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Hi Lab Insiders!

As cities open up, it is thrilling to experience the waves of energy carried by the familiar, new, and long-missed faces of people.

In this issue of the newsletter, we would like to share some important updates with you:

  1. The preprint for Phase 2 of our COVID-19 wearable study is available now.

  2. Prof. Eric Topol talks about the opportunities and challenges in the future of precision medicine.

  3. Dr. Michael Snyder on Find My Fitness podcast with Dr. Rhonda Patrick, discussing continuous glucose monitoring and deep profiling for personalized medicine

  4. Our summer virtual workshop is still open to register and will launch on July 19th.

  5. Research to the People will join Ethan Perlstein, Founder of Perlara, on Clubhouse on July 15th, 14:00 PDT/ 17:00 EDT.

  6. XR Healthcare speaker series successfully wrapped up. Recordings for all 3 talks are available on Youtube.

If you are interested in contributing content to this newsletter, please get in touch with us:

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COVID-19 Phase 2 preprint header image and link to study sign up page

Preprint: Real-time Alerting System for COVID-19 Using Wearable Data

We are very excited to share with you our recent preprint for Phase 2 of our COVID-19 wearable study

Before we dive in, if you are part of our 3,246 study cohort, or have helped us make this study known to the world in any way, we are beyond grateful for your patience and trust. Thank you for helping us making this research study possible within 7 months! 

Why do we think this study is important?
  1. This is the 1st large-scale real-time monitoring and alerting system for detecting abnormal physiological changes, including COVID-19 and other respiratory illnesses, using smartwatch data.

  2. Our algorithms are agnostic for different smartwatches to send users alerts.

We believe there is a huge potential to scale our research and implement these findings for improving healthcare. Please reach out if you would be interested in supporting our work. 

Currently, we are still actively recruiting study participants for the COVID-19 wearable study. If you have a smartwatch, or know someone who may be interested, you can help us grow our study cohort by signing up, or tell your friends about our work and this newsletter!
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COVID-19 Phase 1 study header image and link to publication

A quick recap of the COVID-19 wearable study

The recent preprint demonstrates results from Phase 2 of the COVID-19 wearable study. If you are interested in learning more about both Phase 1 and Phase 2 studies, we have covered them in better detail in a previous newsletter:

  • Phase 1 study was published in Nature Biomedical Engineering in December 2020. The research team used retrospective wearable data to develop algorithms that could detect COVID-19 infection before symptoms emerge. The study shows 63% of COVID-19 positive cases could have been detected in real-time, with a median of 4 days prior to symptom onset. 

  • In Phase 2, the research team aimed to validate algorithms to detect presymptomatic and asymptomatic COVID-19 cases in real-time.

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Figure 1 of the preprint

What are our findings from the Phase 2 study?

  1. Our system demonstrates a 78% accuracy in alerting study participants who are pre-symptomatic or asymptomatic

    • Among 68 COVID-19 positive study participants who have wearable data, our algorithm (NightSignal) sent alerts to 53 of them at or before symptom onset/ diagnosis or diagnosis date in asymptomatic cases.

    • The alert is generated at a median of 3 days prior to symptom onset, to enable effective early self-isolation and testing

  1. The algorithms also identify signals resulting from vaccination:

    • Red alert is triggered after both vaccination doses or only one dose for Pfizer-BioNTech and Moderna vaccines.

    • For the first vaccine dose, the maximum resting heart rate overnight occurs on the first night after receiving Pfizer-BioNTech vaccine. 

    • For the second vaccine dose, the maximum resting heart rate overnight occurs on the first night or the second night after receiving Pfizer-BioNTech or Moderna vaccine.

    • In terms of reported symptoms, participants who received Pfizer-BioNTech vaccine reported fever after either dose; however, participants who received Moderna vaccine reported no fever after the first dose, but close to 60% of them reported fever after the second dose.

  1. An association between symptoms and signals was investigated:

    • Fatigue and poor sleep are common symptoms reported by study participants who received red alerts. However, aches and pains, headaches, coughs, and feeling ill were reported more frequently by COVID-19 positive study participants.

