MyPHD

Name: MyPHD
Author: Stanford University

Stanford University

VALIDATED

Proven effective in research studies

For:General Public & EnthusiastsPatients & Caregivers

Free

Download on the App Store Get it on Google Play

App Summary

MyPHD is a personal health management app that collects and displays data from wearable devices, built upon a research platform designed to analyze physiological signals for early detection of health changes. The associated research, including a prospective study of a real-time alerting system (N=3,318), found that analyzing smartwatch data could detect 80% of SARS-CoV-2 infections at a median of 3 days before symptom onset. The authors conclude that this approach of analyzing wearable data can provide advance warning for respiratory infections like COVID-19, as well as other physiological stressors.

App Screenshots

Detailed Description

Functionality & Mechanism

MyPHD integrates with consumer wearable devices and health platforms, including Apple HealthKit, to aggregate longitudinal physiological data such as heart rate, step count, and sleep patterns. The system de-identifies and encrypts this data for secure transfer to the open-source Personal Health Dashboard (PHD) platform for large-scale analysis. The user interface provides a personal dashboard for real-time visualization of collected health metrics, facilitating continuous monitoring and engagement with personal health information.

Evidence & Research Context

The underlying Personal Health Dashboard (PHD) is an open-source framework designed for secure, scalable aggregation and analysis of multi-modal biomedical data from wearables, clinical records, and omics.
A prospective study (N=3,318) demonstrated that a real-time alerting system built on the platform identified 80% of SARS-CoV-2 infections, with a median alert time of 3 days pre-symptom onset.
An initial retrospective analysis of 32 individuals with COVID-19 found that 81% exhibited detectable alterations in heart rate, daily steps, or sleep patterns around infection onset.
The associated research acknowledges that physiological alerts may be triggered by non-infectious events, including travel, alcohol consumption, or other stressors, necessitating contextual interpretation.

Intended Use & Scope

MyPHD is intended for research participants contributing to large-scale health studies. Its primary utility is the secure, longitudinal collection of physiological data for cohort-level analysis and the development of predictive health algorithms. The system is not a diagnostic tool; any generated alerts require interpretation and follow-up with a qualified healthcare professional.

Studies & Publications

3 publications

Peer-reviewed research associated with this app.

Effectiveness/Outcome Study

Real-time alerting system for COVID-19 and other stress events using wearable data

Alavi et al. (2021) · Nature Medicine

Detected 80% of COVID-19 infections in real-time, with early warning signals appearing 3 days before symptoms.

Early detection of infectious diseases is crucial for reducing transmission and facilitating early intervention. In this study, we built a real-time smartwatch-based alerting system that detects aberrant physiological and activity signals (heart rates and steps) associated with the onset of early infection and implemented this system in a prospective study. In a cohort of 3,318 participants, of whom 84 were infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), this system generated alerts for pre-symptomatic and asymptomatic SARS-CoV-2 infection in 67 (80%) of the infected individuals. Pre-symptomatic signals were observed at a median of 3 days before symptom onset. Examination of detailed survey responses provided by the participants revealed that other respiratory infections as well as events not associated with infection, such as stress, alcohol consumption and travel, could also trigger alerts, albeit at a much lower mean frequency (1.15 alert days per person compared to 3.42 alert days per person for coronavirus disease 2019 cases). Thus, analysis of smartwatch signals by an online detection algorithm provides advance warning of SARS-CoV-2 infection in a high percentage of cases. This study shows that a real-time alerting system can be used for early detection of infection and other stressors and employed on an open-source platform that is scalable to millions of users. In a prospective study, a smartwatch-based alerting system was able to detect pre-symptomatic and asymptomatic SARS-CoV-2 infection in a high percentage of cases.

Development/Design Paper

A scalable, secure, and interoperable platform for deep data-driven health management

Bahmani et al. (2021) · Nature Communications

Describes the research-driven development of this app

The large amount of biomedical data derived from wearable sensors, electronic health records, and molecular profiling (e.g., genomics data) is rapidly transforming our healthcare systems. The increasing scale and scope of biomedical data not only is generating enormous opportunities for improving health outcomes but also raises new challenges ranging from data acquisition and storage to data analysis and utilization. To meet these challenges, we developed the Personal Health Dashboard (PHD), which utilizes state-of-the-art security and scalability technologies to provide an end-to-end solution for big biomedical data analytics. The PHD platform is an open-source software framework that can be easily configured and deployed to any big data health project to store, organize, and process complex biomedical data sets, support real-time data analysis at both the individual level and the cohort level, and ensure participant privacy at every step. In addition to presenting the system, we illustrate the use of the PHD framework for large-scale applications in emerging multi-omics disease studies, such as collecting and visualization of diverse data types (wearable, clinical, omics) at a personal level, investigation of insulin resistance, and an infrastructure for the detection of presymptomatic COVID-19.

In the Media

Stanford's New Leap in Personalized Medicine and Trials

Stanford University's Dr. Michael Snyder developed MyPHD to advance personalized medicine through deep longitudinal biomarker analysis, moving beyond population averages to individual biological blueprints. Snyder's research debunked the myth that average human temperature is 98.6Â°F, presenting Stanford study data showing the true average as 97.7Â°F with significant individual variation. His team profiled over 100 individuals for nearly a decade to identify distinct "aging types" that could transform clinical trials from reactive treatment to proactive prevention.

ClinicaltrialvanguardRead article

How the death of his wife drives data scientist to improve the system

Stanford's Amir Bahmani developed the MyPHD app to bridge the gap between data science and medicine, driven by his wife's misdiagnosed cancer death in 2014. "If they had collected the data, they would have seen that something was going on internally, that there was a big shift underway that occurs with cancer patients," said Bahmani, director of the Stanford Deep Data Research Center. The app aims to create a common language between engineers, biologists and physicians to advance precision medicine approaches.

STANFORDRead article

Exciting new updates to COVID-19 wearables study and MyPHD study app

Stanford researchers developed MyPHD to serve as a research platform for collecting high-quality health data across various studies from infectious illnesses to chronic diseases, using wearable technology for early detection. Their COVID-19 early detection study published in Nature Biomedical Engineering showed that "63% of the COVID-19 cases could have been detected before symptom onset in real-time." Stanford has extended the app's use to research institutions across the country to advance precision health research.

STANFORDRead article