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AI Models for Non-invasive Glycaemic Event Detection Using ECG in Type 1 Diabetics

NCT05461144 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 30

Last updated 2022-07-15

No results posted yet for this study

Summary

This observational study aims to recruit up to thirty T1DM patients from a diabetic outpatient clinic at the University Hospital Coventry and Warwickshire for a two-phase study. The first phase involves attending an inpatient protocol for up to thirty-six hours in a calorimetry room at the Human Metabolism Research Unit under controlled conditions, followed by a phase of free-living, for up to three days, in which participants will go about their normal daily activities without restriction. Throughout the study, the participants will wear commercially available wearable sensors to measure and record physiological signals (e.g., electrocardiogram and continuous glucose monitor). Data collected will be used to develop and validate an AI model using state-of-the-art deep-learning methods for the purpose of non-invasive glycaemic event detection.

Conditions

  • Metabolic Disease

Sponsors & Collaborators

  • University of Warwick

    collaborator OTHER

  • University Hospitals Coventry and Warwickshire NHS Trust

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2022-09-30
Primary Completion
2026-05-01
Completion
2027-05-01

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05461144 on ClinicalTrials.gov