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QS Access App

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Access your data from HealthKit in a table so you can explore it using Numbers, Excel, R, or any other CSV compatible tool. Download it here.

QS Access is designed to give you a simply-formatted table so that you can make health, fitness, and other discoveries by exploring your own data. When you open QS Access you will see a list of all the types of data potentially collected by HealthKit. You can choose as many items as you like from this list.

IMG_9857Select “Create Table” from the bottom right corner of your screen.

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The first time you select any data type for review using QS Access, you will be asked by iOS to give permission for QS Access to read your data. After you grant permission, QS Access will produce a table:

QSaccessTableGWYou can view the table directly on your iOS device, print it, send it to yourself via email or text message.

Hourly/Daily

Each time you create a table, you can chose to work with either hourly or daily values. QS Access does some processing to HealthKit data so that it can be accessed in convenient “chunks” for analysis. These won’t be appropriate for all uses, but many interesting questions can be asked of data that is presented as a time series using hourly and/or daily values.

QS Labs

The mission of QS Labs is to inspire meaningful discoveries about ourselves and our communities, grounded in accurate observation, and enlivened by a spirit of friendship.

Since 2008, we’ve been organizing “QS Show&Tell” events where people share their self-tracking projects, working with a loosely affiliated network of over 100 independent Quantified Self groups around the world, and maintaing a Quantified Self website and QS forum.

If you use QS Access to learn something about yourself, we hope you’ll consider sharing your discoveries by coming to a QS event. Some ways to participate include:

Join a QS Meetup (scroll down the linked page to find your city)
Start a QS Meetup
Come to the QS15 Global Conference and Exposition

Examples

When your data is in tabular format, there are lots of things you can try. Some are simple, and some are quite complex. For inspiration, here are a few examples from talks given at Quantified Self events of self-tracking projects featuring the analysis of tabular data using common tools.

Kouris Kalligas: Analyzing My Weight and Sleep

“You’ll find it surprising that to decrease my weight I had to slightly increase my calories during the day.”

http://quantifiedself.com/2014/09/kouris-kalligas-analyzing-weight-sleep/

Adrienne Andrew Slaughter on Tracking Carbs and Exercise

“I recently experienced extreme fatigue while riding my bike in downtown Seattle. A few days before I had changed my diet, eating all vegetables, lean chicken, seafood, and nuts, on the guidance of my doctor. Three days into this I was feeling terrible.”

http://quantifiedself.com/2014/06/adrienne-andrew-slaughter-going-carbless-seattle/

Paul La Fontaine: ‘We Never Fight On Wednesdays’

“In 1991 I was a soldier in combat, and I was very alert but felt quite calm. In 2006 I was in Istanbul in a negotiation; one comment was made and I felt physical panic. So I was interested in how I reacted to situations because there didn’t seem to be any logic to it at all. So I devised a study to see how I reacted… What I found was the the vast amount of my stress came from me anticipating a disaster scenario in my future, which was not at all what I expected.”

Feedback and Support

You can download the QS Access App on iTunes.

Please share your feedback with us by emailing qsaccess@quantifiedself.com.

We’ve created a QS Access Questions thread on the QS Forum. We’ll keep an eye out for your questions there.

Leave a comment

What We Are Reading

It’s a new year, and we are starting it off with a collection of articles that we’ve been collecting for the last couple of months. I hope you find them as interesting as we did. -Steven

Articles

Making Statistics Matter: Using Self-data to Improve Statistics Learning by Jeffrey L. Thayne. Can Quantified Self projects solve an ongoing problem in teaching statistics? This doctoral dissertation supervised by Victor Lee, a long time participant in our Quantified Self Public Health symposia, argues that it can. The reason QS can help is simple: in QS practices, statistics become personally relevant. As Thayne writes:

[A]n essential feature of effective statistics instruction [is] a relevant, immediately available context of application, wherein learners feel that they are taking part in an ongoing inquiry process in which statistics is being used as a tool for illuminating something new and important about their world.

