General Population
Conditions
Brief summary
The purpose of this study is to collect data to develop and evaluate the use of state-of-the-art machine learning approaches within a mobile phone application for the estimation of blood pressure.
Detailed description
Participants will simultaneously acquire blood pressure measurements through a cuff-based, automatic, over-the-counter blood pressure monitor on the upper arm, while recording an optical signal through the camera of a smartphone on the tip of the index of the opposite arm.
Interventions
DEVICEOptiBP Study app
Each participant will use OptiBP Study app to measure their blood pressure optically by applying their fingertip to their smartphone camera, and subsequently enter the blood pressure values obtained by measuring it with their own blood pressure cuff.
Sponsors
Biospectal
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
OTHER
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
1. Be at least 18 years old (at least 19 years old in Alabama and Nebraska, at least 21 years old in Puerto Rico); 2. Live in the United States of America 3. Have an Android smartphone 4. Have access to an arm-worn blood pressure monitor (cuff) 5. Have access to the Google Play store to download the OptiBP study app on their phone 6. Be comfortable communicating in written and spoken English 7. Be willing and able to provide informed consent to participate in the study
Exclusion criteria
1. Known contact dermatitis to nickel/chromium 2. Lesion or deficiency on hand, preventing placing a finger on the smartphone camera 3. Known dysrhythmia like bigeminy, trigeminy, isolated VPB, atrial fibrillation
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Collect Blood Pressure (BP) data and assess performance of smartphone based BP estimations models | 12 months | To collect data to train a machine-learning based solution for estimating blood pressure. Participants will simultaneously acquire blood pressure measurements through a cuff-based, automatic, over-the-counter blood pressure monitor at the upper arm, while recording an optical signal through the camera of a smartphone on the tip of the index of the opposite arm. The performance of the machine learning model will be assessed by calculating the mean and standard deviation of the error of blood pressure estimations versus cuff-based, automatic, over-the-counter blood pressure monitors. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Safety by assessing inconvenience and adverse events | 12 months | To identify potential use errors of an app that use the machine-learning model to estimate blood pressure. And to assess safety of the intervention. |
Countries
United States
Contacts
Primary ContactFrederic Frappereau
Outcome results
None listed