    • Stress, intense exercise, and alcohol intake are symptoms most reported by COVID-19 negative participants. Interestingly, there are more red alerts triggered among COVID-19 negative study participants during the winter holiday season. This is a consistent phenomenon observed in our previous study before the pandemic. This observation implies people may experience increased stress, alcohol intake, or travel during the holiday season.

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Webinar: Prof. Eric Topol on The Future of Precision Medicine - Opportunities and Challenges

We invited Prof. Eric Topol, the Director and Founder of Scripps Research Translational Institute, as a guest speaker to a new course in the Department of Genetics, Cloud Computing for Biology and Healthcare (GENE222/CS273C/BMI222). 

“I don’t like the term precision medicine because if you keep making the same mistakes over and over again, it is very precise. We need accurate medicine.”, says Prof. Eric Topol. In the U.S., there are more than 12 million diagnosis errors each year, which means most of us will encounter one medical error once a year. 

One contributor to this largely overwhelming number is human factors among healthcare practitioners, such as errors in interpreting medical images, fatigue after working intense and long hours, and cognitive bias.

This is where Artificial Intelligence (AI) can step in. Improving the accuracy and effectiveness of human efforts is what artificial intelligence is good at. Prof. Eric Topol went through the advancements in using deep learning models to reduce human errors, and to improve patient experience:

  1. Using AI on the human retina to diagnose kidney disease, control diabetes, predicting neural degenerative diseases, and predict the heart calcium score.

  2. Using machine vision to detect abnormal tissue in real time, and to decide whether the tissue needs biopsy.

  3. Paring AI with sensors to monitor the intensity of nursing support in ICU rooms to prevent falls, and to promote better hand washing in surgical settings.

  4. Using AI diagnostic tool to sequence critically ill neonates’ whole genome with interpretation in as fast as 13.5 hours

Despite the enormous transformation AI is creating across all healthcare professions, it also provides patients with autonomy in managing their own health. The implication of patient autonomy enabled by AI is going to significantly improve healthcare in areas where trained healthcare professionals are scarce. Here are some early examples:

  1. Comparing incoming heart rate data with personalized baseline established over time to notify smartwatch users of irregular heart rate patterns.

  2. An At-home UTI testing kit utilizing a smartphone app to analyze urine samples on a dipstick and deliver immediate results. 

  3. An average user without any training can create a high-resolution scan of organs within minutes within the convenience of their own home.

  4. AI health tool launched by Google can diagnose skin conditions using a smartphone app.

AI health tools give more precious time back to healthcare practitioners to engage in thorough thinking and making better judgments. It also provides patients with better access to high-quality healthcare at convenience. Prof. Eric Topol ended the talk with a profound message: human needs are at the core of healthcare, so healthcare practitioners’ empathy for the patients plays a unique role that is irreplaceable by AI. 

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Dr. Michael Snyder on FoundMyFitness podcast with Dr. Rhonda Patrick

Podcast: Dr. Michael Snyder on Continuous Glucose Monitoring and Deep Profiling for Personalized Medicine

“We all have habits that have unhealthy things in our routines, and glucose monitoring helps people pay attention to it.” As one of the most extensively monitored scientists, Dr. Michael Snyder exchanges fascinating stories with Dr. Rhonda Patrick about the personalized insights people learned about their health from continuous glucose monitoring. 

For example, the vinaigrette dressing in a perfectly healthy salmon salad may be the cause for a person’s glucose level to spike. Exercises, even a walk for as short as 15 minutes, could suppress a person’s glucose level after consuming a specific type of food that induces the glucose level spike. Continuous glucose monitoring allows people to gain personalized insights about how lifestyle impacts their health.

These personalized insights are not just anecdotal. Dr. Snyder is a pioneer of a biomedicine research approach, called longitudinal multi-omic baseline profiling. By monitoring a person’s molecular changes across a variety of omic domains over time, Dr. Snyder’s research team is able to gain a holistic and granular picture of the person’s health profile. What’s surprising is that each person’s health profile is vastly different. In fact, one person’s health profile is more similar to themselves when they are sick than to another person. 

