Show&Tell talks are personal stories focused on how someone went about a self-tracking project or self-experiment and what they’ve learned. These talks use the ignite+ format (7.5 minutes each).
Breakout sessions are an hour long and are a time to discuss topics that are of interest to the QS community and set the agenda for our movement in the coming year.
How-To’s are a one-hour session where you can come and learn from a knowledgeable peer about some aspect of a Quantified Self practice. We’ll announce the how-to sessions shortly.
Office Hours are one-hour sessions for toolmakers to share their work. (Learn more here.)
Opening PlenarySession 1Session 2LunchSession 3Session 4Afternoon Plenary
Morning PlenarySession 5Session 6LunchSession 7Session 8Closing Plenary
Allen Neuringer, Emeritus Professor of Psychology at Reed College, is a pioneering teacher, scholar, and self-tracker whose work has influenced many of us in the Quantified Self community. His 1981 paper "Self Experimentation: A Call For Change" was a prescient argument for using self-collected data as a tool of discovery. Professor Neuringer will open this year's meeting with lessons he's learned from his own experiments and those of his many students.
Over years of tracking exercise, Valerie Lanard inadvertently compiled an incredible data set by documenting her excuses for not exercising. From this unexpected trove she learned why she tended to get sick, how she's prone to injury, and also the importance of logging a little extra context.
Mad Ball is a carrier for a rare genetic disease that entailed the risk of having a child with a serious intellectual disability. But how much risk? Through careful self-investigation based on consumer genomics, a reasonable estimate turned out to be possible.
Daniel Reeves has made it a strict personal rule that every time he utters a statement starting with "I will" to someone, no matter how casually, he logs the commitment, with a due date, and keeps track of when he follows through.
How much work does it take to get a PhD? How do marathon work sessions affect future productivity? Maggie Delano will answer these questions and more using data from tracking over 5000 pomodoros over the course of earning her PhD.
Azure Grant is interested in circadian and ultradian rhythms. Over a 10-day period she collected EEG, EKG, EGG, glucose levels, and body temperature measurements to explore how these different systems interacted.
Madison Lukaczyk wanted to improve her focus by controlling her distractions. Using time-tracking data, she created visualizations that revealed how frequently her workflow was interrupted by chat threads, emails, and texts.
Justin Lawler tracked his glucose over four months using a continuous glucose monitor. He compiled a total of 21,000 glucose measurements, along with many other biometrics, to gain insight into what affects his metabolism.
Thomas Christiansen's allergies are aggravated when he runs during grass pollen season. For this project he used a GoPro to document passing vegetation and a device to record his sneezes in order to pinpoint which plants activated his nose.
How far can we go using our fat as fuel? Mikey Sklar will be sharing the diet, training, and testing that went into running 76 miles in less than 24 hours without the use of food.
Diabetes is unique because the “cure” could be a widely available medical device instead of a drug. The success of the new artificial pancreas systems relies on knowledgeable users setting the pace. Now major device companies and startups have joined in, so let’s discuss what comes next.
Do we have a right to download "our data" generated in platforms, apps, and devices? Are there reasonable limitations? Does government regulation help?
It's hard to decide whether to take yourself out of the workforce to pursue post-secondary education. Economists think about this decision in terms of lifetime earning potential. What are other ways to quantify the value of an education?
Longtime Quantified Self participant and scholar Dawn Nafus has been deeply involved in community environmental monitoring projects. Come take advantage of her knowledge and experience before starting your own.
Eternalist is the world's first self-writing journal. It works by syncing and displaying data from the apps you already use—like Spotify, Fitbit, Instagram, and Google Calendar—into a private and timeless record of your "beautiful life."
Avenna is a Quantified Self approach to precision medicine. We're developing self-care pathways incorporating the GlyHealth Index (a blood glycomics test) and other precision medicine biomarkers for early stratification and monitoring of chronic inflammatory disease (CID) progression.
We all have blind spots that prevent us from seeing our own strengths and limits, even if we can see those of others. Leap Forward's Quantified We is a platform for earning trust so we can learn from how we're reflected by others and harvest this data for our own growth.
Come learn more about the artificial pancreas I use: how it's made, how it works, and how you may be able to benefit and contribute.
INGH is committed to accelerating new discoveries, technologies, and platforms to transform the definition of "health" and contribute to a new and growing model of healthcare innovation. Come see our newest tool for N-of-1 studies.
Mymee is a platform for data-driven coaching, with a focus on sustainable behavior changes.
RescueTime helps people take control of their digital lives and find balance with their devices both at work and at home. With accurate data about how you are spending your time, you are able to make more informed choices and take action to improve the quality of your time.
Winks is a novel sleep diagnostic that uses a case-based AI to assess sleep and learn from sleep clinicians.
Stephen Maher started with paper and pen, and eventually developed his own app to help him learn about his headache patterns and manage his actions, medications, and expectations.
Do hormones activate creative thinking? Using fertility data from 70+ cycles, Shara Raqs discovered the time in her cycle that she was most likely to experience eureka moments.
For years, Eli Ricker has tracked her self-created "Life Satisfaction" score and whether or not she did what she said she would. She’ll describe what this practice taught her about effective goal setting, true productivity, and deeper satisfaction.
Anna Franziska Michel will describe how she uses her own running and cycling data as material for her startling and beautiful work in fashion design.
Ralph Pethica has been combining fitness tracking and subjective data with genetics, using techniques from his work with professional athletes to help find the optimal way for him to train.
For people who deal with conditions that induce fatigue, what tools are there to help safely manage their exertion and recovery?
Today's DNA swab tests offer lots of information, including guidance on vitamin deficiencies, pharmaceutical drug compatibility, food sensitivities, and health risks. Let's discuss how one should use the information that different reporting services are currently offering.
Our senses pick up a wealth of information about our environment and, consequently, ourselves. But it's often difficult to pick up on the signal. Let's talk about what we can learn by augmenting, or at least paying attention to, our senses.
Machine learning is typically done on large data sets. Zenobase founder and QS Forum co-admin Eric Jain will lead a discussion on applying machine learning techniques to our N-of-1 experiments.
Are you interested in joining a participant-led research project? Our next PLR is focused on continuous body temperature and ovulatory cycling. Come discuss and help shape this project.
There are now a large number of sleep trackers, including trackers from Oura, Whoop, ResMed, Fitbit, and various Apple and Android devices. Come discuss the ins and outs of tracking our sleep.
What are the risks of making our personal data public?
Several attempts have been made to build online platforms for self-tracking and self-experimentation. What's been learned, and what are plausible next steps?
You have the power to develop your own self-tracking devices! Join some of the QS community's most experienced technologists, and get started using open hardware Arduino boards to create your own instrumentation.
What if our self-tracking tools were open from top to bottom: hardware, firmware, software, and data all equally accessible and customizable? We'll discuss current work on the Open Stack and have a freewheeling discussion of what features we'd like to see and who can build them.
After 10 years of collecting data on herself, data visualizer Lillian Karabaic embarked on a project to make a daily art piece from her data for 100 consecutive days, with pieces ranging from "Mildly Scary Things I Have Done" to "Burritos Per Year."
Victor Lee has been running after-school experiments in measuring engagement in organized activities, using a visual lifelog and an electrodermal activity device. But what can students learn that they don't already know?
Teacher and scholar Kyrill Potapov has created a series of innovative Quantified Self projects using books, plants, and other unlikely elements. In this talk he'll explore the particular kind of learning self-tracking can support.
For the last decade Todd Greco has been using a variety of data sources to build up his exobrain, including recording every location he's visited, allowing him to call up each place individually or map them.
Jakob Eg Larsen thought tracking headaches would be an easy task. But the very first question turned out to be less straightforward than it seemed: What counts as a headache?
Jordan Clark used his heart rate variability (HRV) data to measure psychological effects of microaggressions as part of his research quantifying the Black experience.
Alec Rogers wanted to see if there was a way to measure mindfulness after meditation, so he built his own simple, open-source meditation tracker.
Since the day Aaron Yih was born, his grandfather documented his life in large photo collages he hung on the walls. Now that his grandfather is 84, Aaron is using digital archiving and modern lifelogging tools to continue the record that his grandfather began over two decades ago.
Standard approaches to ethical oversight of human subjects research don't easily apply to QS projects, where we collect our own data and ask our own questions. But what standards and procedures do reasonably apply? In this breakout we'll discuss emerging practices and perhaps propose new ones.
When does it make sense to construct a self-experiment? What are the advantages of tracking a metric over a long period of time? Let's talk about practical considerations for doing both well.
New technologies allow non-invasive methods for measuring glucose. Access to this data is obviously useful for managing diabetes and more. What will we do with it?
There's a wealth of data in your breath, but it's been hard to access. Available tools are either expensive or unreliable. This session will demonstrate how to build an inexpensive device for measuring acetone in your breath, which is useful for understanding your body's fat metabolism.
Aaron Parecki will talk about what he's learned from using extensive continuous location data, based on a decade of experience.
Fah Sathirapongsasuti's project, carried out on his way up Mt. Everest, allowed him to carefully evaluate both the drop in his blood oxygenation and the effect of acclimatization—and contained some useful discoveries.
Kyrill Potapov underwent a five-day fast to measure the impact of cell death (apoptosis) on cholesterol and hormone levels, using two InsideTracker panels to show before and after states.
Exercising without food for a person with diabetes is akin to scuba diving without air; medical “experts” say it’s impossible. Jessica Ching was unwilling to believe this and conducted a series of personal trials. She has since run thousands of miles, almost all without eating.
Shamay Agaron has been using a breath measurement instrument, the Spire, to understand more about his patterns of focus.
Women- and non-binary-centered QS meetups in SF, Boston, and NYC have created space for interesting QS conversations over the years. This session is specifically for people who identify as women and/or non-binary people in a way that is significant to them. We'll have an open discussion.
There are devices to help people improve their posture, but how well do these work? Let's talk about our experiences using data to create better posture, and how to improve posture-improving devices.
Marcel van der Kuil is well known in the QS community for his experience and expertise with personal data in running training. He will show how to use morning heart rate variability measurements to guide the intensity of your workouts, while explaining what those metrics represent physiologically.
Psygraph is an open-source app that measures mindfulness practice. It tracks time spent during meditation, breath-counting ability (as a measure of mind-wandering), self-report of mindfulness at random intervals, and user notes.
Yooneeque creates sporty, intelligent fashion using neural networks to convert the personal training data of athletes into individual designs.
Data Sense is a personal data exploration tool for those of us with no interest in learning how to code. It offers flexible ways of finding patterns and an introduction to key data concepts.
I'm sharing my research on how meditation and neurofeedback affect cognitive performance, mood, and productivity. Ask me about what the research found and what the implications are.
Open Humans is an open-source ecosystem and community for exploring and collaborating with personal data. Stop by to learn more, especially about our "Personal Data Notebooks," where people can create shareable analyses of their data that others can use to analyze their own data.
SpineTracker is a wearable and app that shows the shape of your spine in real time with five small sensors placed vertically from the sacrum up the lumbar spine.
Genetrainer is used by professional athletes to combine genetics with fitness data, and it's now available for everyone.
Linaid is an iPhone tracking assistant with user-defined statistics and graphs, and .csv export.
We've made simple, open instrumentation that creates a private, time-stamped record every time it's pressed.
Benjamin Smarr has been collecting glucose, body temperature, heart rate, and stomach activity data to see how his body responds to scheduled meals, and whether it keeps the schedule when he fasts.
Lydia Lutsyshyna tracked the timing and location of her activities, then experimented with clearly separating studying and non-studying intervals to see if this simple delineation produced noticeable effects in her behavior.
Albara Alohali combined self-collected data and storytelling to help himself meet a personal challenge: running a marathon every month.
Ben Best measured his blood glucose, ketones, triglycerides, and cholesterol in response to a wide variety of foods and other activities. He'll show how his analysis changed his dietary choices.
Michael Lim, teacher, and Alex Truong, 12th grader, redesigned the AP Statistics course at Summit Shasta High School to have students learn by analyzing their self-tracking data. They prepared by doing their own QS project with Rescuetime, MyFitnessPal, multivariate regression, and more.
Join Eric Hekler, director for the Center for Wireless and Population Health Systems at UCSD, for a discussion on how to use self-tracking data to update your causal models.
Being human is important. Is it possible that representing individuals through numbers denies us some humanity? In this session, we will discuss whether quantification of experiences makes people less human-like and more machine-like.
Kids find self-tracking valuable too, but what are they interested in tracking? What considerations should be taken with young people's data?
This workshop will show how to use readily available and affordable tools (fitness apps, tests, and devices) to help make your workouts more efficient, including what kind of metrics to track and how to interpret them.
The QS community includes self-trackers and toolmakers who have experimented deeply with tools for achieving goals and making personal changes. Come discuss what's worked, what's failed, and what we've learned so far.
Have you ever said, "I love that app, but I wish it did X," or "That was my favorite self-tracking app, but it's no longer maintained"? Let's talk about design principles for self-tracking tools that make them secure, useful, and usable for a long time.
Some of us microdose and use nootropics. Many of us use supplements. Let's talk about how to evaluate whether the impacts live up to our expectations. How do you test for the efficacy of different doses or protocols?
Inspired by Jordan Clark's talk on his attempt to understand the impact of microaggressions through logging his experiences on Twitter and heart rate variability measurements, we're going to continue the conversation.
Discover the experiences and meta-lessons of a two-year deep dive into teaching one particular self/family-tracking tool (Atlas CareMap) and observing lots of unexpected outcomes. Are these unique or widely applicable?
Location tracking is very common now, but it presents some special challenges: privacy, secure long-term data storage, and integration with other data types. Aaron Parecki, who has been tracking his location continuously for over a decade using open tools, will lead this workshop.
After giving birth, Whitney Erin Boesel learned that her cholesterol was very high. Given her family history, it seemed that an intervention was in order. But what if she did nothing and simply made observations?