An Exploratory Study to Assess the Accuracy in the Normo- to Hyperglycemic Range of the Spiden Non-invasive Continuous Glucose Monitor "Clinical Demo" (niCGM), in Trial Participants with Type 1 or Type 2 Diabetes
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Type 1 Diabetes
- Sponsor
- Liom Health AG
- Enrollment
- 80
- Locations
- 1
- Primary Endpoint
- Glucose changes will be measured noninvasively and transcutaneously in dynamic states of glycaemia
- Status
- Recruiting
- Last Updated
- last year
Overview
Brief Summary
This is a single-centre, multiple cohort, open study.
Detailed Description
The study will include 5 cohorts. After each cohort, optimisation of the Spiden Clinical Demo 2.0 system and machine learning models may be pursued before the next cohort is started. Trial participants with type 1 or type 2 diabetes mellitus.
Investigators
Eligibility Criteria
Inclusion Criteria
- •Male or female trial participant with clinically diagnosed type 1 or type 2 diabetes for at least 1 year.
- •Age between 18 and 65 years, both inclusive.
- •Treated with insulin and/or oral antidiabetic drugs (OADs; type 2 only), multiple dosing insulin therapy (MDI), continuous subcutaneous insulin infusion (CSII) or a hybrid closed loop system.
Exclusion Criteria
- •Known or suspected hypersensitivity to any of the components of the Liom Clinical Demo 2.
- •Trial participant with any injury, infection, atypical skin condition (e.g., hyperkeratosis, hyperpigmentation) of or tattoo on the wrists.
- •Presence or history of a cardiovascular disease including stable and unstable angina pectoris, myocardial infarction, transient ischaemic attack, stroke, cardiac decompensation, clinically significant arrhythmias or clinically significant conduction disorders.
Outcomes
Primary Outcomes
Glucose changes will be measured noninvasively and transcutaneously in dynamic states of glycaemia
Time Frame: The data is collected during the study procedure (up to 5 hours)
Spectroscopic research platform and associated computational models will be used to detect and track glucose changes noninvasively and transcutaneously in dynamic states of glycaemia