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.

iMeRG

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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Do It Yourself Diabetes

DanaScott_Header

Dana Lewis and Scott Leibrand are the creators of the amazing “Do-It-Yourself-Pancreas-System,” also known by #DIYPS. We had a few question for them.

Ernesto: Why build your own pancreas?

IMG_3561Dana: I’ve had Type 1 diabetes for about 12 years. I use an insulin pump and a continuous glucose monitor (CGM), but the devices are separate. They don’t talk to each other. I have to look at the data from the CGM and then make decisions about my insulin. I have to make about 300 decisions per day on average. It’s really fatiguing. So we created some algorithms that took my blood glucose data, the amount of insulin that I’ve given myself, and the amount of carbohydrates that I’ve decided that I’ve eaten, and ran them over and over again to give me a prediction of what my blood sugar was going to be and whether I need to take any action. Instead of having to constantly do the math myself, our system will push an alert to my phone or watch.

Ernesto: Does it dose you automatically?

Dana: Originally no, but more recently we’ve built a full closed loop version of #DIYPS, that is essentially an artificial pancreas, that talks to my pump and adjusts to give me a little more or a little less insulin.

Ernesto: Who writes the code?

Scott: I’m doing all the coding. I’m sure Dana could, but she has a lot going on and designs the algorithms. My title is Chief Spaghetti-Coder. This is the bleeding edge. It doesn’t need to be elegant code.

Ernesto: What have you learned from building your own pancreas?

Dana: The beauty of a CGM is that it gives you a data point every five minutes. Over the past year I’ve produced more than 130,000 data points of blood sugar levels alone. That gives me an incredible picture of what’s happening. With a traditional meter, it’s rare to find somebody who tests up to even 10 times a day. And the standard use for an insulin pump is very much “set it and forget it.” The #DIYPS allows me to customize without having to constantly adjust my insulin pump manually, and that frees me up to live my life, work, and do whatever it is that I want to do.

A visualization of Dana’s Data over the first year of the #DIYPS system.

A visualization of Dana’s Data over the first year of the #DIYPS system.

Ernesto: How did this project start?

Dana: We first started building the system just to make the alarms on the device louder, to wake me up because I would sleep through them. The device manufacturers didn’t seem to have a solution. Then we started looking at getting the data onto a computer so Scott would be able to view it. At the time, we had recently started dating, and he lives 20 miles away. I wanted him to be able to see what my blood glucose level was, so if it was low, he could text me; and if I didn’t respond, he could call 911. But we didn’t have a way to get the data off of the device.

Scott: The key moment was when we saw a tweet from John Costik, who was working on the Nightscout Project. Nightscout is open source code that helps people transmit their CGM data to other devices. I tweeted John right away: “Hey it would be awesome if we could get access to this code.” That’s really where it started. And along the way the whole process has been extremely public. We’ve been tweeting, blogging, and making everything we’ve been doing completely visible.

Ernesto: I’ve seen you tweet using the hashtag #wearenotwaiting. What does that mean?

Dana: #WeAreNotWaiting is a hashtag that was coined at a conference hosted by an online diabetes advocacy and information sharing community called DiabetesMine.com. For me it means that we’re not waiting for traditional device manufacturers to come out with the product. In three to ten years there’ll be devices like our artificial pancreas systems out in the market, being sold by companies approved by the FDA. I need to be alive when that system gets out in the market in, perhaps, five years.

I need to be alive when a cure becomes available.

Scott: Right about the time that we started working on #DIYPS, the Nightscout Project started to grow really quickly. There are now over 10,000 people in the CGM in the Cloud group. Over 2,000 people are using Nightscout to view their own or their loved ones’ blood sugar levels remotely on phones, watches, and other devices. This is real stuff that’s making a real difference in the world. And that’s only going to accelerate as more people do more interesting things like this closed loop that we’ve just done.

Ernesto: You’ve written about “data as free speech.” What do you mean? How can data be speech?

Dana: People often don’t understand why its legal for us to ‘hack’ a CGM and an insulin pump. (Note that hacking isn’t a negative thing; we’re just sharing the data across devices!) They assume that because all my DIY gadgets are not FDA-approved to use them the way I’m using them is somehow against the rules. But I can treat my own body, my own diabetes, the way I want to. And if I share my data, that’s obviously a kind of speech. But if we decide to share our code? I think the FDA sees this as a gray area. We very much want to continue our conversations with regulators.

