Diabetes Type 2
Conditions
Keywords
Non-invasive, Blood Glucose Levels, Monitoring
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.
Interventions
DEVICEOpuz 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.
Sponsors
Scimita Operations Pty Ltd.
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
OTHER
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to 70 Years
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Generation of a predictive models for determining blood glucose levels | at 14 days post introduction of intervention | Performance of computer models for blood glucose level estimation using collected bioimpedance spectroscopy data. |
| Validation of predictive model for determining blood glucose levels | at 14 days post introduction of intervention | Performance of predictive models will be evaluated using the consensus error grid. Mean Absolute Relative Difference (MARD) and Consensus Error Grid (CEG) distribution. |
Countries
Australia
Outcome results
None listed