA1c control in individuals with type 1 diabetes
Yeh et al³⁷	10 RCTs n = 1066	P: type 1 diabetes I: rtCGM C: SMBG O: HbA1c T: NR	Critically low	moderate	AHRQ	−0.26% (95% CI −0.33 to −0.19)	For glycemic control, rtCGM is superior to SMBG
Szypowska et al³⁸	7 RCTs n = 948	P: type 1 diabetes I: rtCGM C: SMBG O: HbA1c and major and minor hypoglycemic episodes T: 12 weeks or more	Critically low	Moderate	Medical University of Warsaw	0.25 (95% CI 0.34-0.17)	CGM is more effective than SMBG in patients with type 1 diabetes
Wojciechowski et al³⁹	14 RCTs n = 1268	P: type 1 diabetes I: rtCGM (on an intensive insulin regimen) C: SMBG O: HbA1c T: 12 weeks or more	Critically low	Moderate	No	WMD −0.26% (–0.34 to −0.19)	rtCGM is superior to SMBG in type 1 diabetes
Gandhi et al⁴⁰	19 RCTs n = 1801	P: type 1 diabetes/type 2 diabetes I: CGM C: SMBG O: HbA1c T: 8 weeks or more	Critically low	Moderate	No	WMD: –0.27% (95% CI −0.44 to −0.10)	CGM is effective in reducing HbA1c
Pickup et al⁴¹	6 RCTs n = 892	P: type 1 diabetes adults (intensive insulin therapy) I: rtCGM C: SMBG O: HbA1c T: 8 weeks or more	Critically low	High	The Engineering and Physical Sciences Research Council.	MD: −0.30% (95% CI −0.43 to −0.17)	CGM is effective in reducing HbA1c
Golicki et al⁴²	5 RCTs n = 151	P: type 1 diabetes (pediatrics) I: CGM C: SMBG O: HbA1c T: NR	Critically low	Low	Medical University of Warsaw	WMD = −0.02% (95% CI −0.29 to 0.25)	CGM is not superior to SMBG in children with type 1 diabetes
Janapala et al⁴³	5 RCTs n = 382	P: type 2 diabetes adults (≥19 years of age and baseline mean HbA1c ≥6.5%) I: CGM C: SMBG O: HbA1c T: NR	Critically low	Moderate	No	MD in HbA1c was −0.25 (–0.45, –0.06) (at P = .01)	CGM in type 2 diabetes significantly reduces HbA1c compared with SMBG
Poolsup et al⁴⁴	15 RCTs n = 1045	P: type 1 diabetes (pediatric)/type 2 diabetes (adults) I: CGM C: SMBG O: HbA1c T: 8 weeks or more	Critically low	Moderate	No	Type 1 diabetes (pediatric): – 0.13% (95% CI −0.38 to 0.11) Type 2 diabetes (adults): 0.31% (95% CI −0.6% to −0.02%); P = .04	CGM is not superior to SMBG in reducing HbA1c in children with type 1 diabetes CGM is superior to SMBG in adults with type 2 diabetes
Elbalshy et al⁴⁵	22 RCTs n = 2218	P: type 1 diabetes (any age on an intensive insulin therapy) I: CGM C: SMBG O: HbA1c and TIR T: at least 6 weeks for TIR and, 12 weeks for HbA1c	Low	Moderate	Dexcom	AMD: MD 95% CI −0.22% (−0.31 to −0.14), I² = 79%	CGM is effective in type 1 diabetes
Chetty et al⁴⁶	7 RCTs n = 335	P: type 1 diabetes (90% of the participants having type 1 diabetes) I: CGM C: SMBG O: HbA1c T: 4 weeks or more	Critically low	Low	No	0.22% (95% CI 0.439-0.004); P = .055	The evidence does not show that CGM is superior to SMBG in reducing HbA1c
García et al⁴⁷	18 RCTs n = 1843	P: type 1 diabetes/type 2 diabetes I: CGM C: SMBG O: HbA1c T: NR	Critically low	Moderate	Canary Islands Health Research Foundation	type 1 diabetes: WMD = −0.23% (95% CI: −0.35 to −0.11). Type 2 diabetes (WMD = −0.48% (95% CI −0.79 to −0.17)	rtCGM use is not a cost-effective compared with SMBG in Spain
Riemsma et al⁴⁸	4 RCTs n = 435	P: type 1 diabetes (any age) I: CGM an insulin pump system C: SMBG, CSII, MDI, CGM + CSII, CGM + MDI O: HbA1c T: 6 weeks or more	Critically low	Low	NIHR	–1.10 (–1.46 to −0.74)	CGM/pump systems are not cost-effective and not inferior to stand-alone systems
Cowart et al⁴⁹	9 RCTs n = 1039	P: type 1 diabetes/type 2 diabetes I: isCGM C: SMBG O: HbA1c or patient satisfaction T: NR	Critically low	Moderate	No	No MA	isCGM is effective in some subgroups of patients with diabetes
