study of re.doctor vitals® software accuracy

Conditions: Respiratory Disorder; Cardiovascular Diseases; Diabetes; Pre-Diabetes; Primary Care

Summary: 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

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. 2. objectives 2.1 primary objective primary objective: 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. rationale: the primary objective aligns with the overarching goal of optimizing existing algorithms. by collecting a comprehensive dataset, we aim to provide a robust foundation for refining and training machine learning models, ultimately enhancing their capacity to accurately interpret and correlate ppg waveforms with key physiological parameters. 2.2 secondary objectives secondary objectives: to explore variations in ppg waveforms across diverse demographic groups and health conditions to ensure the generalizability of algorithmic improvements. to assess the impact of increased data volume on the performance and scalability of existing algorithms. to validate the optimized algorithms through comparison with traditional physiological measurements and clinical assessments. to engage participants in the study for feedback on the usability and acceptability of the smartphone application for continuous health monitoring. rationale: the secondary objectives complement the primary goal by addressing specific aspects of algorithmic improvement. exploring demographic variations ensures that the refined algorithms remain applicable across diverse populations. assessing the impact of increased data volume and validating against traditional measurements contribute to the overall robustness and reliability of the optimized algorithms. following informed consent, participants will be assigned to sub-protocols for the assessment of the re.doctor vitals software. the study aims to evaluate the accuracy and usability of the software in monitoring vital signs and blood glucose. recruitment criteria: participants with the capacity to consent may be assigned to one or to more than one cohort. all individuals will undergo the following measurements: * blood pressure measurement * heart rate measurement * respiration rate measurement * blood oxygen measurement * blood glucose measurement procedures: pre-measurement observation: demographic and medical history questions. routine observations: measurements taken using standard clinical equipment. measurement taken using similar tools (phone app using ppg) ppg signal capture and health parameters estimated (heart rate, respiration rate, blood pressure, blood oxygen, blood glucose) of the participant's finger using the re.doctor vitals app during approx 60 seconds. blood glucose measurement using standard procedures. post-measurement observation: completion of post-measurement observation questions. questionnaire: a subset of participants from both sub-protocols will be asked to complete a questionnaire related to vital sign monitoring, including preferences for re.doctor vitals or other technologies. overall study protocol: the study comprises two sub-protocols - sub-protocol a for blood pressure, heart rate, and respiration rate measurements, and sub-protocol b for blood glucose measurements.

Eligibility criteria:
inclusion criteria: * 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 exclusion criteria: * 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.