Clinical trial NCT05482958: DECIDE-CV Using AI
Official title: Delivering Evidence Based Care Using Artificial Intelligence to Patients With Diabetes and CardioVascular Comorbidities: The DECIDE-CV Innovation Project
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Type 2 Diabetes
Keywords: Type 2 diabetes
Artificial intelligence
Wearable technology
Cardiovascular complications
Digital biomarker
Lead sponsor: McGill University Health Centre/Research Institute of the McGill University Health Centre
Sponsor class: OTHER
Collaborators: name: HOP-Child Technologies Inc
name: MedTeq
name: Boehringer Ingelheim
First posted: 2022-08-01
Enrollment: 126
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_CONTROL
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: The investigators will recruit participants with T2DM (case group; n=72) and without T2DM (control group; n=54).

Brief summary: The purpose of this study is to identify digital biomarkers associated with type 2 diabetes mellitus (T2DM) by combining sensor data from a wrist-worn wearable and clinical data. This will be done by recruiting patients with and without diabetes within the cardio-metabolic clinics a the MUHC. Consented patients will be provided with a HOP Technologies (HOP) watch in this project across two observation periods. The Watch-HOP platform facilitates the development of predictive algorithms built with data collected in a clinical setting or at home in a passive (sensors) and active (self-assessments) way. Data from the Watch-Hop will be analyzed using machine learning strategies to determine associations with clinical measures of T2DM.

Detailed description: The epidemic of type 2 diabetes mellitus (T2DM) continues to increase. Sensor technologies and artificial intelligence present us with an opportunity to identify patients suffering from T2DM and to optimize their treatment.

Specifically, our primary objective is to identify digital biomarkers associated with T2DM by combining sensor data from a wrist-worn wearable and clinical data.

Eligibility criteria: Inclusion Criteria:

1. Age \> 18 years
2. Able to follow-up with study protocol schedule
3. Life expectancy \> 1 year
4. Case group only a. HbA1c \>= 6.5% or is diagnosed with T2DM

Exclusion Criteria:

1. Age \> 18 years
2. Able to follow-up with study protocol schedule
3. Life expectancy \> 1 year
4. Case group only a. HbA1c \>= 6.5% or is diagnosed with T2DM

Primary outcomes:
measure: Presence or absence of T2DM
timeFrame: Cross sectional based on a single clinic visit with device worn for an estimated 24 hours
measure: Glycemic control amongst people with established T2DM.
timeFrame: Cross sectional based on a single clinic visit with device worn for an estimated 24 hours
Secondary outcomes:
measure: Glycemic control.
timeFrame: Cross sectional based on a single clinic visit with device worn for an estimated 24 hours
measure: Change in glycemic control.
timeFrame: On average the change will be evaluated over 3-6 months
Other outcomes:
Locations:
city: Montreal
country: Canada
facility: McGill University health Center
state: Quebec
Study officials:
affiliation: McGill University Health Centre/Research Institute of the McGill University Health Centre
name: Abhinav Sharma
role: PRINCIPAL_INVESTIGATOR
References: