Evidence map›Paper›PMID 29145749›Full record

ArticleJournal of diabetes science and technology2018

First Experiences With a Wearable Multisensor Device in a Noninvasive Continuous Glucose Monitoring Study at Home, Part II: The Investigators' View.

Mattia Zanon, Martin Mueller, Pavel Zakharov, Mark S Talary, Marc Donath, Werner A Stahel, Andreas Caduff

Abstract read
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Article in Journal of diabetes science and technology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–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

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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

8 citing papers in PubMed.

  1. Observational
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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

7 authors.

Mattia Zanon1 Biovotion AG, Biovotion AG, Zurich, Switzerland.
Martin Mueller1 Biovotion AG, Biovotion AG, Zurich, Switzerland.
Pavel Zakharov1 Biovotion AG, Biovotion AG, Zurich, Switzerland.
Mark S Talary1 Biovotion AG, Biovotion AG, Zurich, Switzerland.
Marc Donath2 Clinic for Endocrinology and Diabetes, University Hospital Basel, Basel, Switzerland.
Werner A Stahel3 Seminar for Statistics, ETH Zurich, Zurich, Switzerland.
Andreas Caduff1 Biovotion AG, Biovotion AG, Zurich, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundExtensive past work showed that noninvasive continuous glucose monitoring with a wearable multisensor device worn on the upper arm provides useful information about glucose trends to improve diabetes therapy in controlled and semicontrolled conditions.

methodTo test previous findings also in uncontrolled conditions, a long term at home study has been organized to collect multisensor and reference glucose data in a population of 20 type 1 diabetes subjects. A total of 1072 study days were collected and a fully on-line compatible algorithmic routine linking multisensor data to glucose applied to estimate glucose levels noninvasively.

resultsThe algorithm used here calculates glucose values from sensor data and adds a constant obtained by a daily calibration. It provides point inaccuracy measured by a MARD of 35.4 mg/dL on test data. This is higher than current state-of-the-art minimally invasive devices, but still 86.9% of glucose rate points fall within the zone A

conclusionsThe multisensor device and the algorithmic routine used earlier in controlled conditions tracks glucose changes also in uncontrolled conditions, although with lower accuracy. The examination of learning curves suggests that obtaining more data would not improve the results. Therefore, further efforts would focus on the development of more complex algorithmic routines able to compensate for environmental and physiological confounders better.

Indexed as

AlgorithmsModels, TheoreticalWearable Electronic DevicesAdultBlood GlucoseBlood Glucose Self-MonitoringDiabetes Mellitus, Type 1FemaleHumansMaleMiddle AgedBlood Glucosealgorithmdiabetesdielectric spectroscopymultisensorT1DM

Identifiers

PMID29145749
PMCPMC6154230

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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.