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

Background: Frequent 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. Methods: The 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. Results: A 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. Conclusions: This study demonstrated the reliability of the prototype NI-CGM at collecting bioimpedance data in a realworld 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.

[1]  Yeolho Lee,et al.  Wrist-wearable bioelectrical impedance analyzer with miniature electrodes for daily obesity management , 2021, Scientific reports.

[2]  Standards of Medical Care in Diabetes—2021 Abridged for Primary Care Providers , 2020, Clinical Diabetes.

[3]  P. Bertemes-Filho,et al.  Analytical Model for Blood Glucose Detection Using Electrical Impedance Spectroscopy , 2020, Sensors.

[4]  Jayne Wu,et al.  Review of non-invasive continuous glucose monitoring based on impedance spectroscopy , 2020 .

[5]  N. Oliver,et al.  Is it possible to constantly and accurately monitor blood sugar levels, in people with Type 1 diabetes, with a discrete device (non‐invasive or invasive)? , 2020, Diabetic medicine : a journal of the British Diabetic Association.

[6]  Maryamsadat Shokrekhodaei,et al.  Review of Non-Invasive Glucose Sensing Techniques: Optical, Electrical and Breath Acetone , 2020, Sensors.

[7]  Giovanni Sparacino,et al.  Retrospective Continuous-Time Blood Glucose Estimation in Free Living Conditions with a Non-Invasive Multisensor Device , 2019, Sensors.

[8]  Javier Reina-Tosina,et al.  Fundamentals, Recent Advances, and Future Challenges in Bioimpedance Devices for Healthcare Applications , 2019, J. Sensors.

[9]  Amin M. Abbosh,et al.  The Progress of Glucose Monitoring—A Review of Invasive to Minimally and Non-Invasive Techniques, Devices and Sensors , 2019, Sensors.

[10]  Øyvind Stavdahl,et al.  Differences Between Flash Glucose Monitor and Fingerprick Measurements , 2018, Biosensors.

[11]  L. Ward Bioelectrical impedance analysis for body composition assessment: reflections on accuracy, clinical utility, and standardisation , 2018, European Journal of Clinical Nutrition.

[12]  Mattia Zanon,et al.  First Experiences With a Wearable Multisensor Device in a Noninvasive Continuous Glucose Monitoring Study at Home, Part II: The Investigators’ View , 2018, Journal of diabetes science and technology.

[13]  Maciej Banach,et al.  Complications of Diabetes 2017 , 2018, Journal of diabetes research.

[14]  L. Leelarathna,et al.  Accuracy of flash glucose monitoring and continuous glucose monitoring technologies: Implications for clinical practice , 2018, Diabetes & vascular disease research.

[15]  Xiaohao Wang,et al.  Noninvasive Continuous Glucose Monitoring Using a Multisensor-Based Glucometer and Time Series Analysis , 2017, Scientific Reports.

[16]  Tamar Lin Non-Invasive Glucose Monitoring: A Review of Challenges and Recent Advances , 2017 .

[17]  Harald Sourij,et al.  Evaluation of subcutaneous glucose monitoring systems under routine environmental conditions in patients with type 1 diabetes , 2017, Diabetes, obesity & metabolism.

[18]  Guido Freckmann,et al.  Significance and Reliability of MARD for the Accuracy of CGM Systems , 2017, Journal of diabetes science and technology.

[19]  E. Hernández-Balaguera,et al.  Obtaining electrical equivalent circuits of biological tissues using the current interruption method, circuit theory and fractional calculus , 2016 .

[20]  B. Kovatchev Hypoglycemia Reduction and Accuracy of Continuous Glucose Monitoring. , 2015, Diabetes technology & therapeutics.

[21]  Stephen D Patek,et al.  Assessing sensor accuracy for non-adjunct use of continuous glucose monitoring. , 2015, Diabetes technology & therapeutics.

[22]  E. Liberopoulos,et al.  Diabetes mellitus and electrolyte disorders. , 2014, World journal of clinical cases.

[23]  Tushar Kanti Bera,et al.  Bioelectrical Impedance Methods for Noninvasive Health Monitoring: A Review , 2014, Journal of medical engineering.

[24]  Fatimah Ibrahim,et al.  The Theory and Fundamentals of Bioimpedance Analysis in Clinical Status Monitoring and Diagnosis of Diseases , 2014, Sensors.

[25]  Soumen Das,et al.  Quantitative evaluation of blood glucose concentration using impedance sensing devices , 2013 .

[26]  S. K. Vashist Non-invasive glucose monitoring technology in diabetes management: a review. , 2012, Analytica chimica acta.

[27]  Kup-Sze Choi,et al.  Recent advances in noninvasive glucose monitoring , 2012, Medical devices.

[28]  Andrea Tura,et al.  Noninvasive glycaemia monitoring: background, traditional findings, and novelties in the recent clinical trials , 2008, Current opinion in clinical nutrition and metabolic care.

[29]  Dympna Gallagher,et al.  Assessment methods in human body composition , 2008, Current opinion in clinical nutrition and metabolic care.

[30]  Yu Feldman,et al.  Non-invasive glucose monitoring in patients with diabetes: a novel system based on impedance spectroscopy. , 2006, Biosensors & bioelectronics.

[31]  R. Rubin,et al.  Psychosocial problems and barriers to improved diabetes management: results of the Cross‐National Diabetes Attitudes, Wishes and Needs (DAWN) Study , 2005, Diabetic medicine : a journal of the British Diabetic Association.

[32]  J. DeVries,et al.  Pendra goes Dutch: lessons for the CE mark in Europe , 2005, Diabetologia.

[33]  M. Elia,et al.  Bioelectrical impedance analysis--part I: review of principles and methods. , 2004, Clinical nutrition.

[34]  Yuri Feldman,et al.  Dielectric spectroscopy study of specific glucose influence on human erythrocyte membranes , 2003 .

[35]  Sverre Grimnes,et al.  Bioimpedance and Bioelectricity Basics , 2000 .

[36]  T. Nakamura,et al.  Inverse distribution of serum sodium and potassium in uncontrolled inpatients with diabetes mellitus. , 1999, Endocrine journal.

[37]  Øyvind Stavdahl,et al.  Kalman Smoothing for Objective and Automatic Preprocessing of Glucose Data , 2019, IEEE Journal of Biomedical and Health Informatics.

[38]  N. Rakıcıoğlu,et al.  The effects of meal glycemic load on blood glucose levels of adults with different body mass indexes , 2017, Indian journal of endocrinology and metabolism.

[39]  K. Chinen,et al.  New equivalent-electrical circuit model and a practical measurement method for human body impedance. , 2015, Bio-medical materials and engineering.