Benkhadra et al⁵⁰	11 RCTs n = 1592	P: type 1 diabetes I: rtCGM C: SMBG O: HbA1c and time spent in hypoglycemia T: NR	Critically low	Moderate	No	–0.276 (0.465-0.087)	rtCGM in adults with type 1 diabetes is effective in reducing HbA1c
Azhar et al⁵¹	17 RCTs n = 5407	P: type 1 diabetes/type 2 diabetes I: CGM C: SMBG O: HbA1c T: NR	Critically low	Moderate	No	No MA	CGM is effective in type 1 and type 2 diabetes
Hoeks et al⁵²	7 RCTs n = 1524	P: type 1 diabetes/type 2 diabetes (any age) I: rtCGM C: SMBG O: HbA1c T: 6 weeks or more	Low	Moderate	No	No MA	rtCGM is effective in glycemic control in adults with diabetes
Leelarathna et al⁵³	2 RCTs n = 451	P: type 1 diabetes/type 2 diabetes I: rtCGM C: SMBG O: HbA1c and any other the measure of glycemic control T: 12 weeks or more for HbA1c	Critically low	Moderate	No	No MA	isCGM may be more effective than SMBG at improving glycemic control in adults
Kieu et al⁵⁴	4 RCTs + 6 observational n = 4006	P: type 1 diabetes/type 2 diabetes I: CGM C: SMBG O: HbA1c T: NR	Low	moderate	No	No MA but the authors calculated the WMD = −0.43%	CGM/isCGM is effective in controlling HbA1c
Di Molfetta et al⁵⁵	17 RCTs + 5 observational n = 10 273	P: type 1 diabetes/type 2 diabetes I: CGM C: SMBG O: HbA1c T: NR	High	Moderate	No	MD = 0.28% (95% CI 0.3-0.21)	CGM is superior to SMBG in improving HbA1c control in diabetes
Jacobsen et al⁵⁶	70 studies: CGM-related 27 RCTs + 5 observational n = 3833	P: type 1 diabetes I: CGM, insulin pumps, decision support tools, hybrid closed loop systems C: SMBG O: HbA1c, time in range, glycemic variability, hypoglycemia risk T: NR	Low	Moderate	No	No MA	Our review highlights the potential of technology in the management of type 1 diabetes
Liang et al⁵⁷	5 RCTs n = 719	P: type 1 diabetes/type 2 diabetes I: isCGM C: SMBG O: HbA1c, time in range, frequency of hypoglycemia, time in hypoglycemia/hyperglycemia T: 10 to 24 weeks	High	Moderate	No	MD: –0.17% (95% CI −0.41 to 0.07); P = .164	FGM did not lead to a significant reduction in HbA1c compared with SMBG, however, it improved other metabolic outcomes
Uhl et al⁵⁸	14 RCTs n = 825	P: adults with type 2 diabetes I: CGM C: SMBG O: HbA1c, time in range, time in hyperglycemia, and time in hypoglycemia T: NR	High	Moderate	No	MD: all: 0.32% (rtCGM: 0.34%/FGM: 0.33%)	In type 2 diabetes, both rtCGM and FCGM led to statistically significant declines in HbA1c
Lu et al⁵⁹	11 RCTs n = 1425	P: adults with type 2 diabetes I: CGM C: SMBG O: HbA1c, time in range, time below range, and time above range T: NR	High	Moderate	Shanghai Qingpu District Health Commission	MD: -0.31, 95%CI: -0.45, -0.18)	CGM could provide type 2 diabetes with greater benefits in glycemic management compared with SMBG, particularly in patients using rtCGM
Seidu et al⁶⁰	26 RCTs n = 2783	P: type 2 diabetes I: CGM C: SMBG O: HbA1c T: >8 weeks	Moderate	Moderate	No	CGM: MD: 20.19% (95% CI 20.34-20.04), isCGM: 20.31% (95% CI 20.46-20.17)	CGM is effective in controlling HbA1c in patients with type 2 diabetes
Jancev et al⁶¹	12 RCTs n = 1248	P: type 2 diabetes I: CGM C: SMBG O: HbA1c T: >6 weeks	High	Moderate	No	MD: −3.43 mmol/mol (−0.31%; 95% CI −4.75 to −2.11); P < .00001	CGM is superior to SMBG in improving glycemic control in adults with type 2 diabetes
Ferreira et al⁶²	6 RCTs n = 407	P: non-insulin-treated type 2 diabetes I: CGM C: SMBG O: HbA1c T: NR	High	High	No	WMD: –0.31% (95% CI −0.42 to −0.21)	In type 2 diabetes, CGM is effective in improving glycemic control

