Data Collection and Evaluation of OptiBP Under Investigational Use
- Conditions
- General Population
- Interventions
- Device: OptiBP Study app
- Registration Number
- NCT06017687
- Lead Sponsor
- Biospectal
- 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.
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- All
- Target Recruitment
- 500000
- Be at least 18 years old (at least 19 years old in Alabama and Nebraska, at least 21 years old in Puerto Rico);
- Live in the United States of America
- Have an Android smartphone
- Have access to an arm-worn blood pressure monitor (cuff)
- Have access to the Google Play store to download the OptiBP study app on their phone
- Be comfortable communicating in written and spoken English
- Be willing and able to provide informed consent to participate in the study
- Known contact dermatitis to nickel/chromium
- Lesion or deficiency on hand, preventing placing a finger on the smartphone camera
- Known dysrhythmia like bigeminy, trigeminy, isolated VPB, atrial fibrillation
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- SINGLE_GROUP
- Arm && Interventions
Group Intervention Description Principal Arm OptiBP Study app Each participant will use the OptiBP Study app on their smartphone
- Primary Outcome Measures
Name Time Method 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 Outcome Measures
Name Time Method 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.
Trial Locations
- Locations (1)
Decentralized Trial
🇺🇸Truckee, California, United States