Training and Evaluation of a Breath-Based Glucose Estimation Algorithm in People With Type 2 Diabetes: The BOYDSense-GM Study

This study validates the potential of breath-based VOC analysis for non-invasive SMBG in T2D, demonstrating reliability, usability, and patient acceptance. Further optimization is needed to improve accuracy, particularly at high glucose levels, but the technology shows promise for enhancing diabetes management and screening.

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Publication Details

Journal
Journal of Diabetes Science and Technology
Published
2026-02-26
DOI
https://doi.org/10.1177/19322968261417645
Primary Topic
Advanced Chemical Sensor Technologies
Type
article
Field-Weighted Citation Impact
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article

Training and Evaluation of a Breath-Based Glucose Estimation Algorithm in People With Type 2 Diabetes: The BOYDSense-GM Study

P. Gourdy, Laure Combes, CLARISSE FRANÇOIS-MARSAL, Benoît Lepage et al.
Journal of Diabetes Science and Technology
Advanced Chemical Sensor Technologies
article

Training and Evaluation of a Breath-Based Glucose Estimation Algorithm in People With Type 2 Diabetes: The BOYDSense-GM Study

P. Gourdy, Laure Combes, CLARISSE FRANÇOIS-MARSAL, Benoît Lepage, Frank Flacke, Jochen Sieber, Théo Schiavi, Sandrine Isz
article en

Abstract

This study validates the potential of breath-based VOC analysis for non-invasive SMBG in T2D, demonstrating reliability, usability, and patient acceptance. Further optimization is needed to improve accuracy, particularly at high glucose levels, but the technology shows promise for enhancing diabetes management and screening.

Journal of Diabetes Science and Technology
Inserm (FR), Université Fédérale de Toulouse Midi-Pyrénées (FR), Ipsen (France) (FR), Centre Hospitalier Universitaire de Toulouse (FR), ZNA Middelheim Hospital (BE), BH Consulting (Ireland) (IE)
Openalex Percentile: Top 44%
Advanced Chemical Sensor Technologies
DOIOpen Access PDFOpenAlex
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