Abbreviations: AHRQ, Agency for Healthcare Research and Quality; C, comparator; CGM, continuous glucose monitoring; CI, confidence interval; CSII, continuous subcutaneous insulin infusion; DM, diabetes mellitus; EPSRC, Engineering and Physical Sciences Research Council; FGM, flash glucose monitoring; HbA1C, glycated hemoglobin; I, intervention; isCGM, intermittent continuous glucose monitoring; MA, meta-analysis; MD, mean difference; MDI, multiple daily injection; NIHR, National Institute for Health and Care Research; NR, not reported; O, outcome; P, population; PMD, pooled mean difference; RCT, randomized controlled trial; rtCGM, real-time continuous glucose monitoring; SMBG, self-monitoring glucose monitoring; SMD, standardized mean difference; T, time; WMD, weighted mean difference.

Quality Assessment

Most of the SLRs were considered of critically low quality (n = 27), five of low quality, one of moderate quality and seven of high quality based on the AMSTAR-2 assessment. No SLR was excluded due to low quality. Based on GRADE assessment of the overall quality of evidence, only three SLRs were found to be of high quality, 29 SLRs with moderate quality, and eight SLRs with low quality. AMSTAR and GRADE assessment scores are summarized in Supplemental Tables D and E provided in detail in the supplementary file. Visual inspection of the funnel plot (Supplemental Figure A) shows clear symmetry that suggests potential lack of publication bias. Furthermore, the Egger regression test detected no small-study effects.

Data Synthesis and Analysis

Continuous glucose monitoring was superior in reducing HbA1c in comparison with SMBG (0.31% 95% CI 0.26-0.36) (3 mmol/mol) (Supplemental Figure B). While we identified high heterogeneity (I² = 89.7%), the robustness of this pooled estimate is supported by the direct extraction of data from primary sources rather than relying on the varied effect sizes reported across the 40 SLRs. To explore this heterogeneity, subgroup analyses were performed. The pooled direction of effect remained in favor of CGM across isCGM and rtCGM groups (P = .45) and study designs (P = .23). The treatment effect remained consistent across different study designs; the mean reduction in HbA1c was comparable between RCTs (0.32%, 95% CI 0.27-0.36) and observational studies (0.25%, 95% CI 0.14-0.36), with no significant difference between the two groups (P = .23). This indicates that the inclusion of observational data did not inflate the findings but rather provided a more conservative estimate of the overall treatment effect. However, the residual heterogeneity remained high (I² =77.6%) and was not significantly explained by the meta-regression of the considered variables (Table 2).”

Table 2.

Meta-Regression Analysis of Predictor Variables of CGM Use in Diabetes.

Variable	Coefficient	P-value	95% confidence interval
CGM type	0.058	0.344	–0.062	0.178
Diabetes type	0.002	0.976	–0.136	0.140
Proportion of patients on MDI	0.049	0.945	–0.093	0.192
Study type	0.032	0.712	–0.141	0.207
Baseline HbA1c	0.068	0.056	–0.001	0.139
Age	0.105	0.171	–0.045	0.255
Follow-up time	–0.001	0.577	–0.004	0.002
Model statistics
Tau²	0.031
I ²	77.6%
R ²	0,00
Test of residual homogeneity Q	321.4
p < 0.001*

p < 0.05 for predictor significance and the Q-test of residual heterogeneity.

Missing data were detected in several studies in less than 10% of the values for the following variables: treatment regimens, baseline mean age, and SD. This was handled by imputing values using mean values. Sensitivity analysis showed that the results were not sensitive to the imputation.

An additional analysis of different clinical scenarios was performed to assess the treatment effect in different patient populations with different predictive variables, for example, having type 1 diabetes in addition to being adult on insulin therapy. The analysis showed that the treatment effect was significant across different patient clinical characteristics with no significant difference. However, smaller effect sizes were observed among children than among adults. In the subgroup of pediatric type 2 patients with diabetes who are on non-insulin therapy, the effect was not significant due to low precision (Figure 2).