Ernesto: Where do you see your project going?

Dana: I feel that every time I answer this question my answer changes, because my understanding of its potential is constantly changing. I never would have thought that any of what we’ve done was possible. Right now one of our goals is to make sure that the knowledge we gained about diabetes through our work with #DIYPS is adopted by clinicians, and that patients have access to this new information for treating diabetes. We’re also taking #DIYPS to a new level with #OpenAPS, an open and transparent effort to make safe and effective basic Artificial Pancreas System (APS) technology widely available to more quickly improve and save as many lives as possible and reduce the burden of Type 1 diabetes.

Dana with the #OpenAPS system.

Dana with the #OpenAPS system.

Scott: A few of months ago, at a conference convened by the advocacy group DiabetesMine, we got up and talked about our project, and I said: “I’m putting a stake in the ground that we’re going to make a closed loop artificial pancreas by August 1st, which is the date we’re getting married.” Everybody applauded and thought that was awesome. Then we went home. And we had it done in two weeks.

Dana: For anybody who wants to get involved in this, we would love to talk to you. There are so many people with diabetes and there is so much data that drives the management of this disease.

But there’s not a lot of awareness of how many diseases, including diabetes, could have their care revolutionized just by having better access to data.

That’s the thread of Quantified Self that I’m most interested in. The diabetes community happens to be one of the first to take advantage of what’s possible.

Dana tweeted her blood glucose data during this interview.

We invite you to share your data access stories, and this Access Conversation with the #qsaccess hashtag and follow along here in our Access Channel quantifiedself.com and @quantifiedself.

RWJF_Logo_Support2

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Quantified Homescreens

Quantifying-Homescreens

Betaworks recently announced that they had collected data from over 40,000 users who shared their iPhone homescreens through their apptly named #homescreen app. As they stated in their announcement, the apps people keep on their homescreen are often the apps they use the most. Being a data-minded individual I thought, “I wonder what kind of questions you could ask of this kind of data?” Of course, I immediately jumped to using the data to try and understand the landscape of self-tracking and quantified self app use. Let’s dive in.

Methods

I didn’t do anything fancy here. I used the search function to look up specific applications that I either use myself or have heard of. I also used the “in folders with” and “on homescreens with” lists to find additional applications that weren’t on my initial list. Each of the apps I found went into a Google Spreadsheet along with the “on % of homesceens” value reported by #homescreen. Additionally I categorized each of the apps into one of seven very broad categories: Activity, Fitness, Diet, Sleep, General Tracking, General Health, and Other.

Screen Shot 2015-02-14 at 9.52.05 AM

If you search for an app this is the data that is returned.

Results

I was able identify 65 unique applications reported as being present on #homescreen users iPhone homescreens. While in no way a complete list, I think it’s a good sample to see what people are using in their everyday life. So, how does the data actually break down?

Category Representation

categoryrepresentationThe most popular category was Activity with 20 apps (30.8%) followed by Fitness (15 / 23.1%), General Tracking (11 / 16.9%), a tie between General Health and Diet (6 / 9.2% each), Sleep (5 / 7.7%), and Other (2 / 3.1%).

Frequency of Homescreen Appearance

Perhaps unsurprisingly, Apple’s Health app tops the list here with a whoping 23.45%. No other self-tracking application even comes close to that level of homescreen penetration. The next most frequent application is Day One (a journaling app) with 8.45%.

homescreenfreq_WHealth

Clearly Apple Health skews the data so let’s look at homescreen frequency without it in the data set:

homescreenfreq_woHealth

When you exclude Apple Health (a clear outlier) the average percentage for homescreen frequency is 0.83% with a standard deviation of 1.39%. This data is skewed by a high number of applications that appear very infrequently. How skewed?

freqHistogram

If we disregard all apps that fall below this 1% cutoff what do we find? Fourteen apps meet this criteria: Coach.me, Day One, Fitbit, Health Mate (Withings), Moves, MyFitnessPal, Nike+, Pedometer++, Runkeeper, Runtastic Pro, Sleep Better, Sleep Cycle, Strava, and UP (Jawbone):

homescreenfreq_more1percent

Interestingly, MyFitnessPal is the only “Diet” app that made this >1% list, but it has the second highest appearance percentage at 4.36. Sleep Cycle is not far behind at 3.98%.

