Tag Archives: science

What We Are Reading

We have a great list for you today. Special thanks to all those who are reaching out via Twitter to send us articles, links, and other bits of interestingness. Keep ‘em coming!

Self-Experimentation: Crossing the Borders Between Science, Art, and Philosophy, 1840–1920 by Katrin Solhdju. This brief essay lays out a great foundation for anyone interesting in the history and philosophy of science, with an obvious focus on the self-experiment. This essay is hosted at the Max Plank Institute for the History of Science, at which I highly recommend spending some time clicking around and reading the wonderful essays and articles.

After the Data Confessional: interview with Ellie Harrison by Stephen Fortune. A very interesting and thought-provoking interview with artist Ellie Harrison. For six years self-tracking data was the core component of Ellie’s work as an artist. Then she decided to stop and reconsider her tracking practices and what it meant to her and her work.

Data is the New “___” by Sara M. Watson. “What do we talk about when we talk about data?” is the question Sara posses here to frame a wonderful piece on how our use of metaphors influences our view of data.

A brief history of big data everyone should read by Bernard Marr. If we’re going to talk about how we talk about data it is probably useful to have some historical context. Great timeline here of data in society.

Baby Lucent: Pitfalls of Applying Quantified Self to Baby Products [PDF] by Kevin Gaunt, Júlia Nacsa, and Marcel Penz. An interesting article here from three Swedish design students that looks at current baby and parenting tracking technology. They also conducted a design process to develop a future tracking concept to better understand parent’s reactions to baby tracking. I thought there were a few interesting finding from their interviews.

Hey, Nate: There Is No ‘Rich Data’ In Women’s Sports by Allison McCann. It only seems fitting that a few days before this weekend’s MIT Sloan Conference on Sports Analytics Conference, the “it” place to learn about and discuss sports data, that we learn about the amazing dearth of data collected and published about women’s sports.

Analyzing Email Data by Austin G. Waters. A great deep dive into the 23,965 emails that Austin has collected in his personal account since 2009. I won’t spoil it, but this post just keeps getting better and better as you scroll. Bonus points to Austin for describing his methods and open-sourcing the code he used to conduct this analysis.

The App That Tricked My Family Into Exercising by Adam Weitz. Not a lot of data in this post, but I enjoyed the personal and social changes Adam described through his use the Human activity tracking app.


Smart Art by Natasha Dzurny. Using IFTTT and a few littleBits modules Natasha created a piece of artwork that reflects how often she goes to the gym. Would love to seem more DIY data reflections like this!

How does weather affect U.S. sleep patterns? by Sleep Cycle. Sleep Cycle analyzed 142,272 sleep reports from their users (recorded in January of 2015) to explore mood upon awakening, stress levels before bed, and sleep quality. Fascinating stuff.

Access Links
HHS Expands Its Approach to Making Research Results Freely Available For the Public
Many Patients Would Like To Hide Some Of Their Medical Histories From Their Doctors
Doctors say data fees are blocking health reform

From the Forum
Best ECG/EKG Tool for Exercise
BodyMedia API – Anyone have an active key/application?
Sleep monitor recommendations for research on sleep in hospitals
Simplified nutrition, alertness, mood tracking

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Introducing iMeRG: The Individual Metabolic Research Group

Here at QS Labs we take great pride in supporting a worldwide network of meetup groups. From Bucharest to the Bay Area, we have over 100 groups meeting to discuss self-tracking, share experiences, and learn from each other.

We wanted to highlight a new group, based in southern Oregon, that is using self-tracking to expand and influence medical knowledge within the healthcare system. Dr. Dawn Lemanne, a board certified and practicing oncologist, has started the new Individual Metabolic Research Group (iMeRG) to develop, test, and explore inexpensive way to prevent and treat chronic diseases related to lifestyle, through rigorous N of 1 research methods.


Currently the iMERG is a composed of physicians and other health care professionals frustrated by the rising rates of lifestyle driven chronic disease, and the failure of the large randomized controlled trial (RCT) to provide effective interventions. Inspired by QS, they are working together to develop and use rigorous N of 1 research designs, while using themselves (not their patients) as subjects. Members propose projects, and together they figure out how to do it. QS devices and philosophies play a major role in the data collection and analysis methods being talked about at the group.  Current proposals have included:

  • How best to measure the effect of combining intermittent fasting and exercise on blood ketone levels and inflammatory markers in a sedentary postmenopausal woman
  • The clinical manifestations of Familial Mediterranean Fever gene heterozygosity.

Join the group! If you hold a license to practice a health profession (MD, DO, DDS, DMD, RN, NP, PA, DC, ND, LAc, etc.), you’re interested in N of 1 research design and methods, and you’d like to be involved, please contact Dawn. All individuals are welcome, regardless of geographic location. If you’re in the southern Oregon area you can join their meetup group on February 28th. We’ll be posting updates from the group as their research progresses.

