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Translating Biometric Data Into Blood Glucose Levels

Primary Purpose

Diabetes Type 2

Status
Completed
Phase
Not Applicable
Locations
Australia
Study Type
Interventional
Intervention
Opuz NICGM
Sponsored by
Scimita Operations Pty Ltd.
About
Eligibility
Locations
Arms
Outcomes
Full info

About this trial

This is an interventional other trial for Diabetes Type 2 focused on measuring Non-invasive, Blood Glucose Levels, Monitoring

Eligibility Criteria

18 Years - 70 Years (Adult, Older Adult)All SexesDoes not accept healthy volunteers

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

Sites / Locations

  • Scimita Operations

Arms of the Study

Arm 1

Arm Type

Experimental

Arm Label

Opuz NICGM

Arm Description

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.

Outcomes

Primary Outcome Measures

Generation of a predictive models for determining blood glucose levels
Performance of computer models for blood glucose level estimation using collected bioimpedance spectroscopy data.
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.

Secondary Outcome Measures

Full Information

First Posted
April 22, 2021
Last Updated
July 15, 2021
Sponsor
Scimita Operations Pty Ltd.
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1. Study Identification

Unique Protocol Identification Number
NCT04946188
Brief Title
Translating Biometric Data Into Blood Glucose Levels
Official Title
Non-invasive Monitoring to Translate the Biometric Data of Participants With Diabetes Into Blood Glucose Levels
Study Type
Interventional

2. Study Status

Record Verification Date
July 2021
Overall Recruitment Status
Completed
Study Start Date
July 21, 2020 (Actual)
Primary Completion Date
December 14, 2020 (Actual)
Study Completion Date
December 14, 2020 (Actual)

3. Sponsor/Collaborators

Responsible Party, by Official Title
Sponsor
Name of the Sponsor
Scimita Operations Pty Ltd.

4. Oversight

Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
No
Data Monitoring Committee
No

5. Study Description

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.

6. Conditions and Keywords

Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Diabetes Type 2
Keywords
Non-invasive, Blood Glucose Levels, Monitoring

7. Study Design

Primary Purpose
Other
Study Phase
Not Applicable
Interventional Study Model
Single Group Assignment
Masking
None (Open Label)
Allocation
N/A
Enrollment
14 (Actual)

8. Arms, Groups, and Interventions

Arm Title
Opuz NICGM
Arm Type
Experimental
Arm Description
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.
Intervention Type
Device
Intervention Name(s)
Opuz NICGM
Other Intervention Name(s)
Opuz Device, Opuz Ring
Intervention Description
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.
Primary Outcome Measure Information:
Title
Generation of a predictive models for determining blood glucose levels
Description
Performance of computer models for blood glucose level estimation using collected bioimpedance spectroscopy data.
Time Frame
at 14 days post introduction of intervention
Title
Validation of predictive model for determining blood glucose levels
Description
Performance of predictive models will be evaluated using the consensus error grid. Mean Absolute Relative Difference (MARD) and Consensus Error Grid (CEG) distribution.
Time Frame
at 14 days post introduction of intervention

10. Eligibility

Sex
All
Minimum Age & Unit of Time
18 Years
Maximum Age & Unit of Time
70 Years
Accepts Healthy Volunteers
No
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. 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
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Thomas Telfer, PhD (Med)
Organizational Affiliation
Scimita Operations
Official's Role
Principal Investigator
First Name & Middle Initial & Last Name & Degree
Farid Sanai, PhD (Med)
Organizational Affiliation
Scimita Operations
Official's Role
Study Chair
Facility Information:
Facility Name
Scimita Operations
City
Sydney
State/Province
New South Wales
ZIP/Postal Code
2044
Country
Australia

12. IPD Sharing Statement

Plan to Share IPD
Yes
IPD Sharing Plan Description
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.
IPD Sharing Time Frame
De-identified data is expected to be available after study completion and following publication of results, with no determined end date.
IPD Sharing Access Criteria
Data obtained from this study will be made available after approval from PI Dr Thomas Telfer. Scimita ventures t.telfer@scimitaventures.com +61 481848190
Citations:
PubMed Identifier
29496507
Citation
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.
Results Reference
background
PubMed Identifier
30781431
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):800. doi: 10.3390/s19040800.
Results Reference
background
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
Results Reference
background
PubMed Identifier
18685457
Citation
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.
Results Reference
background
Citation
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
Results Reference
background
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
Results Reference
background
PubMed Identifier
27120602
Citation
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.
Results Reference
background
PubMed Identifier
21204961
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.
Results Reference
background
PubMed Identifier
21722585
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. doi: 10.1177/193229681100500324.
Results Reference
background
Links:
URL
https://www.who.int/news-room/fact-sheets/detail/diabetes
Description
World Health Organisation. (2018, Oct. 30). Diabetes
URL
https://www.niddk.nih.gov/health-information/diabetes/overview/managing-diabetes/continuous-glucose-monitoring#benefits
Description
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK).

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Translating Biometric Data Into Blood Glucose Levels

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