Discussion

Let’s start with the caveats. Obviously you can’t take these simple findings and generalize it to all individuals with smartphone, especially because this is only capturing iPhone users (many of the apps in this list have Android versions as well). Second, this data is based on a relatively small sample size of 40,000 individuals that are using the #homescreen app. Third, users of #homescreen are probably not representative of the general population of self-tracking application users. Lastly, it’s hard to draw conclusions about actual app use from this data. Like many people (myself included), I’m sure this data set has more than a few users who don’t regularly change their homescreen configuration when apps fall out of favor.

With that out of the way, what did I actually find interesting in this data set?

I found that sleep tracking, or having sleep tracking apps on a homescreen, was more popular than I thought it would be. Of the top 15 apps, 2 were sleep. When exploring by category, sleep had the highest mean appearance percentage (when also excluding Apple Health) at 1.32% (n = 5 apps).

For connected tracking devices, Fitbit (3.47%) is the clear winner, far outpacing it’s wearable rivals. The next closest application that is at least partially dedicated to syncing with a wearable device was Health Mate by Withings (2.14%).

I was reluctant to include Day One in this analysis. It isn’t commonly thought of as a “self-tracking” or “quantified self” application, but journaling and daily diaries are a valid form of tracking a life. Clearly it resonates with the people in this data set.

This was a fun exercise, but I’m sure there are many more questions that can be answered with this data set. I’ve compiled everything in an open google spreadsheet. I’ve enabled editing in the spreadsheet so feel free to add apps I might have missed or create additional charts and analysis.

And in the spirit of full disclosure, he’s my current homescreen.

This post first appeared on our Medium publication “Notes on Numbers, where we’re starting to share some of our thoughts and editorial experiments. Follow along with us there

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Meetups This Week

There will be six Quantified Self meetups occurring in two countries this week. Thessaloníki­ in Greece will be having their very first meetup. New York is a must attend event for they always have excellent talks. Chicago will have a toolmaker talk from the maker of an under-desk elliptical trainer that tracks usage, while southern Florida will have two meetups in Miami and Naples.

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. Don’t see one near you? Why not start your own! If you organize a QS meetup, please post pictures of your event to the Meetup website. We love seeing them.

Tuesday, February 17
Chicago, Illinois

Wednesday, February 18
Denton, Texas
New York City, New York

Thursday, February 19
Miami, Florida
Thessaloníki­, Greece

Friday, February 20
Naples, Florida

Also, here are some photos from Cambridge‘s meet up last week:
600_434225761.jpeg 600_434225764.jpeg 600_434225765.jpeg

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What We Are Reading

Enjoy this week’s list!

Articles
Would You Share Private Data for the Good of City Planning? by Henry Grabar. The use of personal, and typically private, data for municipal planning and research is becoming more common. Strava, Uber, and other companies are passing along their user data to government bodies interested in understanding their constituents. In this article, past projects are described and new ideas are put forth about this growing trend.

The social network for people who want to upload their DNA to the Internet by Daniela Hernandez. A wonderful piece of journalism on the growing OpenSNP  platform for open user-donated genetic data. Take some time and read the whole thing. (Full disclosure: My 23&Me data is available on OpenSNP.org.)

What Cognition-as-a-Service should mean? by Debidatta Dwibedi. The promise of fitness trackers for many is that the use of them will improve one’s fitness. Dwibedi expresses the desire for a tool to make one wiser by helping the user avoid logical fallacies. There are tools that can help, like spaced repetition.

Connected Car: Quantified Self becomes Quantified Car by Melanie Swan.

Sensors sensors everywhere
Near and far
On your wrist
In your home
And in your car.

What On Kawara’s Analog Wisdom at the Guggenheim Has to Offer a Digital World by Ben Davis. A fantastic peek into “On Kawara: Silence” a recently opened retrospective hosted at the Guggenheim.

He was making art about the “quantified self”—the contemporary self-improvement craze for tracking and charting one’s personal data—not just before the fitbit, but before the handheld calculator.