What I found especially interesting about this research, which used qualitative methods to explore student’s interest and involvement in their statistics learning, was that the use of self-collected data was not powerful because it appealed to the student’s vanity, but because it was familiar and had contextual meaning. Just as professionals who use statistical methods benefit from understanding where the data comes from and what it is for, students who can situate their practice in a rich context find it easier to master new methods. -Gary

On Progress and Historical Change by Ada Palmer. Historian and science fiction author Ada Palmer’s lucid essay on the idea of historical progress is great to read in light of the never-dying hope among the makers of self-tracking tools that there can be a formula for positive change. I sometimes tire out my colleagues opposing this idea, and I know it seems odd that here at Quantified Self we spend every day supporting people trying to figure out how to use technology for change while at the same time not believing that definite techniques for inducing such change can exist. Isn’t that a contradiction? In contrast to my usual philosophical abstractions and pedantic references to the history of behavioral psychology, Palmer tells the story of where our idea of progress comes from, and offers a fascinating account of how events can be simultaneously free and determined, based on the DIY historical simulation machine she builds every year with her students. -Gary

How a Guy From A Montana Trailer Park Overturned 150 Years of Biology by Ed Yong. One of my favorite stories in citizen science is how Beatrix Potter (of Peter Rabbit fame) was an early and ridiculed proponent of the idea that lichen was a symbiotic fusion of a fungi and an algae. The need for the term “symbiosis” arose from this discovery (credited to Swiss botanist Simon Schwendener). This article follows the humble beginnings of Toby Spribille and the process for how he determined that the theory that lichen is composed of two organisms is wrong. It’s actually three. -Steven

How To Do What You Want: Akrasia and Self-Binding by Daniel Reeves. I’ve been going back and reading some of Daniel Reeves’ excellent posts on the Beeminder Blog about the cluster of concepts and techniques associated with self-control, including also Smoking Sticks and Carrots and What is Willpower? -Gary

How Language Helps Erase the Tragedy of Millions of Road Deaths by Julie Sedivy. What is the difference between the words “accident” and “collision”? The word “accident” implies a lack of blame. This article explores the effects that these connotations have on our subconscious interpretation of the world. -Steven

Faster, Not Smarter: Does Caffeine Really Make You More Productive? by Alex Senemar. Alex surveys what is known about the effects of caffeine on productivity. How do you keep caffeine a boon and not a crutch. What I love about this article is that Alex finishes it with suggestions on how to run your own experiment to see caffeine’s effect on your productivity. -Steven

In Defense of Tracking Our Poop by Adam Butler. Adam makes the argument that one of the best ways to understand the health of the microbiome is to track and pay attention to your poop. How do you turn that into data? Luckily, there is a time-tested classification system that your physician should recognize called the Bristol Stool Scale. Which will help the next time you need to talk to your doctor. -Steven (courtesy of Ernesto Ramirez)

Childhood trauma leads to lifelong chronic illness — so why isn’t the medical community helping patients? by Donna Jackson Nakazawa. “Were there any childhood traumas or stressors that might have contributed to the extreme level of inflammation you’re experiencing as an adult?” Nakazawa says that this was the most important question posed to her in her adult life. From the question, she was able to untangle how her present day health issues have ties to the traumatic death of her father when she was twelve. The article shows that childhood trauma leads to a great likelihood of autoimmune disease. However, knowing about these links, one can help reduce the number of doctor visits. -Steven

Early Modern Bookkeeping and Life-Writing Revisited: Accounting for Richard Stonley, by Jason Scott-Warren. The use of numbers as an element in personal record keeping is ancient, but the account books of early modern elites hold particular interest for historians, since they seem to hold clues to the origin of today’s autobiographical habits. The great 17th century diarist Samuel Pepys kept his entries in a ledger book, and the carefully folded pages and ruled lines of the account books of bourgeois merchants and lawyers provide a dense cultural background for more famous documents (such as Benjamin Franklin’s memoirs) commonly imagined to to lie at the root of the Quantified Self. This essay from The Social History of the Archive (a special volume of the journal Past & Present) takes a close look at the account book of an obscure functionary named Richard Stonley, and shows how mistakes, repetitions, and elisions challenge the idea of the ledger book as a crucible for the modern sense of self. -Gary

