Evidence map›Paper›PMID 31441246›Full record

ReviewDiabetes & metabolism journal2019

Continuous Glucose Monitoring Sensors for Diabetes Management: A Review of Technologies and Applications.

Giacomo Cappon, Martina Vettoretti, Giovanni Sparacino, Andrea Facchinetti

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Diabetes & metabolism journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 181 papers, 11 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
181citing papers in PubMed, 11 pooled it
25.3field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

181 citing papers in PubMed, 11 syntheses or guidelines pooled it, 438 citations in OpenAlex.

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  4. Continuous glucose monitoring in type 2 diabetes: a systematic review of barriers and opportunities for care improvement.International journal for quality in health care : journal of the International Society for Quality in Health Care · 2025
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121 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Giacomo CapponDepartment of Information Engineering, University of Padova, Padova, Italy.ORCID 0000-0003-4358-9268
Martina VettorettiDepartment of Information Engineering, University of Padova, Padova, Italy.
Giovanni SparacinoDepartment of Information Engineering, University of Padova, Padova, Italy.
Andrea FacchinettiDepartment of Information Engineering, University of Padova, Padova, Italy. facchine@dei.unipd.it.ORCID 0000-0001-8041-2280
University of Padua · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

By providing blood glucose (BG) concentration measurements in an almost continuous-time fashion for several consecutive days, wearable minimally-invasive continuous glucose monitoring (CGM) sensors are revolutionizing diabetes management, and are becoming an increasingly adopted technology especially for diabetic individuals requiring insulin administrations. Indeed, by providing glucose real-time insights of BG dynamics and trend, and being equipped with visual and acoustic alarms for hypo- and hyperglycemia, CGM devices have been proved to improve safety and effectiveness of diabetes therapy, reduce hypoglycemia incidence and duration, and decrease glycemic variability. Furthermore, the real-time availability of BG values has been stimulating the realization of new tools to provide patients with decision support to improve insulin dosage tuning and infusion. The aim of this paper is to offer an overview of current literature and future possible developments regarding CGM technologies and applications. In particular, first, we outline the technological evolution of CGM devices through the last 20 years. Then, we discuss about the current use of CGM sensors from patients affected by diabetes, and, we report some works proving the beneficial impact provided by the adoption of CGM. Finally, we review some recent advanced applications for diabetes treatment based on CGM sensors.

Indexed as

AdolescentAdultAlgorithmsBlood GlucoseBlood Glucose Self-MonitoringChildChild, PreschoolDecision Support Systems, ManagementDiabetes MellitusFemaleHumansHyperglycemiaHypoglycemiaInsulin Infusion SystemsMaleWearable Electronic DevicesBlood GlucoseBlood glucose self-monitoringDiabetes mellitusHyperglycemiaHypoglycemiaInsulin infusion systems

Identifiers

PMID31441246
PMCPMC6712232
OpenAlexW2964434942

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.