Evidence map›Paper›PMID 33003524›Full record

ArticleBiosensors2020

Accuracy of the Dexcom G6 Glucose Sensor during Aerobic, Resistance, and Interval Exercise in Adults with Type 1 Diabetes.

Florian H Guillot, Peter G Jacobs, Leah M Wilson, Joseph El Youssef, Virginia B Gabo, Deborah L Branigan, Nichole S Tyler, Katrina Ramsey, Michael C Riddell, Jessica R Castle

Registry-linked trialAbstract read
In one paragraph

Article in Biosensors, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05736263 (Setting a New Algorithm For the Management of Exercise-induced Glycemic Variations in Patients With Type 1 Diabetes in Intensive Therapy With Hybrid Closed-loop Systems), which is not on this map. Cited by 21 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 2 pooled it
–field-weighted citation impact
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.

NCT05736263 naunknown statusnot on this mapstarted 2023, after this paper: background citation

Setting a New Algorithm For the Management of Exercise-induced Glycemic Variations in Patients With Type 1 Diabetes in Intensive Therapy With Hybrid Closed-loop Systems

TypeinterventionalSponsorUniversity of Roma La SapienzaRan2023 to 2024Enrolled50ConditionsType 1 Diabetes, ExerciseArmsModerate intensity exercise, High intensity interval exercise, Combined exercise, Resistance exercise
3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

10 authors.

Florian H GuillotDivision of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR 97239, USA.ORCID 0000-0003-0678-4347
Peter G JacobsArtificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA.
Leah M WilsonDivision of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR 97239, USA.
Joseph El YoussefDivision of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR 97239, USA.
Virginia B GaboDivision of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR 97239, USA.
Deborah L BraniganDivision of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR 97239, USA.
Nichole S TylerArtificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA.ORCID 0000-0003-3293-9680
Katrina RamseyOregon Clinical and Translational Research Institute Biostatistics & Design Program, Oregon Health & Science University, Portland, OR 97239, USA.
Michael C RiddellMuscle Health Research Centre, School of Kinesiology and Health Science, York University, Toronto, ON M3J 1P3, Canada.ORCID 0000-0001-6556-7559
Jessica R CastleDivision of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR 97239, USA.

Funding

Oregon Clinical and Translational Research Institute - The National COVID Cohort Collaborative (N3C)UL1TR002369 · NCATS · OREGON HEALTH & SCIENCE UNIVERSITY · PI Cynthia D Morris, Christopher G. Slatore · 2017 to 2026
$78.4M
Dexcom N/ANCATS NIH HHS UL1 TR002369
6 · The paper itself

Abstract

The accuracy of continuous glucose monitoring (CGM) sensors may be significantly impacted by exercise. We evaluated the impact of three different types of exercise on the accuracy of the Dexcom G6 sensor. Twenty-four adults with type 1 diabetes on multiple daily injections wore a G6 sensor. Participants were randomized to aerobic, resistance, or high intensity interval training (HIIT) exercise. Each participant completed two in-clinic 30-min exercise sessions. The sensors were applied on average 5.3 days prior to the in-clinic visits (range 0.6-9.9). Capillary blood glucose (CBG) measurements with a Contour Next meter were performed before and after exercise as well as every 10 min during exercise. No CGM calibrations were performed. The median absolute relative difference (MARD) and median relative difference (MRD) of the CGM as compared with the reference CBG did not differ significantly from the start of exercise to the end exercise across all exercise types (ranges for aerobic MARD: 8.9 to 13.9% and MRD: -6.4 to 0.5%, resistance MARD: 7.7 to 14.5% and MRD: -8.3 to -2.9%, HIIT MARD: 12.1 to 16.8% and MRD: -14.3 to -9.1%). The accuracy of the no-calibration Dexcom G6 CGM was not significantly impacted by aerobic, resistance, or HIIT exercise.

Indexed as

Blood GlucoseBlood Glucose Self-MonitoringDiabetes Mellitus, Type 1CalibrationExerciseHumansBlood Glucoseaerobic exercisecontinuous glucose monitoringexerciseglucose sensor accuracyhigh intensity interval trainingresistance exercisetype 1 diabetes

Identifiers

PMID33003524
PMCPMC7600074

What Socratic holds

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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 Socratic graph.