Show&Tell

How Software, Data, and a Hell of a Lot of Work Helped Me Lose 110 pounds in 25 Months by Timothy Chambers. Although he doesn’t show his data, it was interesting to read how Timothy integrated various tools into his effort to lose weight. Each tool had a role and each needed certain features to qualify. It’s a complex interaction of data sets and feedback mechanisms. I appreciated one of his points on data portability:

It was critical that my apps could speak to each other and to the cloud, not just to what companies each toolmaker had deals with. My web-based trend tools needed to talk with my scale which needed to speak to my phone. We work so hard for the health data about steps, weight, fat percentage, etc, that should be our data open to use with whatever tools we wish. Not all vendors treat it as such.

-Steven

My to-do list is now public, and it’s the most useful thing I’ve done in years by Joe Reddington. For years, Joe has kept track his number of open to-do’s. In May, he experimented with making his to-do list publicly available. Now that he knows that he’s being “watched”, he is more conscientious about making his items comprehensible, and is  more motivated. As Joe puts it: “When it was [just] a list for me, it looked great; when I decided to make it public, it instantly looked very poor.” -Steven

Analysis of a Personal Public Talk by Alex Martinelli. Alex analyzes a recent talk he gave at a QS Dublin meetup, by looking at his heart rate and speaking speed. The piece has an appropriately casual tone, but he finishes each section of the analysis with a definitive statement based on the data. After looking at how fast he was talking, Alex writes as if he was consulting someone else:

Your average speech rate is 152 Words Per Minute (WPM), but an approximately constant and significant decrease can be observed, bringing you from an initial WPM of 166 to a final value of 142. The primary cause of this is the usage of increasingly longer pauses between words, secondarily reinforced by a combination of using longer words, as well as a tendency to slow down the pronunciation of words, while the talk unfolds.”

As an engineer at IBM, he’s clearly used to this at his job, but I like the idea of bringing this structure and formality to personal data analysis. -Steven

The Somniloquist by Adam Rosenberg. Adam was told by others that he talks in his sleep, so he set up a recorder to capture his “midnight monologues”. The recordings are transcribed, and in addition to being hilarious, they are an interesting insight into what the brain is doing during sleep. -Steven

My Quantified Wardrobe 2017 by Matt Manhattan. Matt analyzes his wardrobe in an effort to define his relationship with his clothes. He looks at how much of each article of clothing he has and their associated cost. But it’s the pictures of his clothes that makes this post delightful. -Steven

Data Visualizations

History Lesson by Clive Thompson. Not a visualization, but an article about the history of data visualizations. -Steven

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The London Wind Map. A whimsical of visualization of where you would go if “you were pushed by the wind each day” in 2015. -Steven

 

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Randy Sargent: Unlocking Patterns with Spectrograms

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In this talk, Randy Sargent shows how he used a spectrogram, a tool mostly used for audio, to better understand his own biometric data. A spectrogram was preferable to a line graph for its ability to visualize a large number of data points. As Randy points out, an eeg sensor can produce 100 million data points per day. It is unusual for a person to wear an eeg  sensor for that long, but Randy used the spectrogram on his heart rate variability data that was captured during a night of sleep. In the video, you’ll see an interesting pattern that he discovered that occurs during his REM sleep.

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Richard Sprague: Microbiome Gut Cleanse

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Richard Sprague has been closely tracking his microbiome and sharing his findings for the last couple of years. He’s even joined our friends at uBiome as their citizen-scientist-in-residence.