What My Hearing Aid Taught Me About the Future of Wearables by Ryan Budish. A great article here about how to think about possible ways our technology with change and shape the world around us. Special consideration is given to our ever evolving relationship with the tools of wearable computing.

Show&Tell
I tried to quantify my sex life—and I am appalled (NSFW language) by Miles Klee. I went back and forth whether to include this here, but in the end I think it’s important to expose tracking of all types.

How I audited my daily media habits and improved the way I read by Lydia Laurenson. Lydia was concerned with the amount of bad content she was reading on the web.For a month, she rated the articles she read according to a 5-point scale with categories like “I’m actually angry I clicked this link” and “Wow, this is really cool or useful. I’m glad I saw this.” With these ratings, she was able to see which publications produced good contents, and which outlets gave her recommendations worth her time. You can check out her (empty) tracking spreadsheet here.

DanBrown_Dinners
The Quantified Chef by Dan Brown. Dan doesn’t fancy himself a self-tracker, but was interested in understanding his cooking habits as the main dinner cook for his family. Some interesting finds and thoughts about what it means to collect data on yourself.

MorrisV_Eventsjpg
Using a Log Book and Excel To Assess Time Use by Morris Villarroel. Morris spoke about how he uses journals to track his life at our 2014 QS Europe Conference. In this post, he explains how he transfers hand-written data into Excel for more in-depth analysis.

Visualizations

SidLee
Sid Lee Dashboard. Sid Lee, a creative agency, outfitted it’s Paris office with multiple sensors and data gathering systems powered by Arduinos to feed a beautiful real-time data dashboard. Make sure to click through for the interactive site and watch their short video.

TylerBaird
Two Thousand And Fourteen by Tyler Baird. A sentence or two cannot do this amazing work justice. Click, read, and take in the 8,760 hours of Tyler’s tracked life.

Access Links
The BMJ Today: Patient Centered Care
Health Data Exploration Project Announces Agile Research Project Awards
FDA makes official its hands-off approach to regulating health apps and medical software
Small thoughts on large cohorts
Selling your right of privacy at $5 a pop

From the Forum
Continuous HRV monitoring
New Member
Separation of cloud vs local storage?

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Fitbit vs. Moves: An Exploration of Phone and Wearable Data

Like many people paying attention to the press around Quantified Self, self-tracking, and wearable technology I was intrigued by the many articles that focused on a newly published research letter in the Journal of the American Medical Association. The letter, Accuracy of Smartphone Applications and Wearable Devices for Tracking Physical Activity Data, authored by Meredith A. Case et al., described a laboratory study that examined a few different smartphone applications and self-tracking devices. Specifically, they tested the accuracy of steps reported by the three different apps: Moves (Galaxy S4 and iPhone 5s),  Withings Health Mate (iPhone 5s), and the Fitbit app (iPhone 5s), three wrist-worn devices: Nike Fuelband, Fitbit Flex, and the Jawbone UP24, and three waist-worn devices: Fitbit One, Fitbit Zip, and the Digi-Walker SW-200. Participants walked on a treadmill at 3.0 MPH for trials of 500 steps and 1500 steps while a research assistant manually counted the actual steps taken. Here’s what they found:

JAMA_PhoneWearables

As the data from this research isn’t available we’re left to rely on the authors description of the data. They state that differences in observed vs device recorded steps counts “ranged from−0.3% to 1.0% for the pedometer and accelerometers [waist], −22.7%to −1.5% for the wearable devices [wrist], and −6.7% to 6.2% for smartphone applications [phone apps].” Overall the authors concluded that devices and smartphone apps were generally accurate for measuring steps. However, much of the press around this study dipped into the realm of sensationalism or attention grabbing headlines, for instance: Science Says FitBit Is a Joke.

Part of our work here at Quantified Self Labs is to encourage and help individuals make sense of their own data. After reading this research letter, or one of the many articles which covered it, you might be asking yourself, “I wonder if my device is accurate?” or “Should I be using a step tracking device or just my phone?” In the interest of helping people make sense of their data so that they can come to their own conclusions I decided to do a quick analysis of my own personal data.

