Translating Biometric Data Into Blood Glucose Levels

Diabetes Type 2 Clinical Trial

Official title:

Non-invasive Monitoring to Translate the Biometric Data of Participants With Diabetes Into Blood Glucose Levels

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04946188
• Other study ID #: 2020-02-099
• Status: Completed
• Phase: N/A
• Start date: July 21, 2020
• Est. completion date: December 14, 2020

Study information

• Verified date: July 2021
• Source: Scimita Operations Pty Ltd.
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

This study is designed to assist with the development of a first, truly non-invasive technology for blood glucose monitoring, which will have the potential to eliminate the need for painful finger pricking or expensive continuous blood glucose monitor use. The purpose of this study is to collect biometric data, such as bioimpedance (how well the body impedes electric current flow), from participants who are living with type 2 diabetes. A proof-of-concept prototype (non-invasive continuous glucose monitor; NI-CGM) will be used to collect this biometric data. The data will then be used to develop and refine a computer model that can be used to predict blood glucose levels (BGLs). Individuals with diabetes experience a great range of blood BGLs throughout their daily life and activities, therefore it is essential to gather biometric data corresponding to this large range to build a computer model, to ensure model reliability.

Description:

The study will be conducted over a two-week period where the participants are required to wear the prototype, that will continuously collect the biometric data. Participants are required to use this device together with two existing commercially available blood glucose meters that are considered management routine for diabetes (an Abbott FreeStyle Libre and an Accu-Chek® Mobile), throughout the duration of the study. The majority of the study is carried out independently, by the participants, where they wear the prototype throughout their daily life and activities. The data collected from the non-invasive custom-built device and the existing blood glucose meters will be used to develop a computer model that will allow for blood glucose levels to be predicted, over time.

The study will not interfere with any of the participants' diabetes management plans provided to them, by their regular doctor, under regular care, such as medications, diet and current use of blood glucose monitoring.

It is hypothesised that the bioimoedance data collected using the non-invasive prototype device, in conjunction with existing devices used in diabetes management, will enable the development of a computer model that allows for blood glucose levels to be predicted in people with type 2 diabetes.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 14
• Est. completion date: December 14, 2020
• Est. primary completion date: December 14, 2020
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 70 Years

Eligibility

Inclusion Criteria:

• Aged 18 - 70 years

• Physician diagnosis of Type 2 diabetes

• Haemoglobin A1c (HbA1c) range between 7 - 10%

• Body mass index between 20 - 40

• Regularly eats 3 meals per day (breakfast, lunch, and dinner)

• Technologically literate (e.g. able to use Apps, smart phones)

• Able to commit to attending the Sponsor site

• Able to commit to wearing a non-invasive, custom-built device through most daily activities

• Currently self-monitoring their BGL and able to commit to taking measurements at least 6 times per day

• Proficiency in reading and writing in English

Exclusion Criteria:

• Currently on insulin therapy (other than long-acting insulin therapy)

• Currently pregnant, pregnant in the last 6 months, or planning a pregnancy

• Currently breastfeeding

• Current smoker

• Any other confounding major disease or condition as deemed appropriate by investigator, determined by review of medical history and/or patient reported medical history

• Clinically unstable or rapidly progressing diabetic retinopathy, neuropathy, and/or frequent nausea, bloating or vomiting, sever gastroesophageal reflux, or early satiety.

• Multiple medications (taking more than 10 medications is often an indicator of having multiple major comorbidities which is an exclusion criteria. Furthermore, we want to exclude potential multiple drug interactions with blood glucose levels which may impact results of study)

• Currently on active curative treatments for cancer

• Currently receiving systemic glucocorticoid therapy

• Using lipid-lowering medication at a dose that has not been stable for the past 3 months

• History of reactions to alcohol wipes, antiseptics, or adhesives (isobornyl acrylate which is the adhesive used for attachment of Freestyle Libre sensors and may cause contact dermatitis)

