Cited by 96Diabetes Management and ResearchDiabetes Management and EducationDiabetes Treatment and Management

Continuous glucose monitoring: Current use in diabetes management and possible future applications

Open access
Vettoretti, Martina, Cappon, Giacomo, Acciaroli, Giada, Facchinetti, Andrea, Sparacino, Giovanni·2018·Journal of Diabetes Science and Technology

Abstract

The recent announcement of the production of new low-cost continuous glucose monitoring (CGM) sensors, the approval of marketed CGM sensors for making treatment decisions, and new reimbursement criteria have the potential to revolutionize CGM use. After briefly summarizing current CGM applications, we discuss how, in our opinion, these changes are expected to extend CGM utilization beyond diabetes patients, for example, to subjects with prediabetes or even healthy individuals. We also elaborate on how the integration of CGM data with other relevant information, for example, health records and other medical device/wearable sensor data, will contribute to creating a digital data ecosystem that will improve our understanding of the etiology and complications of diabetes and will facilitate the development of data analytics for personalized diabetes management and prevention.

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 · 10.1177/1932296818774078

Cites

78 of 78 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?

  • Such data integration could contribute to generating a digital ecosystem of diabetes data that can be used to extract new medical knowledge that cannot be discovered by relying on a single source of information.

    “Such data integration could contribute to generating a digital ecosystem of diabetes data that can be used to extract new medical knowledge that cannot be discovered by relying on a single source of information [66,67].”

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

Research works printed but not linked: 4

  1. Edelman SV. Regulation catches up to reality. J Diabetes Sci Technol. 2017;11:160-164.
  2. Ertin E, Stohs N, Kumar S, et al. AutoSense: unobtrusively wearable sensor suite for inferring the onset, causality, and consequences of stress in the field. In: Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems. Seattle, WA, 2011: 274-287.
  3. Bellazzi R, Dagliati A, Sacchi L, Segagni D. Big data tech- nologies: new opportunities for diabetes management. J Diabetes Sci Technol. 2015;9:1119-1125.
  4. Wendel J, Heintzman ND. Open-source framework for integrating and visualising multimodal data from geoloca- tion, diabetes and exercise devices. Diabetes Technol Ther. 2013;15:A117-A118.

Cited by

Sorted by citations

Authors
Vettoretti, M., Cappon, G., Acciaroli, G., Facchinetti, A., & Sparacino, G.
Affiliations
University of Padua (IT)
Corresponding author
Sparacino, Giovanni
Venue
Journal of Diabetes Science and Technology
Published
2018-05-22
Volume
12
Issue
5
Pages
1064-1071
ISSN
1932-2968 (ISSN-L)
OpenAlex
W2804034601
Cited by
96
References
78
Citations by year
2026: 7 · 2025: 13 · 2024: 10 · 2023: 16 · 2022: 9 · 2021: 9 · 2020: 15 · 2019: 16 · 2018: 1
FWCI
8.15
Type
journal article
Language
en
Open access
Locations
  • Journal of Diabetes Science and Technology (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • Padua Research Archive (University of Padova) (submittedVersion)
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, diabetes management, prediabetes, healthy individuals, digital health ecosystem, wearable sensors, personalized diabetes management, diabetes prevention, reimbursement, data analytics
MeSH terms
Blood Glucose, Diabetes Mellitus, Humans, Blood Glucose Self-Monitoring

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