Translating Biometric Data Into Blood Glucose Levels

NCT ID: NCT04946188

Last Updated: 2021-07-22

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 14 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2020-07-21
• Study Completion Date: 2020-12-14

Brief 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.

Detailed 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.

Conditions

Diabetes Type 2

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: OTHER
• Blinding Strategy: NONE

Study Groups

Opuz NICGM

Participants will be provided with one non-invasive, custom-built prototype device (study device), which they will use throughout their day-to-day life/activities over the study period.

Group Type: EXPERIMENTAL

Opuz NICGM

Intervention Type: DEVICE

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.

Interventions

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.

Intervention Type: DEVICE

Other Intervention Names

Opuz Device; Opuz Ring

Eligibility Criteria

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.
• 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

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 70 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Scimita Operations Pty Ltd.

INDUSTRY

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Thomas Telfer, PhD (Med)

Role: PRINCIPAL_INVESTIGATOR

Scimita Operations

Farid Sanai, PhD (Med)

Role: STUDY_CHAIR

Scimita Operations

Locations

Scimita Operations

Sydney, New South Wales, Australia

Countries

Australia

References

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.

Reference Type: BACKGROUND

PMID: 29496507 (View on PubMed)

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Reference Type: BACKGROUND

PMID: 30781431 (View on PubMed)

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

Reference Type: BACKGROUND

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.

Reference Type: BACKGROUND

PMID: 18685457 (View on PubMed)

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

Reference Type: BACKGROUND

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

Reference Type: BACKGROUND

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):589. doi: 10.3390/s16040589.

Reference Type: BACKGROUND

PMID: 27120602 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 21204961 (View on PubMed)

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. doi: 10.1177/193229681100500324.

Reference Type: BACKGROUND

PMID: 21722585 (View on PubMed)

Provided Documents

Document Type: Informed Consent Form

View Document

Related Links

https://www.who.int/news-room/fact-sheets/detail/diabetes

World Health Organisation. (2018, Oct. 30). Diabetes

Other Identifiers

2020-02-099

Identifier Type: -

Identifier Source: org_study_id