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.
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.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
OTHER
Masking
NONE
Enrollment
500,000
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.
Decentralized Trial
Truckee, California, United States
RECRUITINGCollect 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.
Time frame: 12 months
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.
Time frame: 12 months
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