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.

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.

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

All data have already gone through a careful process of de-identification. Data can be made available after study completion for the purposes of further research, and to develop and validate the model after quality checks and Secondary analyses. Data will be available to all investigators who provide a sound proposal, as well case-by-case basis at the discretion of Primary Sponsor and PI Dr Thomas Telfer.

De-identified data is expected to be available after study completion and following publication of results, with no determined end date.

Data obtained from this study will be made available after approval from PI Dr Thomas Telfer.

Scimita ventures t.telfer@scimitaventures.com

+61 481848190