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The Quantified Self Institute

We are excited to be bringing a scientific and research track to the upcoming 2014 QS Europe Conference. We’ve been pushed and prodded by many of our friends in the QS community to make this happen. Today we’re highlighting one of those friends and collaborators, the Quantified Self Institute. Read below to learn more about their work and then register for the conference to join the conversation in person!

QSI logo

In 2012 the Hanze University of Applied Science founded the Quantified Self Institute (QSI) in collaboration with Quantified Self Labs. The mission of QSI is to encourage a healthy lifestyle through technology, science, and fun. We aim to bring the knowledge and experience of the QS community and the science community together in order to learn from each other.

We are a multidisciplinary group of researchers and teachers who work together with a network of universities, health care institutions and industry partners on personalized science, health and self-tracking.

We focus on the Big Five for Healthy Life (physical activity, food, sleep, stress & relaxation and social interaction) and conduct research on the availability, validity, and efficacy of self-tracking technologies.

Our ultimate goal is to find out by what means and to what extend self-tracking is useful for personal health. We look forward to exploring and along with worldwide QS Community. We hope you join us at the upcoming 2014 QS Europe Conference!

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Submit Your Quantified Self Research

We’ve been holding Quantified Self Conferences since 2011. Every year since then we’ve been approached by scientists and researchers in the academic community to help them find a way to incorporate their work and their ideas into our structure. After a few years of holding back, listening, and watching the research community become engaged with other scientists and the real-world QS practitioners we’re ready to take that next step.

We are excited to announce today that we are inviting scientists and non-scientists to join a research oriented poster session at our upcoming Quantified Self European Conference on May 10th and 11th.

These sessions are a way for us to support interesting work that doesn’t fit into our established show&tell format, including research results from academic and scientific studies relevant to QS practitioners. Possible topics include (but are not limited to):

  • Validity, reliability, usability, and effectiveness of self-tracking devices
  • Experiment design
  • Statistical and/or visualization methods
  • Social and psychological investigation into self-tracking practices
  • Social science research on the QS community

Our hope is that these posters and the conversations around them will help us (scientists and non-scientists) learn from each other, stimulate new ideas/projects, and to uncover new applications for the research findings.

How to submit a poster

The process is very simple. Simply send us a draft of your poster submission via email. We will be accepting submissions until April 14, 2014.  For format and other info, please read the instructions below. The posters will be reviewed for content and relevance; if you would like to be involved with the review process, or have any questions, please contact us.


Posters should contain the following elements:

  • Title
  • Authors and affiliations
  • Sections:
    • Background
    • Method
    • Results
    • Discussion/Conclusions
    • QS Relevance
  • Contact information. We recommend including a picture of yourself so others at the conference can find you, and, if applicable, your twitter account.


  • You must use the A0 size (841 × 1189mm or 33.11 × 46.81 inches)
  • A PowerPoint template is provided for you to use.

Remember to Keep It Short and Simple (KISS). We want to stimulate creativity and strongly recommend the use of tables, figures, and visualizations. For examples and design tips we recommend the following articles:

Dates & Deadlines

Deadline for submission is April 14, 2014. We will conducting reviews and informing submitters of acceptance on a continual basis. All submitters will be notified by April 21, 2014. We look forward to seeing your inspiriting projects and findings.

Submit your poster now!

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Ian Eslick on Self-Tracking, Self-Experimentation, and Self-Science

“Personal experimentation is simply tracking, on a schedule.”

Ian Eslick is a scientist, researcher, and self-tracker. His unique history has led him down a path towards understanding what it means to understand yourself and your health in and outside the world of healthcare. Ian’s health history helped push him down this path. Since being diagnosed with psoriasis he’s been confronted with the difficult task of figuring out triggers, effects, and treatments as his symptoms changed over time. Ian, began to explore self-tracking by mentally noting what was going on in his life and his symptom severity. You would think that this “in my own head” tracking methodology would limit analytical capabilities, but it helped Ian create mental models that informed more consistent and rigorous tracking methods, as well as influenced his future research.

In this talk below Ian describes that research, both personal and community-based, that explored the concept of helping people learn how to create and engage with personal experimentation.

“What I came to in conclusion after all of this is that N of 1 is overkill for QS. It’s unnecessary level of rigor. Ninety-five percent confidence intervals are about scientific causal proof, but what I want to know is am I making a better decision. Is data improving my decision in some measurable way? Not is it a perfect decision or do I have proof. So we want to value personal significance over statistical significance. Statistical significance says that if I run this trial twenty more times I’m likely to get the same result, but what I want to know is should I keep doing this and in QS we’re never going to stop keep experimenting, in a way, because our life keeps going.”

