Cited by 104Diabetes Management and ResearchDiabetes Management and EducationDiabetes Treatment and Management

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

Open access
Elbalshy, Mona, Haszard, Jillian, Smith, Hazel, Kuroko, Sarahmarie, Galland, Barbara, Oliver, Nick, Shah, Viral, de Bock, Martin I. +1 more·2022·Diabetic Medicine

Abstract

Aims: We aimed to conduct a systematic review and meta‐analysis of randomised controlled clinical trials (RCTs) assessing separately and together the effect of the three distinct categories of continuous glucose monitoring (CGM) systems (adjunctive, non‐adjunctive and intermittently‐scanned CGM [isCGM]), compared with traditional capillary glucose monitoring, on HbA1c and CGM metrics. Methods: PubMed, Web of Science, Scopus and Cochrane Central register of clinical trials were searched. Inclusion criteria were as follows: randomised controlled trials; participants with type 1 diabetes of any age and insulin regimen; investigating CGM and isCGM compared with traditional capillary glucose monitoring; and reporting glycaemic outcomes of HbA1c and/or time‐in‐range (TIR). Glycaemic outcomes were extracted post‐intervention and expressed as mean differences and 95%CIs between treatment and comparator groups. Results were pooled using a random‐effects meta‐analysis. Risk of bias was assessed using the Cochrane Rob2 tool. Results: This systematic review was conducted between January and April 2021; it included 22 RCTs (15 adjunctive, 5 non‐adjunctive, and 2 isCGM)). The overall analysis of the pooled three categories showed a statistically significant absolute improvement in HbA1c percentage points (mean difference (95% CI): −0.22% [−0.31 to −0.14], I2 = 79%) for intervention compared with comparator and was strongest for adjunctive CGM (−0.26% [−0.36, −0.16]). Overall TIR (absolute change) increased by 5.4% (3.5 to 7.2), I2 = 71% for CGM intervention compared with comparator and was strongest with non‐adjunctive CGM (6.0% [2.3, 9.7]). Conclusions: For individuals with T1D, use of CGM was beneficial for impacting glycaemic outcomes including HbA1c, TIR and time‐below‐range (TBR). Glycaemic improvement appeared greater for TIR for newer non‐adjunctive CGM technology.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1111/dme.14854

Cites

56 of 56 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Integration of continuous glucose monitoring (CGM) into the treatment plan soon after diagnosis improves glycemic outcomes, decreases hypoglycemic events, and improves quality of life for individuals with type 1 diabetes.

    “Integration of continuous glucose monitoring (CGM) into the treatment plan soon after diagnosis improves glycemic outcomes, decreases hypoglycemic events, and improves quality of life for individuals with type 1 diabetes (18-23).”

    From 9. Pharmacologic Approaches to Glycemic Treatment: Standards of Care in Diabetes—2025 (2025) · cites this paper as [19]

    The Abstract Conclusions report improved glycaemic outcomes, including HbA1c, time in range, and time below range; the Discussion links CGM to minimising hypoglycaemia. The paper does not establish that starting CGM soon after diagnosis produces these benefits or report an improvement in quality of life. The supplied passages have no page numbers. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Elbalshy, M., Haszard, J., Smith, H., Kuroko, S., Galland, B., Oliver, N., Shah, V., de Bock, M. I., & Wheeler, B. J.
Affiliations
  • University of Otago (NZ)
  • Imperial College London (GB)
  • University of Colorado Anschutz (US)
  • Department of Paediatrics University of Otago Christchurch New Zealand
  • Southern District Health Board (NZ)
Corresponding author
Wheeler, Benjamin J.
Venue
Diabetic Medicine
Publisher
Wiley
Published
2022-04-25
Volume
39
Issue
8
Pages
e14854-e14854
ISSN
  • 0742-3071 (ISSN-L)
  • 1464-5491
OpenAlex
W4224293604
Cited by
104
References
56
Citations by year
2026: 17 · 2025: 24 · 2024: 22 · 2023: 32 · 2022: 8
FWCI
3.06
Type
journal article
Language
en
Open access
Locations
  • Diabetic Medicine (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Funders
Dexcom
Topics
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
Keywords
continuous glucose monitoring, type 1 diabetes, glycemic control, HbA1c, time in range, time below range, flash glucose monitoring, insulin therapy
MeSH terms
Glycemic Control, Blood Glucose, Diabetes Mellitus, Type 1, Glycated Hemoglobin, Humans, Hypoglycemic Agents, Technology, Blood Glucose Self-Monitoring, Randomized Controlled Trials as Topic

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review