In this talk, Richard shares his attempt to improve his sleep quality by increasing the amount of bifidobacterium in his gut through eating potato starch. You’ll learn why he took the extreme step of flushing his digestive tract and rebuilding it from scratch.

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Meetups This Week

I haven’t been doing these for awhile, but that does not mean that Quantified Self meetups have not been occurring. Far from it. If you are in the Washington D.C. area, there is a meetup on Friday, September 9th with presentations on using data to personalize one’s fitness regimen and how to use heart rate and EMG measurements to detect one’s sleep stage.

To see when the next meetup in your area is, check the full list of the over 100 QS meetup groups in the right sidebar or you can search for “Quantified Self” on meetup.com. Don’t see one near you? Why not start your own! If you are a QS Organizer and want some ideas for your next meetup, check out the myriad of meetup formats that other QS organizers are using here.

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Abe Gong: Changing Sleep Habits with Unforgettable Reminders

Abe had an issue with staying up too late. The early morning hours often found him on his couch, working on his laptop.

The problem is that he simply lost track of time. To help make his bedtime unforgettable, Abe built a reminder he could not ignore. He wrote a simple app that uses colors to gently prod him to get ready for bed and installed it on an old android phone that he mounted on the wall in his living room. When the screen first lights up in the evening, the colors are blue (“bedtime is coming.”) and increasingly become red (“bedtime is here.”). When he long-presses the screen, it means that he is ready to sleep, and the phone responds by lighting up with a celebratory array of colors.

It was a simple intervention, but did it work? Abe thought so. But the skepticism of friends spurred him to dig into the data to make sure. The problem was that his simple app didn’t record any data. He had an idea, though. For the past year, a webcam connected to a Raspberry Pi had been recording his living room. Abe used the light levels of the video stream as a proxy for his bedtime. When the light levels dropped, it meant that he had gone to bed. This proved to be a reliable indicator because, as Abe says, “I’m always the last one to sleep, and the last light I turn off is always the living room light.”

Would this work for you? Possibly not, but that’s not the point. It is an excellent example of a person building a solution that is specifically designed for his personality, and also how meaning can be found in the unlikeliest of datasets. In the video, you will find out how much sleep Abe saved and learn more about how he set up his device and ran the analysis.

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What We Are Reading

Articles

The Anecdote is the Antidote for What Ails Modern Medical Science by John R. Adler, Jr. M.D. It’s hard to imagine anybody being more of a medical insider than Dr. John R. Adler, the founding editor of Cureus. Adler has a Harvard medical degree, served his residency at Massachusetts General hospital, and is a Stanford professor of neurosurgery, as well as founding CEO of a leading radiation oncology company, Accuray.  This makes it especially heartening that Dr. Adler is now focused on opening up medical research literature to important kinds of evidence that have often been ignored: the anecdote and the case report.  Quote: “The altruism that is supposed to drive the publication of scientific research has been almost entirely co-opted by the peculiar needs of academic promotion and tenure, as well as the pecuniary demands of the scholarly publishing industry; the public good of medical knowledge has been reduced to a mere after-thought by both academia and the publishing industry.” -Gary

You can train your body into thinking it’s had medicine by Jo Marchant. There is an experimental treatment where the patients always drink from a uniquely flavored beverage every time they take their medicine. After a while, the drug is taken away, but the drink is still consumed. Amazingly, the body continues to act like it received the drug. Considering that many of these drugs have terrible side effects, these findings can impact how medication is administered. -Steven

Fleming’s discovery of penicillin couldn’t get published today. That’s a huge problem by Julia Belluz. John Adler’s reflections on the value of anecdote, linked above, were inspired by this essay by Julia Belluz celebrating the creation of another new journal, called Matters, devoted to publishing reports of small scale experiments and observations. As the publishers write on their web site: “Observations, not stories, are the pillars of good science. Today’s journals however, favor story-telling over observations, and congruency over complexity. As a consequence, there is a pressure to tell only good stories. Moreover, incentives associated with publishing in high-impact journals lead to loss of scientifically and ethically sound observations that do not fit the storyline, and in some unfortunate cases also to fraudulence. The resulting non-communication of data and irreproducibility not only delays scientific progress, but also negatively affects society as a whole.” -Gary

Machine Learning for Easier Dieting by Samuel K. Moore.