Discussion

This umbrella review and primary-level meta-analysis reveals that CGM is more effective in improving glycemic control compared with CGM in all patient subgroups. To the best of our knowledge, this analysis represents the most comprehensive synthesis of primary data identified through systematic reviews of the clinical benefit of CGM systems in diabetes including meta-analysis of data involving over 78 000 patients.

A critical methodological distinction of this study is our approach to the “overview of reviews” framework. While we utilized SLRs as the vehicle for study identification, our quantitative results are derived from a de novo meta-analysis of the underlying primary studies. This approach was chosen specifically to synthesize a broader evidence base using the same outcome (change of HbA1c vs baseline) and mitigate the risk of biased effect sizes due to study overlap across multiple reviews—a common limitation of umbrella reviews.

Our findings demonstrated a clear trend toward greater reduction in HbA1c with CGM compared with standard of care. Meta-regression was performed to identify clinical or methodological factors that might explain the observed variance in outcomes. Interestingly, despite the significant heterogeneity, none of the tested covariates—including diabetes type, CGM technology (isCGM vs rtCGM), or patient age—emerged as significant predictors of the treatment effect (Table 2). This lack of significant predictors strengthens the argument for the broad implementation of CGM, as its efficacy does not appear to be confined to any specific demographic or study design. By pooling data at the primary study level, we were able to conduct granular subgroup analyses and meta-regressions that previous narrative overviews could not achieve. Therefore, while the clinical benefit is evident, the magnitude of the response may differ in real-world practice compared with the pooled average.

Another methodological consideration of this synthesis was the inclusion of both randomized and observational studies. While we acknowledge that pooling different study designs can sometimes introduce bias, our analysis revealed no difference between the effect sizes of observational studies and RCTs. This suggests that the inclusion of non-randomized evidence did not introduce bias to the overall effect size. By integrating both evidence streams, this overview offers a comprehensive perspective on CGM performance across controlled and pragmatic settings.

Only one previous study by Kamusheva et al has systematically reviewed the published SLRs of different diabetes monitoring systems including SMBG, CGM, artificial pancreas, CSII and mobile technology or telemedicine in terms of HbA1c. The narrative review showed that the observed benefit of diabetes monitoring systems is minimal. These findings contradict our results, which suggest a significant reduction of HbA1c compared with SMBG.⁶³ Generally, a 0.3% reduction of HbA1c is considered clinically meaningful in lowering the risk of long-term diabetes complications.⁶⁴ One notable limitation of the Kamusheva study is that the findings are based on a narrative review, while our results are based on a meta-analysis of the included studies. Our findings are clinically important given the trend toward telemedicine which can support the use of CGM technology in monitoring patient outcomes. The major strength of this review is the synthesis of primary-level data identified through a large-scale search of systematic reviews. Furthermore, we employed two-step validated quality assessment tools to evaluate both the methodology of the SLRs and the quality of evidence in addition to a thorough subgroup analysis.

Our study has several potential limitations. First, several studies did not explicitly report all data points necessary to compute standard error of effect size; thus, missing data were estimated using data from similar studies. While this approach is widely accepted and adopted, it suffers from the limitation that the data used in the analysis might be different from the one reported in the original published study. Nevertheless, our sensitivity analyses showed no changes in the overall effect estimates.

Since majority of the included studies are controlled studies where the control arm is more controlled than usual care in terms of the frequency of glucose monitoring, it is possible that the true effect size is underestimated. Furthermore, this overview focused exclusively on HbA1c as the primary outcome of efficacy. While HbA1c is the established gold standard for long-term glycemic monitoring and a key predictor of microvascular complications, it does not capture the full clinical utility of CGM. Metrics such as time in range, glycemic variability, and the frequency of hypoglycemic episodes provide a more nuanced understanding of daily glucose management. By focusing only on HbA1c, our analysis may overlook the significant benefits of CGM in reducing hypoglycemia and improving quality of life. Future research should aim to synthesize these “beyond HbA1c” metrics to provide a more holistic clinical interpretation of CGM’s impact.

A notable limitation identified in this overview is that most included systematic reviews were rated as “critically low” quality according to AMSTAR-2. This typically results from a lack of protocol registration, or due to lack of blinding which is not possible given the nature of the intervention. To mitigate this, we did not rely on the conclusions or quality appraisals of the source SLRs. Instead, we performed a de novo quality assessment of the underlying primary studies using RoB-2 and ROBINS-I (RISK Of Bias In Non‑randomized Studies of Interventions). This approach ensured that our results were synthesized from the original trial data rather than being influenced by the methodological gaps of the source reviews. Nevertheless, the low quality of the existing reviews necessitates a cautious interpretation of the overall evidence base.