For this analysis I examined the step data derived from my Fibit One and the Moves app I have installed on my iPhone 5. (Important note: the iPhone 5 does not have the M7 or M8 chip present on the 5s and 6/6+, respectively, which natively tracks steps.) I had a sneaking suspicion that my data experience differed from the findings of Case and her colleagues. Specifically, I had a hypothesis that the data from every day tracking via the Moves app would be significantly different than data from my Fitbit One.

Methods

First, I downloaded and exported my daily aggregate Fitbit data for 2014 using our Google Spreadsheets Fitbit script. I then exported my complete Moves app data via their online web portal. To create a daily aggregate step value from my Moves data I collapsed all activities in the summary_2014.csv file for each day. (Side note: We’ll be publishing a series of how-to’s for doing simple data transformations like this soon). This allowed me to create a file with daily aggregate step data from both Moves and my Fitbit for each day of 2014. Unfortunately I did not have my Fitbit for the first few weeks of 2014 so the data represents steps counts for 342 days (1/24/14 to 12/31/14).

Results

I found that my Fitbit One consistently reports a higher number of total steps per day than my Moves app. Overall, for the 342 days I had 689,192 more steps reported by Fitbit than by the Moves app. The descriptive information is included in the table below:

FitbitMoves2

Another way to look at this is by visualizing both data sets across the full time-frame:

Click for interactive version in Google docs.

Click for interactive version in Google docs.

There a few interesting things to point out in this dataset. On two days I have 0 steps reported from my Moves app. One day, Moves was unable to connect with their online service due to me being in an area with little to no cell signal. On the other day my phone was off, probably due to an iOS 8 release and having to reboot my phone a few times.

It is also clear to me that differences in data are related to how I wear my Fitbit and use my phone. For my Fitbit, it is basically on my hip from the time I wake up until the time I go to bed each night. However, my phone isn’t always “on my body” throughout the day. I think this is probably the case for more people.

Since I wear my Fitbit at all times some of the data it captures erroneously is included in the total step count. For instance, for the last few months in this data set I was commuting about 10 miles per day during the week by bike. This data is accurately captured as cycling by Moves, but captured as steps by my Fitbit. Therefore some over-reporting by Fitbit is present in the data.

Conclusion

For my own data I found that the Fitbit reports higher steps on most, if not all days, than the Moves app on my iPhone 5. There are a few caveats with this data and analysis that are worth mentioning. First, this exploration was intended to begin a conversation around the real-world use of activity monitoring apps and devices, and the data they collect. It was not intended as a statement on truth or validity (however I would welcome the help of a volunteer to follow me around with a manual clicker counting all my steps). Second, this analysis was undertaken in part to help you understand that scientists of all types, be it citizen or academic, have the ability to work with their own data in order to come to their own conclusions about what works or doesn’t work for them. Lastly, this analysis was completed very quickly and I am sure that other individuals may have different ideas about how to explore and analyze the data. For this reason I’m posting the daily aggregate values in a open Google Spreadsheet here.

If you’re inspired to analyze your own data in this way we’d love to hear from you. Reach out on twitter or send us an email. We’re listening.

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QS Access: Personal Data Freedom

We are happy to welcome this guest post by Madeleine Ball. Madeleine is the Senior Research Scientist at PersonalGenomes.org, co-founder of the upcoming Open Humans project, and the Director of Research at the Harvard Personal Genome Project. She can be found online @madprime.

MBallThe digital trails we create are becoming thick and personal. Increasingly, people choose to collect meaningful data about themselves. Activity tracking to understand health and fitness. Genetic testing to understand ancestry and inheritance. Incidental data also expands: smartphones quietly observe our location through the day. Who gets to see our data? Can we see our own data? Beyond “privacy policy” documents, people are starting to call for something stronger: for personal data ownership.

Unpacking “data ownership”

It’s worth unpacking this phrase. What do we mean by “data ownership”? If we want to see changes, we need to start with a little more clarity.

Legally, data is not property. There is no copyright ownership of facts, as they are not “creative work”: the United States Supreme Court famously established this in the landmark case Feist vs. Rural. They are not patents, there is no invention. They are not trademarks. There is no “intellectual property” framework for data.

 Yes, data is controlled: through security measures, access control, and data use agreements that legally restrict its usage. But it’s not owned. So let’s set aside the word “ownership” and talk about what we really want.