“I had a half-cup of oatmeal, with two-tablesoons of maple syrup and a cup of coffee. Oh, I put a handful of blueberries in the oatmeal, and there was milk in the coffee. It was skim milk.”

It would be wonderful to log one’s food by speaking to your tracker in this natural manner. Machine learning may make it possible, but this article reviews some of the obstacles that need to be overcome when it comes to parsing speech. -Steven

A Case of Complete and Durable Molecular Remission of Chronic Lymphocytic Leukemia Following Treatment with Epigallocatechin-3-gallate, an Extract of Green Tea by Dawn Lemanne, Keith I. Block, Bruce R. Kressel, Vikas P. Sukhatme, Jeffrey D. White. This paper from Cureus about a notable clinical outcome offers an excellent example of the kind of research that might never come to light without the work of pioneering science publishers. -Gary

This Dude’s Fitness Tracker May Have Just Saved His Life by George Dvorsky. When a 42-year-old man recently went to the emergency room following a seizure, the doctors had to make a decision in how to proceed that depended on whether the man’s arrhythmia was caused by the seizure or was chronic. The answer was found in the man’s Fitbit data. -Steven

Show&Tell

How Can I Stop Feeling Cold by Justin Timmer. A few years ago, I spent a winter wearing only a t-shirt when I went out. I was interested in how my body adapted to the cold over time. I am happy to see Justin attempt a similar thing but with more rigor. -Steven

Letter of Recommendation: Segmented Sleep by Jesse Barron. I’ve been long fascinated by the concept of segmented sleep, where people have a first and second sleep period during the night. I’ve read historical references of this phenomenon, but not many experiences from now. -Steven

Leave that Thermostat Alone! by Michael VanDaniker. Taking advantage of the ability to export his data from his electric provider, Michael compared his electricity usage against the outside temperature to get a better understanding of his electricity use (and created some nice visualizations in the process). -Steven

Resource: Home Hacking Blood Glucose by Jenny Horner. Jenny heard about a study in Israel that showed that post-meal blood sugar spikes differ highly according to the individual. For example, ice cream is fine for some, but sushi is not. Jenny decided to apply the findings to her life. She shares the method she is using to construct her own personal glycemic index. ‑Steven

Personal Information Manager by Fabian Benetou. Fabian’s site is a fascinating mind dump of many aspects of his life. Open to all, but comprehensible only to him, there is a voyeuristic pleasure in traipsing around and seeing a glimpse into someone else’s head (another fun example is Jerry Michalski’s “Brain”). In particular, I love seeing the notes that he keeps from the books he reads, which dovetails with my interest in commonplace books‑Steven

Data Visualizations

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Arbor Ludi“Un proyecto de visualización de datos compuesto por una serie de representaciones gráficas que reconstruyen el árbol de juego de ocho de los mejores ajedrecistas de la historia.” Each image in this beautiful and fascinating series of data visualizations represent the playing life of a chess master. Created by the design and architecture firm Ootro Estudio, the portraits are made out of data from every move from hundreds or thousands of published games (the number varies significantly between masters). The coral-like shape emerges from the fact that in chess the first moves are common and well known, while later moves inhabit a vast possibility space. For more on the technique, see this informative post on The Zugzwang Blog: Arbor Ludi: arquitectura mental de un genio del ajedrez. (In Spanish.) -Gary

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All Those New Dinosaurs May Not Be New — Or Dinosaurs by Maggie Koerth-Baker. My first experience of the messiness of paleontology is when I learned that my mug with a green brontosaurus depicted an animal that never existed (though that may no longer be true). This chart shows how often a dinosaur genus is later declared to be invalid. The error rate for dinosaurs named between 1850 and 1980 is 48 percent! -Steven