A primary limitation of this analysis is the high level of unexplained heterogeneity. Despite conducting meta-regressions and subgroup analyses, a large proportion of the variance remains unaccounted for, which limits the precision of our estimates when applied to specific patient populations or clinical environments. It is likely that heterogeneity stems from the broad clinical diversity of the primary studies identified.

The extant body of published research on the impact of CGM on diabetes control outcomes such as HbA1c, time in range and risk of hypoglycemia in diabetes is extensive. One notable exception, however, is the efficacy of CGM in pediatric patients with type 2 diabetes where the evidence remains uncertain. It is also advised that future research perspectives should be focused on the impact of CGM on long-term complications.

Conclusion

In conclusion, while CGM shows superior efficacy in reducing HbA1c, the strength of this recommendation is tempered by the high heterogeneity and the critically low methodological quality of many existing systematic reviews. Future research should focus on high-quality, standardized reporting to improve the certainty of evidence.

Supplemental Material

sj-docx-1-dst-10.1177_19322968261445111 – Supplemental material for Optimizing Glycemic Control Using Continuous Glucose Monitoring: An Umbrella Review of Systematic Reviews and Meta-Analysis

Supplemental material, sj-docx-1-dst-10.1177_19322968261445111 for Optimizing Glycemic Control Using Continuous Glucose Monitoring: An Umbrella Review of Systematic Reviews and Meta-Analysis by Hana A. Al-Abdulkarim, Mohammad Al-Shraim, Hossein Motahari-Nezhad, Meriem Fgaier, Márta Péntek, László Gulácsi, Raed Aldahash and Zsombor Zrubka in Journal of Diabetes Science and Technology

Footnotes

Abbreviations

AMSTAR-2, A MeaSurement Tool to Assess systematic Reviews) checklist; CGM, continuous glucose monitoring; CI, confidence interval; CSII, continuous subcutaneous insulin infusion; GRADE, grading of recommendations assessment, development, and evaluation tool; HB1Ac, glycated hemoglobin; isCGM, intermittently scanned CGM; MDI, multiple daily injection; NRCT non-randomized controlled trial; PICOT, Population-Intervention-Comparator-Outcome-Time; PRIOR, The Preferred Reporting Items for Overviews of Reviews; PRISMA, The Preferred Reporting Items for Systematic Reviews and Meta-Analyses; RCT, randomized controlled trial; ROB, risk of bias; ROB 2, Cochrane Risk of Bias tool (version 2); ROBINS-1, RISK Of Bias In Non‑randomized Studies of Interventions; rtCGM, real-time CGM; SD, standard deviation; SLR, systematic literature review; SMBG, self-monitoring of blood glucose; TIR, time in range.

ORCID iDs

Hana A. Al-Abdulkarim

Mohammad Al-Shraim

Zsombor Zrubka

Author Contributions

HA and ZZ coordinated the study, systematically searched the databases, and retrieved the studies. HA, MA, MF, and ZZ conducted data screening, selection, and extraction. HA, HM, and ZZ performed quality assessment and evidence grading. HA and ZZ wrote and edited the manuscript. All the authors HA, ZZ, HM, MA, MF, MP, LG, and RA contributed substantially to the data analysis and the interpretation of the data and commented on the drafts of the manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: No funding for this study; however, during the conduct of this research, MP, LG, and ZZ have received funding from the National Innovation and Research Office of Hungary, under the grant number TKP2021-NKTA-36.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data analyzed in this study were extracted from published systematic reviews that are publicly available. The datasets generated in the current research are available from the corresponding author upon request.

Registration Number

10.17605/OSF.IO/D6KGQ /Open Science Framework (OFS) Registry.

URL

Supplemental Material

Supplemental material for this article is available online.

References

Fleming

Petrie

Bergenstal

Holl

Peters

Heinemann

Diabetes digital app technology: benefits, challenges, and recommendations. A consensus report by the European Association for the Study of Diabetes (EASD) and the American Diabetes Association (ADA) diabetes technology working group. Diabetes Care. 2020;43(1):250-260.

Shan

Sarkar

Martin

SS.

Digital health technology and mobile devices for the management of diabetes mellitus: state of the art. Diabetologia. 2019;62(6):877-887.

Danne

Nimri

Battelino

, et al. International consensus on use of continuous glucose monitoring. Diabetes Care. 2017;40(12):1631-1640.

Funtanilla

Candidate

Caliendo

Hilas

Continuous glucose monitoring: a review of available systems. P T. 2019;44(9):550-553.

