An Observational Study Comparing RE.DOCTOR Vitals® Software Accuracy

RE.DOCTOR LLC1 个研究点分布在 1 个国家目标入组 1,000 人开始时间: 2024年3月1日最近更新: 2024年2月5日

适应症

试验速览

阶段: 不适用
状态: 尚未招募
发起方
入组人数: 1,000
试验地点: 1
主要终点: Systolic and Diastolic Blood Pressure Measurement taken with RE.DOCTOR Vitals Software

研究概览研究设计入排标准结局指标研究者研究点记录与标识符相似试验

研究概览

简要总结

The purpose of this observational trial is to advance digital health monitoring through the analysis of Photoplethysmography (PPG) waveforms collected via RE.DOCTOR Vitals software. The study aims to collect a diverse and extensive dataset of PPG waveforms, alongside traditional physiological measurements, for the purpose of enhancing existing algorithms and machine learning models used in health monitoring. The primary focus is on improving the accuracy and reliability of algorithms in interpreting PPG data to derive meaningful insights into physiological parameters. The main questions it aims to answer are:

How can extensive datasets of PPG waveforms be utilized to enhance existing algorithms and machine learning models?
How do correlations between PPG waveforms and key physiological parameters (such as glucose levels, blood pressure, heart rate, respiration rate) contribute to refining algorithms for more accurate and reliable health predictions?

Participants will be asked to:

Continuously monitor their health using smartphone applications.
Allow the collection of PPG waveforms in diverse settings.
Engage in tasks related to monitor health parameters using medically approved devices

详细描述

1.1 Introduction Physiological parameters, such as glucose levels, blood pressure, blood oxygen, respiration rate, and pulse, are critical indicators of an individual's health. Monitoring these parameters is crucial for early detection and management of various health conditions. Photoplethysmography (PPG) has emerged as a non-invasive and convenient method for capturing real-time cardiovascular information. In the context of this study, the focus is on leveraging PPG data collected through smartphone applications to enhance the performance of existing algorithms and machine learning models.

1.2 Rationale for the Study The rationale for this study lies in the potential of PPG data to contribute significantly to the refinement of algorithms and machine learning models for health monitoring. While existing models have shown promise, collecting more diverse and extensive datasets can address limitations and improve their accuracy and reliability. By understanding the nuances of PPG waveforms in relation to key physiological parameters, the investigators aim to advance the field of digital health and contribute to the development of more effective monitoring solutions.

1.3 Significance of the Study The significance of this observational trial extends to the optimization of health monitoring algorithms. Improving the accuracy of existing models through extensive data collection can lead to more reliable insights into an individual's health status. The study's findings may influence the development of algorithmic solutions for personalized health monitoring, paving the way for more precise and timely interventions based on real-time physiological data.

Objectives

2.1 Primary Objective

研究设计

研究类型: Observational
观察模型: Cohort
时间视角: Prospective

入排标准

年龄范围: 13 Years 至 80 Years（Child, Adult, Older Adult）
性别: All
接受健康志愿者: 是

入选标准

•Age between 13-80 years.
•Ownership of a smartphone compatible with the designated data collection application.
•Willingness to use the smartphone application for continuous PPG data collection.
•Ability to provide informed consent for participation in the study.
•No known history of conditions or medications that significantly affect PPG waveforms (e.g., severe arrhythmias, use of beta-blockers).
•Healthy individuals and individuals with diagnosed CVD diseases, individuals with diagnosed pre-diabetes, diabetes I, diabetes II

排除标准

•Age outside the specified range of 13-80 years.
•Lack of ownership of a compatible smartphone.
•Inability or unwillingness to use the designated smartphone application for data collection.
•Known history of severe arrhythmias or use of medications affecting cardiovascular function.
•Any condition that, in the judgment of the investigator, may compromise the participant's ability to complete the study.