 

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Changing river path seen through satellite images by Zoltan Sylvester. Using Landsat images, this is a time lapse that shows, over a thirty year period, the oxbow section of the Ucayali river in Peru get pinched and then cut off from the main flow of the river. You can view thirty year timelapses like this for any location on earth at the Google Earth Engine. The Las Vegas one is an incredible example of urban expansion. -Steven

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Akhsar Kharebov: A Smart Scale for Healthy Weight Loss

Historically, the most prevalent self-tracking tool in the home was the scale and the relationship between people and weight is complicated. Akhsar found healthy weight loss to be an emotionally difficult process. His breakthrough came with the Withings smart scale with which he lost 65 pounds in the first year and has kept it off for the last three. In this talk he discusses how the data helped him gain the self control to overcome temptations.

Weight has been a popular topic for Show&Tell talks:
Julie Price on the effect of running and family events.
Nan Shellabarger on seeing her life story in 26 years of weight data.
Kouris Kalligas on the relationship between his weight and sleep.
Jan Szelagiewicz on being motivated by family history.
Lisa Betts-LaCroix on using spreadsheets, forms and wireless scales changes the tracking experience.
Rob Portil on how he and his partner experience weight tracking differently.
Amelia Greenhall on using a 10-day moving average.

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#WeAreNotWaiting by Dana Lewis

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From the Quantified Self Public Health Symposium

“I didn’t want to wait. I don’t want to die in my sleep. We are patients who are not waiting.”

Dana Lewis became a reluctant self-tracker at the age of 14 when she was diagnosed with type 1 diabetes. Dana and her partner Scott Leibrand have been developing a DIY artificial pancreas that is built on top of the data flows from Dana’s continuous glucose monitor. In this talk, she describes the role that access to data plays in the DIY pancreas, with immediate and profoundly positive effects on her life.

Watch Dana’s talk on Medium.

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A Public Infrastructure For Data Access

Smarr2 (1)Larry Smarr’s major contributions to scientific progress are well known. A physicist and the founding director of the National Center for Supercomputing Applications (NCSA), he helped bring the power of computing to scientific research at a time when computers will still highly specialized instruments. Today he is the Director of the California Institute for Telecommunications and Information Technology (Calit2), one of the most innovative research institutes in the world. He’s also an avid self-tracker, using his own data to correctly self-diagnose the onset of Crohn’s disease. In preparation for our upcoming Quantified Self Public Health Symposium I asked Larry about his idea for a large scale, non-commercial, broadly accessible infrastructure for improving access to self-collected data for both personal and public benefit.

Gary Wolf: What’s the role of the public health sector and of the academic research community in a world where individuals and consumer-oriented tech companies are taking on increasingly complex questions of personal and population health?

Larry Smarr: The fundamental role is bridging the gap between N=1 and N=a lot. Any time in the last 30 years when I’ve seen a technical innovation that mattered, like a software tool, the first approaches aren’t ready for prime time. They are not developed with professional-level software engineering, version controls, documentation and all that. Similarly, scaling up of biomedical observations made by N=1 quantified individuals is going to require the professional methodologies of the public health sector.

GW: Can this be left to industry?

LS: Not entirely, although startups are doing a fabulous job of getting tracking tools into the hands of tens of millions of individuals. The problem is how to do research on the data produced by that broad population. Too often these days I see  researchers from the university going to tracking companies and asking for access to the company’s raw data feeds, for instance to heart rate or exercise time series, and the company says no. They will give you the weekly or daily average, but you can’t get to the raw data. If you go to them and say, I’ve got this really great innovation that can be used to understand this data, more often than not they decline. They have an installed base and market share to protect, which naturally tends to make them conservative. I think there is a real opening for companies to make this anonymized broad population data available to academic researchers. That’s when a raft of scientific discoveries will be made from the quantified population.