Chandler

Cumpston

Thomas

Higgins

Deeks

Clarke

. Chapter I: introduction. In: Higgins

Thomas

Chandler

Cumpston

Page

, et al, eds. Cochrane Handbook for Systematic Reviews of Interventions Version. Cochrane; 2019:6.

Gulácsi

Zrubka

Brodszky

, et al. Long-Term efficacy of tumor necrosis factor inhibitors for the treatment of Methotrexate-Naïve rheumatoid arthritis: systematic literature review and meta-analysis. Adv Ther. 2019;36:721-745.

Pollock

Fernandes

Becker

Pieper

Hartling

. Chapter V: overviews of reviews. In: Higgins

Thomas

Chandler

Cumpston

Page

, et al, eds. Cochrane Handbook for Systematic Reviews of Interventions Version. Cochrane; 2020:6.

Prousali

Haidich

Fontalis

Ziakas

Brazitikos

Mataftsi

Efficacy and safety of interventions to control myopia progression in children: an overview of systematic reviews and meta-analyses. BMC Ophthalmol. 2019;19(1):106.

American Diabetes Association Professional Practice Committee. 6. Glycemic targets: standards of medical care in diabetes—2022. Diabetes Care. 2022;45(suppl 1):S83-S96.

10.

Page

McKenzie

Bossuyt

, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:105906.

11.

Gates

Pieper

, et al. Reporting guideline for overviews of reviews of healthcare interventions: development of the PRIOR statement. BMJ. 2022;378:e070849.

12.

Alabdulkarim

, ed. Outcomes of continuous glucose monitoring: protocol for a systematic review of systematic reviews. In: 2022 IEEE 20th Jubilee International Symposium on Intelligent Systems and Informatics (SISY). IEEE; 2022:37-40.

13.

Alabdulkarim

Zrubka

Outcomes of Continuous Glucose Monitoring: Protocol for a Systematic Review of Systematic Reviews. OSF Registry; 2024.

14.

Shea

Reeves

Wells

, et al. AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ. 2017;358:j4008.

15.

Guyatt

Oxman

Vist

, et al. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ. 2008;336(7650):924-926.

16.

Motahari-Nezhad

Al-Abdulkarim

Fgaier

, et al. Digital biomarker–based interventions: systematic review of systematic reviews. J Med Internet Res. 2022;24(12):e41042.

17.

Sterne

Savović

Page

, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ. 2019;366:l4898.

18.

Sterne

Hernán

Reeves

, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ. 2016;355:i4919.

19.

Hoelzel

Weykamp

Jeppsson

, et al. IFCC reference system for measurement of hemoglobin A1c in human blood and the national standardization schemes in the United States, Japan, and Sweden: a method-comparison study. Clin Chem. 2004;50(1):166-174.

20.

Deeks

Higgins

Altman

; Cochrane Statistical Methods Group. Analysing data and undertaking meta-analyses. In: Higgins

Thomas

Chandler

Cumpston

Page

, et al, eds. Cochrane Handbook for Systematic Reviews of Interventions. Cochrane; 2019:241-284.

21.

DerSimonian

Laird

Meta-analysis in clinical trials. Control Clin Trials. 1986;7(3):177-188.

22.

Egger

Davey Smith

Schneider

Minder

Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315(7109):629-634.

23.

Wang

Zou

Zeng

Effect of different glucose monitoring methods on bold glucose control: a systematic review and meta-analysis. Comput Math Methods Med. 2022;2022:2851572.

24.

Aggarwal

Pathak

Goyal

Clinical and economic outcomes of continuous glucose monitoring system (CGMS) in patients with diabetes mellitus: a systematic literature review. Diabetes Res Clin Pract. 2022;186:109825.

25.

Teo

Hassan

Tam

Koh

Effectiveness of continuous glucose monitoring in maintaining glycaemic control among people with type 1 diabetes mellitus: a systematic review of randomised controlled trials and meta-analysis. Diabetologia. 2022;65(4):604-619.

26.

Thomas

Avari

Godsland

Lett

Reddy

Oliver

Optimizing type 1 diabetes after multiple daily injections and capillary blood monitoring: pump or sensor first? A meta-analysis using pooled differences in outcome measures. Diabetes Obes Metab. 2021;23(11):2521-2528.

27.

Kłak

Mańczak

Owoc

Olszewski

Impact of continuous glucose monitoring on improving emotional well-being among adults with type 1 diabetes mellitus: a systematic review and meta-analysis. Pol Arch Intern Med. 2021;131(9):808-818.

28.

Dicembrini

Cosentino

Monami

Mannucci

Pala

Effects of real-time continuous glucose monitoring in type 1 diabetes: a meta-analysis of randomized controlled trials. Acta Diabetol. 2021;58(4):401-410.

29.

Castellana

Parisi

Di Molfetta

, et al. Efficacy and safety of flash glucose monitoring in patients with type 1 and type 2 diabetes: a systematic review and meta-analysis. BMJ Open Diabetes Res Care. 2020;8(1):e001092.

30.

Gordon

Rutherford

Makarounas-Kirchmann

Kirchmann

Meta-analysis of average change in laboratory-measured HbA1c among people with type 1 diabetes mellitus using the 14 day flash glucose monitoring system. Diabetes Res Clin Pract. 2020;164:108158.

31.

Maiorino

Signoriello

Maio

, et al. Effects of continuous glucose monitoring on metrics of glycemic control in diabetes: a systematic review with meta-analysis of randomized controlled trials. Diabetes Care. 2020;43(5):1146-1156.

32.

Dicembrini

Mannucci

Monami

Pala

Impact of technology on glycaemic control in type 2 diabetes: a meta-analysis of randomized trials on continuous glucose monitoring and continuous subcutaneous insulin infusion. Diabetes Obes Metab. 2019;21(12):2619-2625.

33.

Ida

Kaneko

Murata

Utility of real-time and retrospective continuous glucose monitoring in patients with type 2 diabetes mellitus: a meta-analysis of randomized controlled trials. J Diabetes Res. 2019;2019:4684815.

34.

Park

QA.

The effectiveness of continuous glucose monitoring in patients with type 2 diabetes: a systematic review of literature and meta-analysis. Diabetes Technol Ther. 2018;20(9):613-621.

35.

Floyd

Chandra

Hall

, et al. Comparative analysis of the efficacy of continuous glucose monitoring and self-monitoring of blood glucose in type 1 diabetes mellitus. J Diabetes Sci Technol. 2012;6(5):1094-1102.

36.

Golden

Sapir

Methods for insulin delivery and glucose monitoring in diabetes: summary of a comparative effectiveness review. J Manag Care Pharm. 2012;18(6 suppl):S1-S17.

37.

Yeh

Brown

Maruthur

, et al. Comparative effectiveness and safety of methods of insulin delivery and glucose monitoring for diabetes mellitus: a systematic review and meta-analysis. Ann Intern Med. 2012;157(5):336-347.

38.

Szypowska

Ramotowska

Dzygalo

Golicki

Beneficial effect of real-time continuous glucose monitoring system on glycemic control in type 1 diabetic patients: systematic review and meta-analysis of randomized trials. Eur J Endocrinol. 2012;166:567-574.

39.

Wojciechowski

Ryś

Lipowska

Gawęska

Małecki

MT.

Efficacy and safety comparison of continuous glucose monitoring and self-monitoring of blood glucose in type 1 diabetes: systematic review and meta-analysis. Pol Arch Med Wewn. 2011;121(10):333-343.

40.

Gandhi

Kovalaske

Kudva

, et al. Efficacy of continuous glucose monitoring in improving glycemic control and reducing hypoglycemia: a systematic review and meta-analysis of randomized trials. J Diabetes Sci Technol. 2011;5(4):952-965.

41.

Pickup

Freeman

Sutton

AJ.

Glycaemic control in type 1 diabetes during real time continuous glucose monitoring compared with self monitoring of blood glucose: meta-analysis of randomised controlled trials using individual patient data. BMJ. 2011;343:d3805.

42.

Golicki

Golicka

Groele

Pankowska

Continuous glucose monitoring system in children with type 1 diabetes mellitus: a systematic review and meta-analysis. Diabetologia. 2008;51(2):233-240.

43.

Janapala

Jayaraj

Fathima

, et al. Continuous glucose monitoring versus self-monitoring of blood glucose in type 2 diabetes mellitus: a systematic review with meta-analysis. Cureus. 2019;11(9):e5634. doi:10.7759/cureus.5634

44.

Poolsup

Suksomboon

Kyaw

AM.

Systematic review and meta-analysis of the effectiveness of continuous glucose monitoring (CGM) on glucose control in diabetes. Diabetol Metab Syndr. 2013;5:39-34.

45.

Elbalshy

Haszard

Smith

, et al. Effect of divergent continuous glucose monitoring technologies on glycaemic control in type 1 diabetes mellitus: a systematic review and meta-analysis of randomised controlled trials. Diabet Med. 2022;39(8):e14854.

46.

Chetty

Almulla

Odueyungbo

Thabane

The effect of continuous subcutaneous glucose monitoring (CGMS) versus intermittent whole blood finger-stick glucose monitoring (SBGM) on hemoglobin A1c (HBA1c) levels in type I diabetic patients: a systematic review. Diabetes Res Clin Pract. 2008;81(1):79-87.

