Evidence map›Paper›PMID 21129353›Full record

ReviewJournal of diabetes science and technology2010

Technologies for continuous glucose monitoring: current problems and future promises.

Santhisagar Vaddiraju, Diane J Burgess, Ioannis Tomazos, Faquir C Jain, Fotios Papadimitrakopoulos

2 registry-linked trialsOpen access · bronzeAbstract readReview
In one paragraph

Review in Journal of diabetes science and technology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 97 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
97citing papers in PubMed, 1 pooled it
14.7field-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.

NCT03186300 nacompletednot on this mapstarted 2017, after this paper: background citation

Patient Empowerment Through Predictive Personalised Decision Support (PEPPER)

TypeinterventionalSponsorInstitut d'Investigació Biomèdica de Girona Dr. Josep TruetaRan2017 to 2018Enrolled8ConditionsDiabetes Mellitus, Type 1ArmsPEPPER system
NCT03849755 nacompletednot on this mapstarted 2018, after this paper: background citation

Patient Empowerment Through Predictive Personalised Decision Support(PEPPER)-Validation Study.

TypeinterventionalSponsorInstitut d'Investigació Biomèdica de Girona Dr. Josep TruetaRan2018 to 2019Enrolled58ConditionsDiabetes Mellitus, Type 1ArmsPEPPER system
3 · Its place in the literature

Who cites it

97 citing papers in PubMed, 1 synthesis or guideline pooled it, 270 citations in OpenAlex.

  1. Pooled it
  2. Trial
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  9. Review
  10. Article
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  12. Article
  13. Strategies for optimization of hypoglycemia rat models.Animal models and experimental medicine · 2025
    Review
  14. Review
  15. Review
  16. Article
  17. Review
  18. Diabetology and Nanotechnology: A Compelling Combination.Recent patents on nanotechnology · 2025
    Review
  19. Article
  20. Article

37 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

5 authors at 2 institutions in 1 country.

Santhisagar VaddirajuNanomaterials Optoelectronics Laboratory, Polymer Program, Institute of Materials Science, University of Connecticut, Storrs, Connecticut 06268, USA.
Diane J Burgess
Ioannis Tomazos
Faquir C Jain
Fotios Papadimitrakopoulos
University of Connecticut · USBiorasis (United States) · US

Funding

BIOSENSOR ARRAYS BASED ON SINGLE WALL CARBON NANOTUBESR01ES013557 · NIEHS · UNIVERSITY OF CONNECTICUT STORRS · PI RUSLING, JAMES F. · 2006 to 2010
$1.6M
Needle-Implantable, Wireless Multi-Sensor for Continuous Glucose MonitoringR43EB011886 · NIBIB · BIORASIS, INC. · PI ISLAM, SYED K., JAIN, FAQUIR C · 2009 to 2010
$901k
Miniaturized Wireless Implantable Biosensors for Metabolic MonitoringR21HL090458 · NHLBI · UNIVERSITY OF CONNECTICUT STORRS · PI BURGESS, DIANE JANE · 2007 to 2009
$692k
NHLBI NIH HHS 1-R21‑HL090458-01NIBIB NIH HHS R43EB011886NIEHS NIH HHS ES013557
6 · The paper itself

Abstract

Devices for continuous glucose monitoring (CGM) are currently a major focus of research in the area of diabetes management. It is envisioned that such devices will have the ability to alert a diabetes patient (or the parent or medical care giver of a diabetes patient) of impending hypoglycemic/hyperglycemic events and thereby enable the patient to avoid extreme hypoglycemic/hyperglycemic excursions as well as minimize deviations outside the normal glucose range, thus preventing both life-threatening events and the debilitating complications associated with diabetes. It is anticipated that CGM devices will utilize constant feedback of analytical information from a glucose sensor to activate an insulin delivery pump, thereby ultimately realizing the concept of an artificial pancreas. Depending on whether the CGM device penetrates/breaks the skin and/or the sample is measured extracorporeally, these devices can be categorized as totally invasive, minimally invasive, and noninvasive. In addition, CGM devices are further classified according to the transduction mechanisms used for glucose sensing (i.e., electrochemical, optical, and piezoelectric). However, at present, most of these technologies are plagued by a variety of issues that affect their accuracy and long-term performance. This article presents a critical comparison of existing CGM technologies, highlighting critical issues of device accuracy, foreign body response, calibration, and miniaturization. An outlook on future developments with an emphasis on long-term reliability and performance is also presented.

Indexed as

Monitoring, AmbulatoryBiomarkersBlood GlucoseDiabetes MellitusEquipment DesignForecastingHumansInsulin Infusion SystemsMiniaturizationPredictive Value of TestsReproducibility of ResultsSignal Processing, Computer-AssistedTime FactorsBiomarkersBlood Glucose

Identifiers

PMID21129353
PMCPMC3005068
OpenAlexW2161313513

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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.