Type 2 Diabetes
Conditions
Keywords
Type 2 diabetes, Artificial intelligence, Wearable technology, Cardiovascular complications, Digital biomarker
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.
Interventions
DEVICEHOP watch
A multisensor smartwatch that includes neurophysiological sensors such as heart rate sensor to monitor the vitals of the participant.
Sponsors
HOP-Child Technologies Inc
MedTeq
Boehringer Ingelheim
McGill University Health Centre/Research Institute of the McGill University Health Centre
Study design
Observational model
CASE_CONTROL
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
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. Any person who does not meet the above criteria or who refuses to participate 2. Undergoing chemotherapy or dialysis 3. Currently in palliative care 4. Any person who does not have an email address 5. Control group only a. HbA1c \>= 6.5% or is diagnosed with T2DM
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Presence or absence of T2DM | Cross sectional based on a single clinic visit with device worn for an estimated 24 hours | As defined by HbA1c \> 6.5 %, known history of T2DM, or on antihyperglycemic therapies |
| Glycemic control amongst people with established T2DM. | Cross sectional based on a single clinic visit with device worn for an estimated 24 hours | As defined by HbA1c % |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Glycemic control. | Cross sectional based on a single clinic visit with device worn for an estimated 24 hours | Whether the digital biomarker as identified by the wrist-worn device differs between participants who have T2DM with glycemic control while using antihyperglycemic medications versus participants who do not have T2DM with baseline HbA1c \< 6.5%. Glycemic control is defined as HbA1c \< 6.5% |
| Change in glycemic control. | On average the change will be evaluated over 3-6 months | Whether changes provided by the digital biomarker also correlate with changes in HbA1c after initiation of antihyperglycemic treatments in the same participant over time. Change in glycemic control as measured by HbA1c % with specific antihyperglycemic medication |
Countries
Canada
Outcome results
None listed