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Donate Your Spit for Science

Scanadu, a valued annual sponsor of the Quantified Self, invites you to donate your spit for science! Check out the announcement below to learn more.

Do you have a cough, fever, sore throat, achy muscles, and/or a runny nose?

If you do, you can help us better understand the biology of upper respiratory infections and/or the flu. Donating your spit may, down the line, help reduce unnecessary antibiotic use, help limit the spread of respiratory pathogens and contribute to the design of a new product.

Benefits of participating:

  • An opportunity to participate in science and help Scanadu
  • A $10 Amazon gift card
  •  Upon request, we will be happy to share your experimental results. It is understood that this is not an approved diagnostic test and results should not be used for medical diagnosis.

Who can participate?

  • Children 6+ years and adults (parent/guardian consent required for children under the age of 18)
  • Currently experiencing a common cold, sore throat or influenza
  • Currently living in the United States

How do I participate?

Click the following link for more information and to sign-up: http://bit.ly/1dQNk8n

Need more information? 

For questions regarding participation in this study and the collection of saliva, please contact scanaflu@scanadu.com or info@scanadu.com

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Daniel Gartenberg: The Role of QS in Scientific Discovery

Today’s breakout session preview for the upcoming QS conference comes from Daniel Gartenberg, organizer of the Washington DC QS meetup group. Here is Daniel describing his session “Is QS Science? The Role of QS in Scientific Discovery:”


Do you believe in the power of using Quantified Self to solve some of science’s toughest questions, but have concerns about the validity of QS data?  There is actually a long scientific tradition of N=1 studies (i.e. studies conducted with a single participant).  Additionally, there are various advantages of N=1 studies, such as repeated, longitudinal, and naturalistic data.  These advantages of N=1 studies enable the personalization of treatments because they can take into account individual differences.

Yet N=1 studies are atypical in current scientific research.  We will be discussing why the scientific community frowns on N=1 studies, and how we can alleviate some of the scientific community’s concerns regarding QS.  This involves understanding what makes something ‘Science.’ Additionally, this will involve identifying threats to validity when conducting studies and QS research.  Threats to validity include, but are not limited to: Mortality, History, Maturation, Treatment Fidelity, Treatment Interaction, Compensatory Rivalry, Regression Towards the Mean, and Reactivity.

If done correctly, QS can be a new standard in scientific rigor, but this will require a concerted and collaborative effort by the QS community that will involve developing a system where QSers can post their data to the cloud and have the data aggregated and analyzed across individuals (e.g. curetogether).  The potentials and challenges for creating a QS database will be discussed.

Come to this breakout session if you are trying to make sense of your QS data, are interested in the scientific method, are interested in data analysis techniques, or want to create systems and tools that make QS data meaningful to the general population.

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Gustavo Glusman on Introducing QS into the Scientific Community

Gustavo Glusman is a member of Leroy Hood’s group at the Institute for Systems Biology. At a recent Hood group retreat, the main topic of conversation was Quantified Self! In the video below, Gustavo gives a fascinating recap of the retreat, including how the researchers talked about QS, what experiments they did on themselves, and the main challenges they see with QS from a scientific perspective. (Filmed by the Seattle QS Show&Tell meetup group.)

Gustavo Glusman: Introducing QS in the scientific community (Seattle) from David Reeves on Vimeo.

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QS 101: The Science of Self Experimentation

This special guest post in our ongoing QS 101 series comes to us from Dan Gartenberg, our great QS-Washington DC meetup organizer, and his fellow graduate students in the Human Factors and Applied Cognition program at George Mason University.

Turning Scientific Concerns into Strengths for Quantified Self Experimentation

By Dan Gartenberg, Ewart de Visser, and Jonathan Strohl

Quantified Self and Science are not oil and water. They are intertwined with one another and have a long history together. Though some scientists may not hold QS in high regard, and have the following claims:

“these studies lack validity!”

“a study of a single individual will not generalize to the broader population”

“the possibility of experimenter bias makes your findings highly suspicious and inconclusive.”

“is your effect even real?”

While these are legitimate concerns, QS is science.  And if we keep in mind the scientific method when conducting QS research, this strengthens the validity of our QS projects.

History speaks for itself.  QS studies are actually in line with an age-old scientific tradition: The n=1 study. Back in the day, scientists did not have large labs and took to experimenting on themselves. For example, Hermann Ebbinghaus, one of the first cognitive psychologists, conducted experiments on himself to reveal the process of learning and forgetting.  As a scientist he used a level of rigor that was expected of scientists at the time, and more importantly, Eddinghaus contemplated reasonable mechanisms that explained his results.  Science gives us the tools to make precise measurements, and QS, with its emphasis on improvement of the self, provides a social framework for people to discuss novel phenomena.  In this article we demonstrate how science lends itself to QS and how the scientific method provides us with useful tools for self-discovery.  We first recommend a framework for conducting QS experiments and then discuss the scientific methods to keep in mind.

1)  Achieve your goal:  Unlike most experimental research, in QS our main objective is more often than not self improvement.  A frequently used approach to improve yourself is by throwing the kitchen sink at the problem until you get the sought after effect.  When we make this process social, we can then discuss with others what they are doing and how they think they are being affected by what they are doing.  Based on this information, we get a better understanding of how to most effectively modify our behaviors for the desired outcome.

2)  Use a simple design.  When presenting your data the scientific establishment might criticize your conclusions because it was not the gold standard “double blind randomized control trial.”  But running the right type of design isn’t the be-all-end-all of good science.  If you see a difference and have a reasonable mechanism that explains the difference, with no viable alternative explanations – you are solid.

3) Stats don’t matter as much.  Just graph it! One of the biggest sources of confusion is how to analyze QS data and knowing the right stats to run.  But statistics are only really useful when predicting small effects or for more complex prediction models.  In QS, any change is usually meaningful.  For example, if you are tracking your mood and you see a small improvement it is likely meaningful to you.  So don’t worry too much about statistics.

To discuss the roles of QS and science, we’ll use a dataset that we generated as a case study.  After reading the 4 Hour Body by Tim Ferris, three friends all had the same goal of losing weight.  None of us were extremely overweight at the time, but we could all stand to lose about 10-pounds.  This inspired us to create the 10-Pound Challenge, where we competed to lose weight and either did a slow carb diet or a low carb diet.  We then weighed ourselves every morning.

Quantified Friends: The 10-Pound Challenge:

Here are some issues and concepts that you should consider when making sense of your findings and how Science and QS can benefit one another:

Threat to validity Definition 10-pound application
Mortality  When your manipulation affects the likelihood of whether or not you respond to the measure of interest (i.e. you don’t respond to a survey out of embarrassment). There are almost no skipped days over the course of our study.  This demonstrates how QS can be used as a way to address the problem of mortality by making data collection more social.  This makes people more accountable and motivated to input their data.
History When external events from the environment impact the variables of interest. The diet was made social when we shared our progress with one another. This socialization, where we competed to lose weight, may explain our progress.  We knew that this resulted in alternative explanations, but it didn’t matter because we simply wanted to lose weight and were pulling out all the stops to help us reach our goal.
Maturation When over the course of a study you have changed in other ways that have confounded the impact of the variables in your experiment. In QS our goal is to actually change and mature.  In our QS project the intention was to lose weight and the mechanism was only as important as it was necessary to understand and use in order to promote our increased weight loss.
Treatment Fidelity  In science we usually compare a treatment group to a control group, but what if the treatment is not much different from the control? (i.e. there is not a good counterfactual). We administered relative treatments based on each of our unique situations (this is a common issue with QS).  For example, one of us already had a relatively strict diet.  He was able to make precise modifications to his diet in order to promote weight loss, whereas; the other two QSers made broader changes. This prevented us from making precise claims about how the diet affected weight loss.  Though we still got the general idea that the diet worked.
Treatment Interaction  When the variable that you manipulate interacts with other variables that explain the outcome. When undertaking the 10-pound challenge we frequently told people about the challenge.  This in turn made us more accountable for what we consumed due to social pressure.  In this example, the response to the treatment interacted with the social environment in a way that made us consume healthier meals and lose weight.  Since in QS we are not as fixated on control, we can see how these interactions unfold in the environment and discuss them with others in order to confirm or deny our intuitions.  This provides us with ways to explore new ideas and mechanisms.
Compensatory Rivalry  When the control group is aware that they are not getting the treatment and in turn seeks out other alternatives. In the case of the 10-pound challenge, there was no control group.  Control groups do not play a large role in QS because of the focus on self-improvement.  This is an issue that can be addressed by the scientific method of an A-B-A design where the QSer acts as their own control group.
Regression Towards the Mean  This intuitive premise from science is the basic idea that at the extremes of a behavior you are increasingly likely to gravitate towards the mean. QSers should be particularly sensitive to this because people frequently try to improve on a behavior when they are at an all time low or an all time high.
Reactivity  When your response is affected by external factors, for example, social desirability. In QS, reactivity can actually be used to improve upon outcomes. In our example, there was a social desirability to discuss what was and was not working.  At one point the team members independently agreed that eating too many slow-carbs (legumes) was hindering their progress.  We then made the appropriate changes to our behaviors and found increased improvements.

And these are just some of the threats to validity that QSers can consider to improve their projects. So look out for more to come!

We would like to thank Dr. Patrick McKnight and the MRES group (http://mres.gmu.edu) for providing helpful insights on our QS project.



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