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Feasibility of ECG-Based Glucose Monitoring System

Feasibility Assessment of Novel ECG-Based Continuous Glucose Monitoring System

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT06883682
Enrollment
165
Registered
2025-03-19
Start date
2025-02-10
Completion date
2025-09-30
Last updated
2025-03-21

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Diabetes Mellitus

Keywords

ECG, glucose, singular wings

Brief summary

This is a single-center, open-label clinical trial to assess the feasibility of using ECG information in estimating glucose level continuously in adult subjects.

Detailed description

About 165 subjects distributed in 3 categories according to the risk of developing events of hyperglycemia or hypoglycemia will be enrolled. Subjects will wear a portable ECG recorder to monitor ECG information and use 3 blood glucose monitoring methods to monitor blood glucose level over time. All data will be pooled together to train a specific artificial intelligence (AI) algorithm to conduct a blood glucose detection model using ECG features. The trial period is about 21 days.

Interventions

OTHERNo intervention

No intervention is conducted in this study.

Sponsors

Landseed International Hospital
CollaboratorUNKNOWN
Singular Wings Medical Co., Ltd.
Lead SponsorINDUSTRY

Study design

Observational model
COHORT
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes

Inclusion criteria

* Males and females, age 18 years or older. * Subjects can communicate clearly, fully understand the informed consent form, and are aware of the rights and obligations of the study. * Category 1: Subjects without a medical history of hyperglycemia / hypoglycemia or type 1 / type 2 diabetes. Category 2: Subjects having diabetes mellitus (DM) and HbA1c value of. 5.5% to 10%, with eGFR ≥ 60 mL/min. Category 3: Subjects having chronic kidney disease (CKD) and diabetes. mellitus (DM), with eGFR \< 60 mL/min. • Willing and able to participate in all aspects of the study.

Exclusion criteria

* Subjects suffering from insomnia or severe digestive system disorders within 3 months prior to the screening visit. * Subjects who are addicted to alcohol or caffeine. * Existing severe cardiac conditions such as recent myocardial infarction or advanced heart failure within 6 months prior to the screening visit. * Has experienced an episode of confirmed or suspected diabetic ketoacidosis (DKA) within 6 months prior to the screening visit. * Subjects who are vulnerable groups or with higher risks, such as HIV carriers, pregnant women or planning to become pregnant within the study duration, breast-feeding mothers, subjects with rare diseases, physical disabilities, incurable fatal diseases, dependents in nursing homes, incapacity, or intellectual or mental disabilities. * Has a concomitant disease or condition that may compromise subject safety such as unstable coronary heart disease, cystic fibrosis, serious psychiatric disorder, or any other uncontrolled medical conditions. * Any active infection or malignancy requiring acute therapy. * Having coagulation disorders. * Has known allergy to medical adhesives. * Subjects using any cardiac related implantable medical devices such as a pacemaker and so on. * Subjects on certain drugs known to cause arrhythmias or significantly impact kidney function or blood glucose levels (except for the current daily use medication). * Subjects have used defibrillators within 3 months prior to the screening visit. * Currently participating in another device or drug study. * Currently receiving dialysis treatment or planning to receive dialysis during the study. * Any other clinical condition that, in the investigator's judgment, would potentially compromise study compliance or the ability to evaluate safety/efficacy.

Design outcomes

Primary

MeasureTime frameDescription
To assess the accuracy of glucose level determined by the glucose detection model through ECG data and blood glucose monitoring values.14 daysPart of the ECG and glucose data will be used for AI model training, and the rest will be used for accuracy testing (such as through the comparison of mean absolute relative difference (MARD) between glucose levels from different methods).

Countries

Taiwan

Contacts

Primary ContactJasper Yang, Ph.D.
jasper@singularwings.com886 3667 5801
Backup ContactJesse Liao
jesse@singularwings.com886 3667 5801

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026