Evidence map›Paper›PMID 34711074›Full record

Observational studyJournal of diabetes science and technology2023

Evaluation of a Continuous Blood Glucose Monitor: A Novel and Non-Invasive Wearable Using Bioimpedance Technology.

Farid Sanai, Arshman S Sahid, Jacqueline Huvanandana, Sandra Spoa, Lachlan H Boyle, Jonathan Hribar, David Ta-Yuan Wang, Benjamin Kwan, Stephen Colagiuri, Shane J Cox and 1 more

Open access · greenAbstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in Journal of diabetes science and technology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
1.4field-weighted citation impact, top 18% 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

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. New Insights into Cardiac Intensive Care.Reviews in cardiovascular medicine · 2026
    Review
  4. Article
  5. Smart Ring in Clinical Medicine: A Systematic Review.Biomimetics (Basel, Switzerland) · 2025
    Review
  6. Article
  7. Article
  8. Article
  9. Electrical bioimpedance in the era of artificial intelligence.Journal of electrical bioimpedance · 2024
    Article
  10. Review
  11. Review
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

11 authors at 2 institutions in 1 country.

Farid SanaiScimita Ventures Pty Ltd, Sydney, NSW, Australia.ORCID 0000-0001-5518-6867
Arshman S SahidScimita Ventures Pty Ltd, Sydney, NSW, Australia.ORCID 0000-0001-9012-2156
Jacqueline HuvanandanaScimita Ventures Pty Ltd, Sydney, NSW, Australia.ORCID 0000-0001-7683-9070
Sandra SpoaScimita Ventures Pty Ltd, Sydney, NSW, Australia.ORCID 0000-0002-8650-9216
Lachlan H BoyleScimita Ventures Pty Ltd, Sydney, NSW, Australia.
Jonathan HribarScimita Ventures Pty Ltd, Sydney, NSW, Australia.
David Ta-Yuan WangScimita Ventures Pty Ltd, Sydney, NSW, Australia.
Benjamin KwanCanterbury Hospital, Sydney, NSW, Australia.ORCID 0000-0002-3998-9720
Stephen ColagiuriBoden Collaboration of Obesity, Nutrition, Exercise and Eating Disorders, The University of Sydney, Sydney, NSW, Australia.
Shane J CoxScimita Ventures Pty Ltd, Sydney, NSW, Australia.
Thomas J TelferScimita Ventures Pty Ltd, Sydney, NSW, Australia.ORCID 0000-0002-5838-2537
Canterbury Hospital · AUThe University of Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundFrequent blood glucose level (BGL) monitoring is essential for effective diabetes management. Poor compliance is common due to the painful finger pricking or subcutaneous lancet implantation required from existing technologies. There are currently no commercially available non-invasive devices that can effectively measure BGL. In this real-world study, a prototype non-invasive continuous glucose monitoring system (NI-CGM) developed as a wearable ring was used to collect bioimpedance data. The aim was to develop a mathematical model that could use these bioimpedance data to estimate BGL in real time.

methodsThe prototype NI-CGM was worn by 14 adult participants with type 2 diabetes for 14 days in an observational clinical study. Bioimpedance data were collected alongside paired BGL measurements taken with a Food and Drug Administration (FDA)-approved self-monitoring blood glucose (SMBG) meter and an FDA-approved CGM. The SMBG meter data were used to improve CGM accuracy, and CGM data to develop the mathematical model.

resultsA gradient boosted model was developed using a randomized 80-20 training-test split of data. The estimated BGL from the model had a Mean Absolute Relative Difference (MARD) of 17.9%, with the Parkes error grid (PEG) analysis showing 99% of values in clinically acceptable zones A and B.

conclusionsThis study demonstrated the reliability of the prototype NI-CGM at collecting bioimpedance data in a real-world scenario. These data were used to train a model that could successfully estimate BGL with a promising MARD and clinically relevant PEG result. These results will enable continued development of the prototype NI-CGM as a wearable ring.

Indexed as

Diabetes Mellitus, Type 1Diabetes Mellitus, Type 2Wearable Electronic DevicesAdultBlood GlucoseBlood Glucose Self-MonitoringHumansReproducibility of ResultsBlood Glucoseblood glucosecontinuous glucose monitoring systemdevicediabetesmodelnon-invasive

Identifiers

PMID34711074
PMCPMC10012362
OpenAlexW3208364258

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.