47.

García-Lorenzo

Rivero-Santana

Vallejo-Torres

, et al. Cost-effectiveness analysis of real-time continuous monitoring glucose compared to self-monitoring of blood glucose for diabetes mellitus in Spain. J Eval Clin Pract. 2018;24(4):772-781.

48.

Riemsma

Corro Ramos

Birnie

, et al. Integrated sensor-augmented pump therapy systems [the MiniMed® Paradigm™ Veo system and the Vibe™ and G4® PLATINUM CGM (continuous glucose monitoring) system] for managing blood glucose levels in type 1 diabetes: a systematic review and economic evaluation. Health Technol Assess. 2016;20(17):v-xxxi.

49.

Cowart

Updike

Bullers

Systematic review of randomized controlled trials evaluating glycemic efficacy and patient satisfaction of intermittent-scanned continuous glucose monitoring in patients with diabetes. Diabetes Technol Ther. 2020;22(5):337-345.

50.

Benkhadra

Alahdab

Tamhane

, et al. Real-time continuous glucose monitoring in type 1 diabetes: a systematic review and individual patient data meta-analysis. Clin Endocrinol. 2017;86(3):354-360.

51.

Azhar

Gillani

Mohiuddin

Majeed

RA.

A systematic review on clinical implication of continuous glucose monitoring in diabetes management. J Pharm Bioallied Sci. 2020;12(2):102-111.

52.

Hoeks

Greven

de Valk

HW.

Real-time continuous glucose monitoring system for treatment of diabetes: a systematic review. Diabet Med. 2011;28(4):386-394.

53.

Leelarathna

Guzder

Muralidhara

Evans

ML.

Diabetes: glycaemic control in type 1. BMJ Clin Evid. 2011;2011:0607.

54.

Kieu

King

Govender

Östlundh

The benefits of utilizing continuous glucose monitoring of diabetes mellitus in primary care: a systematic review. J Diabetes Sci Technol. 2023;17(3):762-774.

55.

Di Molfetta

Caruso

Cignarelli

, et al. Professional continuous glucose monitoring in patients with diabetes mellitus: a systematic review and meta-analysis. Diabetes Obes Metab. 2023;25(5):1301-1310.

56.

Jacobsen

Sherr

Considine

, et al. Utility and precision evidence of technology in the treatment of type 1 diabetes: a systematic review. Commun Med. 2023;3(1):132.

57.

Liang

Koye

Hachem

Zafari

Braat

Ekinci

EI.

Efficacy of flash glucose monitoring in type 1 and type 2 diabetes: a systematic review and meta-analysis of randomised controlled trials. Front Clin Diabetes Healthc. 2022;3:849725.

58.

Uhl

Choure

Rouse

Loblack

Reaven

Effectiveness of continuous glucose monitoring on metrics of glycemic control in type 2 diabetes mellitus: a systematic review and meta-analysis of randomized controlled trials. J Clin Endocrinol Metab. 2024;109(4):1119-1131.

59.

Ying

Wang

Han

Zhang

Effects of continuous glucose monitoring on glycaemic control in type 2 diabetes: a systematic review and network meta-analysis of randomized controlled trials. Diabetes Obes Metab. 2024;26(1):362-372.

60.

Seidu

Kunutsor

Ajjan

Choudhary

Efficacy and safety of continuous glucose monitoring and intermittently scanned continuous glucose monitoring in patients with type 2 diabetes: a systematic review and meta-analysis of interventional evidence. Diabetes Care. 2024;47(1):169-179.

61.

Jancev

Vissers

Visseren

, et al. Continuous glucose monitoring in adults with type 2 diabetes: a systematic review and meta-analysis. Diabetologia. 2024;67:798-810.

62.

Ferreira

Trevisan

Pasqualotto

, et al. Continuous glucose monitoring systems in noninsulin-treated people with type 2 diabetes: a systematic review and meta-analysis of randomized controlled trials. Diabetes Technol Ther. 2024;26(4):252-262.

63.

Kamusheva

Tachkov

Dimitrova

, et al. A systematic review of collective evidences investigating the effect of diabetes monitoring systems and their application in health care. Front Endocrinol. 2021;12:636959.

64.

Khaw

Wareham

Luben

, et al. Glycated haemoglobin, diabetes, and mortality in men in Norfolk cohort of European Prospective Investigation of Cancer and Nutrition (EPIC-Norfolk). BMJ. 2001;322(7277):15-18.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.17 MB