Evidence map›Paper›PMID 33445438›Full record

ReviewSensors (Basel, Switzerland)2021

A Comprehensive Review of Continuous Glucose Monitoring Accuracy during Exercise Periods.

Elena Muñoz Fabra, José-Luis Díez, Jorge Bondia, Alejandro José Laguna Sanz

Open access · goldAbstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
5.1field-weighted citation impact, top 4% 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

22 citing papers in PubMed, 43 citations in OpenAlex.

  1. Trial
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  8. Observational
  9. Criteria for Personalised Choice of a Continuous Glucose Monitoring System: An Expert Opinion.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2024
    Article
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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 2 institutions in 1 country.

Elena Muñoz FabraInstituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, Camino de Vera s/n, 46022 València, Spain.ORCID 0000-0002-2204-136X
José-Luis DíezInstituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, Camino de Vera s/n, 46022 València, Spain.ORCID 0000-0002-5659-1212
Jorge BondiaInstituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, Camino de Vera s/n, 46022 València, Spain.ORCID 0000-0001-7286-3719
Alejandro José Laguna SanzCentro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, 28029 Madrid, Spain.ORCID 0000-0002-1789-5467
Instituto de Salud Carlos III · ESUniversitat Politècnica de València · ES

Funding

Agencia Estatal de Investigación PID2019-107722RB-C21European Commission FEDER fundsMinisterio de Economía, Industria y Competitividad, Gobierno de España DPI2016-78831-C2-1-RUniversitat Politècnica de València PAID-06-18
6 · The paper itself

Abstract

Continuous Glucose Monitoring (CGM) has been a springboard of new diabetes management technologies such as integrated sensor-pump systems, the artificial pancreas, and more recently, smart pens. It also allows patients to make better informed decisions compared to a few measurements per day from a glucometer. However, CGM accuracy is reportedly affected during exercise periods, which can impact the effectiveness of CGM-based treatments. In this review, several studies that used CGM during exercise periods are scrutinized. An extensive literature review of clinical trials including exercise and CGM in type 1 diabetes was conducted. The gathered data were critically analysed, especially the Mean Absolute Relative Difference (MARD), as the main metric of glucose accuracy. Most papers did not provide accuracy metrics that differentiated between exercise and rest (non-exercise) periods, which hindered comparative data analysis. Nevertheless, the statistic results confirmed that CGM during exercise periods is less accurate.

Indexed as

Blood Glucose Self-MonitoringDiabetes Mellitus, Type 1ExerciseHumansRestaccuracycontinuous glucose monitoringexercisemean absolute relative differencephysical activitytype 1 diabetes

Identifiers

PMID33445438
PMCPMC7828017
OpenAlexW3119854720

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.