Control over what others do

One thing we might want is: “to control what others do with our data”. Whom they share it with, what they use it for. Practically this can be difficult to enforce, but the legal instruments exist.

 If a company is generating data about you, then the “control” you have is spelled out in their contractual agreement with you. Check the policies: “Privacy policy” or “Data use policy” documents are a standard feature.

 Think about what you really want. Are you opposed to commercial use of your data? Look for words like “sell”, “lease”, and “commercial”. Are you concerned about privacy? Look for words like “share”, “third-parties”, and “aggregate” – and if individual data is shared, find out what that data is.

Companies won’t change if nobody is paying attention and nobody knows what they want. We can encourage change by getting specific, and by paying more attention to current policies. Raise awareness, criticize the bad actors, and praise the good ones.

Personal data access and freedom

The flip side of “data control” is our own rights: what can we do with our own data? We want access to our personal data, and the right to use it.

 This idea is newer, and it has a lot of potential. This was what Tim Berners-Lee called for, when he called for data ownership last fall.

“That data that [firms] have about you isn’t valuable to them as it is to you.”

I think it’s worth listening, when the inventor of the world wide web thinks we should have a right to our data.

So let’s spell it out. Let’s turn this into a list of freedoms we demand. We should be inspired by the free software and free culture movements, which advocate for other acts of sharing with users and consumers. In particular, inspired by Richard Stallman’s “Four Freedoms” for free software, I have a suggested list.

Three Freedoms of Personal Data Rights

Raw data access – Access to digital files in standard, non-proprietary file formats.

Without raw data, we are captive to the “interface” to data that a data holder provides. Raw data is the “source code” underlying this experience. Access to raw data is fundamental to giving us the freedom to use our data in other ways.

Freedom to share - No restriction on how we share our data with others.

Typically, when data holders provide access to data, their data use agreements limit how this data may be shared. These agreements are vital to protecting user privacy rights when third parties have access, but we have the right make our own sharing decisions about our own data.

Unrestricted use – Freedom to modify and use our data for any purpose.

 Data use agreements can also impose other limitations on what individuals can do with data. Any restriction imposed on our use of our data impinges on our personal data rights. Freedom for personal data means having the right to do anything we wish with data that came from us.

In the short term, access to raw data can seem obscure and irrelevant: most users cannot explore this data. But like the source code to software, access to this data has great potential: a few will be able to use it, and they can share their methods and software to create new tools.

Raw data access is also an opportunity for us to share for the greater good, on our own terms. We could share this data with research studies, to advance knowledge and technology. We could share data with developers, to develop software around it. We could share it with educators, with artists, with citizen scientists. We could even cut the red tape: dedicate our data public domain and make it a public good.

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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Richard Sprague: Fish Oil Makes me Smarter

Richard Sprague is interested in understanding peak performance. Over the last few years he’s been tracking various aspects of this life to try and understand what helps and what hinders. Inspired by our friend and renowned self-experimenter, Seth Roberts, Richard decided to test if consuming fish oil affected his response time. Using a simple reaction time test developed by Seth to test if butter made him smarter, Richard tested himself when he was and was not taking fish oil pills. In this talk, Richard explores his data and discusses what he found out when he ran his analysis.

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Meetups This Week

This week will bring six Quantified Self meetups in four countries! In Cambridge, they will have a toolmaker talk for Emotion Sense, a mood app developed by the University of Cambridge. Zürich will have a BYOD (Bring Your Own Device) session, where everyone will have some hands-on time with the QS tools in use by their community.

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. Don’t see one near you? Why not start your own! If you organize a QS meetup, please post pictures of your event to the Meetup website. We will feature them here.

Tuesday, February 10
Cambridge, England
Lansing, Michigan
Zürich, Switzerland

Wednesday, February 11
Lausanne, Switzerland
London, England

Thursday, February 12
Groningen, Netherlands

Here are some photos from last week’s meetup in San Francisco, including avuncular, lifelogging pioneer, Gordon Bell!

GordonBellBayAreaFebGaryBayAreaFeb

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What We Are Reading

Another batch of links, examples, and visualizations from our QS community and around the web. Enjoy!

