General Population Clinical Trial
Official title:
Data Collection and Evaluation of a Mobile, Machine-learning-based Solution for Blood Pressure Estimation - the OptiBP Study.
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.
Status | Recruiting |
Enrollment | 500000 |
Est. completion date | September 2026 |
Est. primary completion date | September 2024 |
Accepts healthy volunteers | Accepts Healthy Volunteers |
Gender | All |
Age group | 18 Years and older |
Eligibility | 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 |
Country | Name | City | State |
---|---|---|---|
United States | Decentralized Trial | Truckee | California |
Lead Sponsor | Collaborator |
---|---|
Biospectal |
United States,
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Collect Blood Pressure (BP) data and assess performance of smartphone based BP estimations models | 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. | 12 months | |
Secondary | Safety by assessing inconvenience and adverse events | 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. | 12 months |
Status | Clinical Trial | Phase | |
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