This finding can change how diseases s are diagnosed. In conventional medicine, we are told we are sick when our health profile diverts from the population average of what is considered healthy. Dr. Snyder’s research shows, due to the wide individual differences, it makes more sense to compare a person’s current health profile to their own baseline healthy state than to the average healthy state of a population. In a longitudinal study with 109 study participants, Dr. Snyder’s research team is able to detect diseases, such as cancer, pre-cancer, and diabetes, before study participants receive diagnosis. This kind of health insight, if gained early enough, would empower people to change their lifestyles to shift the disease trajectory. The same research team is currently recruiting participants to understand the long-term health effect of dietary fiber supplementation.

Dr. Snyder asked during the podcast, “Guess what the puzzle looks like when you only have 5 pieces of the puzzle?” This could be what our health looks like through the lens of conventional medicine - limited health metrics are taken into consideration a few times a year. We will understand so much more about our health, and how it is changing as we make more diverse measurements over time. Dr. Snyder’s team is currently recruiting study participants for a series of studies, ranging from individual’s different responses to COVID-19 vaccines to environmental exposures’ impact on Crohn’s disease.

Other related podcasts and webinars:


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Virtual summer camp image and link to sign up

Community event: virtual summer camp

We are approaching the launch date of our virtual summer program: Personalized Medicine, Big Data, & A.I. Explorers Workshop.

  • Time: July 19 - July 30, 2021 @ 9:00 am - 11:00 am PT, Monday-Friday
  • Price: $1,500 per student. Scholarships are available.
  • Prerequisite: No background experience is necessary, just a willingness to join the healthcare innovation revolution.

What to expect:

This virtual, two-week summer intensive workshop transports students to the forefront of healthcare innovation research. Students will explore cutting-edge precision medicine topics guided by Stanford's leading medical innovators. It will provide students with the opportunity to utilize Stanford's revolutionary Platform as a Service (PaaS) for Deep Medicine.

Apply for a scholarship:

We believe academic institutions like Stanford should take a leading role in making education accessible and affordable. We will be giving scholarships to bright, motivated students who are under-resourced. If you are interested in applying for a scholarship, please email us at


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Research to the People on Clubhouse header image and link to sign up

Discussion: Research to the People on Clubhouse

Research to the People runs genomic data-driven hackathons for patients with rare diseases — to solve diagnostic mysteries, find out-of-the-box treatment options, and advance research all at once.

Join Research to the People on Clubhouse on July 15th at 14:00 PDT for Gene Fixers with Ethan Perlstein and Julia Vitarello!

In this episode, you will get to hear from Dr. Michael Synder, Chair of Stanford Genetics and Personalized Medicine, talk about how we're building innovative models to tackle rare and challenging diseases. You will also have the opportunity to meet some of the Research to the People's past and current patients.

Gene Fixers is a group of patients, parents, scientists and entrepreneurs on a mission to end the long tail of genetic disease.

Clubhouse is an audio-based social media app. The company describes itself as "a new type of social product based on voice [that] allows people everywhere to talk, tell stories, develop ideas, deepen friendships, and meet interesting new people around the world."

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XR Healthcare Speaker Series banner image that introduces 3 speakers' name, professional role, and workplace, and link to Youtube channel

Community Event: XR Healthcare Speaker Series

We wrapped up our first community event speaker series’ focusing on Extended Reality (XR)’s use cases in healthcare. If you would like to speak at our future eventtell us more about yourself via our old-fashioned Google form. If you are interested in volunteering for our events, please kindly leave us your contact, and we will reach out!

You could revisit the recordings on our Youtube channel. Here is the quick access to each talk:

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On the news & media

There are more we would like to share with you. We compiled a list of articles that featured our work on the news and media during this month for you:


Please contact for press inquiries.
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Thank you for reading!

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We will see you next time!

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