• Using an insulin pump

• Pacemaker fitted

• Fasting C-peptide levels below 0.5 ng/mL or above 2.0 ng/mL

• Has had an episode of diabetic ketoacidosis in the past 6 months

• Has had an episode of severe hypoglycemia within the past 6 months

• Currently unstable blood glucose control

• Receiving dialysis treatment

• Has had a blood transfusion or severe blood loss within the past 3 months

• Unwilling to self-monitor their BGL (at least 6 measurement, daily)

• Currently participating in another clinical study

• Known to the Investigators

• Other investigator-determined criteria making participants unsuitable for participation

Related Conditions & MeSH terms

• Diabetes Mellitus, Type 2
• Diabetes Type 2

Intervention

Device:
• Opuz NICGM
A wearable and non-invasive prototype device that allows for measurement of bioimpedance data with the aim to help develop a mathematical model to predict blood glucose levels.

Locations

Country	Name	City	State
Australia	Scimita Operations	Sydney	New South Wales

Sponsors (1)

Lead Sponsor	Collaborator
Scimita Operations Pty Ltd.

Country where clinical trial is conducted

Australia, 

References & Publications (9)

Cho NH, Shaw JE, Karuranga S, Huang Y, da Rocha Fernandes JD, Ohlrogge AW, Malanda B. IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract. 2018 Apr;138:271-281. doi: 10.1016/j.diabres.2018.02.023. Epub 2018 Feb 26. — View Citation

D. K. Kamat, D. Bagul, and P. M. Patil, "Blood Glucose Measurement Using Bioimpedance Technique," Adv. Electron., vol. 2014, pp. 1-5, 2014, doi: 10.1155/2014/406257

Ding S, Schumacher M. Sensor Monitoring of Physical Activity to Improve Glucose Management in Diabetic Patients: A Review. Sensors (Basel). 2016 Apr 23;16(4). pii: E589. doi: 10.3390/s16040589. Review. — View Citation

Mueller M, Talary MS, Falco L, De Feo O, Stahel WA, Caduff A. Data processing for noninvasive continuous glucose monitoring with a multisensor device. J Diabetes Sci Technol. 2011 May 1;5(3):694-702. — View Citation

N. D. Nanayakkara, S. C. Munasingha, and G. P. Ruwanpathirana, "Non-invasive blood glucose monitoring using a hybrid technique," in MERCon 2018 - 4th International Multidisciplinary Moratuwa Engineering Research Conference, pp. 7-12, 2018, doi: 10.1109/MERCon.2018.8421885

P. Daarani & A.Kavithamani, "Blood glucose level monitoring by noninvasive method using near infra red sensor," Int. J. Latest Trends Eng. Technol., vol. IRES, no. 1, 2017, doi: 10.21172/1.ires.19

Tura A. Noninvasive glycaemia monitoring: background, traditional findings, and novelties in the recent clinical trials. Curr Opin Clin Nutr Metab Care. 2008 Sep;11(5):607-12. doi: 10.1097/MCO.0b013e328309ec3a. Review. — View Citation

Valensi P, Extramiana F, Lange C, Cailleau M, Haggui A, Maison Blanche P, Tichet J, Balkau B; DESIR Study Group. Influence of blood glucose on heart rate and cardiac autonomic function. The DESIR study. Diabet Med. 2011 Apr;28(4):440-9. doi: 10.1111/j.1464-5491.2010.03222.x. — View Citation

Villena Gonzales W, Mobashsher AT, Abbosh A. The Progress of Glucose Monitoring-A Review of Invasive to Minimally and Non-Invasive Techniques, Devices and Sensors. Sensors (Basel). 2019 Feb 15;19(4). pii: E800. doi: 10.3390/s19040800. Review. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Generation of a predictive models for determining blood glucose levels	Performance of computer models for blood glucose level estimation using collected bioimpedance spectroscopy data.	at 14 days post introduction of intervention
Primary	Validation of predictive model for determining blood glucose levels	Performance of predictive models will be evaluated using the consensus error grid. Mean Absolute Relative Difference (MARD) and Consensus Error Grid (CEG) distribution.	at 14 days post introduction of intervention

External Links

•   National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK).
•   World Health Organisation. (2018, Oct. 30). Diabetes