GW: Those are the consumer fitness companies, but what about the healthcare IT world?

LS: Again there is a disconnect between the consumer fitness cloud-based apps for millions of individuals and the electronic health records in your healthcare provider. If you’re a doctor in a medical office, unlike a data science researcher, you don’t want all this data. What you want to know is: did my patient do 1000 steps or 10,000 steps today, did you get aerobic exercise or not, are they getting enough sleep? So it’s not like you need a vast dumping place inside electronic health records.  Again, I think pilot experiments are the way to get started.

GW: You’re arguing that the incentives aren’t there.

LS: These are currently major structural barriers. Who is going to work on the bridging we are discussing? There aren’t incentives for the commercial tracking companies to work on it. Neither are there incentives for the electronic medical record companies to work on it. NIH isn’t going to support bridging between commercial companies.  It falls between the stools. You need to have the research community, and health care IT experts, the commercial tracking companies, and the individual self-trackers all come together and collaborate.

GW: You envision some kind of technical system so that individuals and health care providers and researchers could all benefit from access to data. What does your experience tell you about how long this would take to have a working prototype that would be practically useful?

LS: It’s a three-to-five year project. I think if a major funder did a call for proposals requiring a health care provider, university research community and the self-tracking community to come together a prototype a solution, I think they would get some very interesting proposals.

GW: In a talk you gave in 2011, you said “science is not enough.” You pointed out that we’ve known the link between smoking and cancer for over half a century, and yet global cigarette consumption has tripled during this time. So we have all this possibility for new discoveries with self-tracking data, but how is that going to help make people healthier?

LS: Yes, just knowledge of what causes negative impacts on health is not enough. My former UC San Diego colleague Naomi Oreskes documents how economic interests slowed down the logical social reaction to smoking health threats and climate change in her Merchants of Doubt: How a Handful of Scientists Obscured the Truth on Issues from Tobacco Smoke to Global Warming (2010). We are seeing similar delaying and disinformation tactics in the obesity/diabetes epidemic, which has been building for four decades. It is sobering to me to see someone as politically skilled as New York City mayor Bloomberg defeated in his efforts to ban jumbo sugary drinks. My best guess is that we face a multi-decadal battle, just as we have had with tobacco and climate change, to get our society to move to healthy eating and drinking. The bright spots are subcultures of healthy living, often empowered by tracking and social media, that are developing across the country.  My hope is that these will spread and scale over the next decade.

GW: It seems you are also pointing toward activism, since that’s been so important with smoking.

LS: Activism is essential given the enormous power of the entrenched economic interests. Activism can lead to regulatory reform, which over time can make huge social changes.  For example, when I grew up in the 1950s and early 60s my father didn’t smoke, but he was embarrassed that you had to have ashtrays in your house, because he said you couldn’t tell people not to smoke in your own home. Socially, you just couldn’t. About that time the Surgeon General’s report on smoking was published. Fifty years later, huge chunks of society are smoke-free, such as all the University of California campuses, restaurants, and large social gatherings. Just think of what an enormous shift that has been! We are beginning to see similar activism in getting pension fund investors to boycott carbon fuel companies in order to slow down climate change. So can we imagine a boycott against sweetened beverages and high glycemic prepared foods? I believe that there is a huge role for health-related individual and organized activism in the near future.

GW: At the last Quantified Self Public Health meeting, you suggested that this emerging field needs a new kind of journal where individuals can report their discoveries. In light of the big challenges you’ve been describing, challenges that can’t be solved by academic and research publication alone, what kind of contribution could a new journal make?

LS: Let’s go back to the issue of scaling we discussed. Imagine the journal articles are fairly short, describing how the data was generated, but the back end is a publicly available cloud of data so that you could begin growing a large dataset of N=1 projects. Then the research community could pick up on the ideas coming out of the Quantified Self community, explore the data, and take it further. That’s how things grow.

GW: You want to be on the editorial board?

LS: No, I want to submit a paper!

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