Articles

The phone is a gateway drug to health: what MyFitnessPal knows, and what Under Armour gets by Jane Sarasohn-Kahn. There has been a lot of talk this week about Under Armour’s acquisition of the self-tracking app companies Endomondo and MyFitnessPal. Having read through many reactions, I thought this short post by Jane was one of the best.

The Electronic Health Record: Are we the tools of our tools? [PDF] by K. Patrick Ober and William B. Applegate. This article, written for The Pharos, a quarterly journal covering nontechnical medical subjects, is a very interesting peak into how some physicians are thinking about how they practice medicine in the era of the EHR. The authors make the case that the “EHR in the exam room” is not only degrading the patient-doctor relationship, but may be contributing to a growing lack of understanding and a reduction in the holistic view of patient care.

Introducing the #OpenAPS project by Dana Lewis and Scott Leibrand. Dana and Scott, pioneers in the open medical device data and #WeAreNotWaiting movement, have done it again. Building on their experience with testing an using an Artificial Pancreas System (APS) they’ve decide to release an open reference design for an “overnight closed loop APS system.”

We believe that we can make safe and effective APS technology available more quickly, to more people, rather than just waiting for current APS efforts to complete clinical trials and be FDA-approved and commercialized through traditional processes. And in the process, we believe we can engage the untapped potential of dozens or possibly hundreds of patient innovators and independent researchers and also make APS technology available to hundreds or thousands of people willing to participate as subjects in clinical trials.

Ringly’s Destiny Revealed by Robin Sloan. Too often, the narrative around the impending device-creep, which is invading every aspect of our lived experience, settles into a “look at  how terrible we’re becoming as humans.” I really liked how Robin Sloan spun the idea of networked devices into something that becomes a fun enjoyable hacked experience.

How Data Will Help Me Keep My Resolution by Emmy Ganos. Emmy, who is a program associate at the Robert Wood Johnson Foundation, recently attended a few “Data for Health” events and wrote up her thoughts. It was nice to see her expose some ideas around public/communal data and it’s impact on health as well as a this gem from our own Gary Wolf:

In San Francisco, I was surprised to hear Gary Wolf, the leader of the Quantified Self movement, passionately challenge the idea that historically disempowered groups are less capable of analyzing and understanding data about themselves. He shared the provocative point that we too often underestimate people’s intelligence, and think that we have to interpret data FOR people. Wolf’s point is that everyone deserves access to data about themselves, in whatever format it is available.

Quantified Man by Jedd Cole. A nice piece of of short fiction here. To say more would be to spoil it. (Side note: If you run across other QS-themed works of fiction please do send them in. We love reading them.)

Show&Tell

Using tools to analyze my uBiome results by Richard Sprague. Richard is a member of our great QS Seattle meetup group and recently gave a talk (video coming soon!) about analyzing and understanding his uBiome micorbiota data. In this post he walks us through analyzing his data using R. He also has another great post for analyzing the data in Excel if you’re so inclined.

Visualizations

TobiLehman_standing_histogram
Standing Desk Histogram by Tobi Lehman. Tobi has a standing desk and wanted to track how much he was actually standing. He wrote a simple script to allow him to track the state of his desk and found that he typically stands for less than an hour at a time.

ChristopherPenn_BasisWatson
Marketing Analytics Tools for Non-Marketing Uses by Christopher Penn. Don’t let the title fool you, this is a great Quantified Self post. Christopher accessed his data from his Basis watch, visualized it, and then fed it into IBM’s Watson to see what was actually influencing calorie expenditure.

Music_Qlik
Making Qlik sense of the music that you play by Patrick Tehubijuluw. A nice visualization here of Patrick’s music listening history. Make sure to click through to see how you can make this same visualization.

Access Links
Smart Ways to Manage Health Need Smart Regulation
HHS Changes Incentivize Value Driven Care, But What About Device Interoperability?
BYOD – Bring your own Data. Self-Tracking for Medical Practice and Research
Big Data: Seizing Opportunities, Preserving Values (White House Report, PDF)
Patient-Generated Data Fuels Population Health Management
ONC unveils Interoperability Roadmap for public comment
Medical researchers and health care providers must consider moral as well as legal questions on data use, says bioethics body

From the Forum
Basis Peak
QS for Down Syndrome children
Diagnosed sleep apnea, looking for metrics pre/post treatment
HRV app APIs
Separation of cloud vs local storage?

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