Search Results for: sleep

Meetups This Week

There will be eight great Quantified Self meetups happening all over the world this week. The group in Stockholm will have show&tell talks on sleep tracking, using the Scanadu Scout and using personal data to identify cause and effect in one’s life. Our exemplary QSXX Boston group will be getting together as well. To learn more on how the QSXX meetup groups create a safe space for discussing women-centric self-tracking topics, click here.

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.

Monday, March 2
Manchester, England
Oxford, England
Stockholm, Sweden

Tuesday, March 3
Reno, Nevada

Wednesday, March 4
Pittsburgh, Pennsylvania

Thursday, March 5
Lille, France
Toronto, Canada
QSXX Boston, Massachusetts

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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!

Articles
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.

Show&Tell
AustinWaterstop-25-word-frequency
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.

Visualizations

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!

SleepCycle_US-Wake-Up-Mood
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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What We Are Reading

We hope you enjoy this week’s What We’re Reading list!

Articles
The Wow of Wearables by Joseph Kvedar. An excellent post here in the wake of the “Smartphones vs. Wearables” hype in the past weeks. Favorite part:

“I’d have to say that reports of the death of wearables have been greatly exaggerated. The power of sensor-generated data in personal health and chronic illness management is simply too powerful to ignore.”

Survival of the Fittest: Health Care Accelerators Evolve Toward Specialization by Lisa Suennen. If you’re at all interested in the recent surge in health and healthcare focused accelerators this is for you. Excellent reporting. (Thanks for sharing Maarten!)

Your Brain Is Primed To Reach False Conclusions by Christie Aschwanden. Fascinating piece here about the nature of the “illusion of causality.”

A Few Throughs About Patient Health Data by Emil Chiauzzi. Emil, Research Director at PatientsLikeMe, lays out four point to consider when thinking about how to best use and grow self-collected patient data.

Having Parkinson’s since I was 13 has made me an expert in self-care by Sara Riggare.

I am the only person with the whole picture. To me, self-care is everything I do to stay as healthy as possible with a disease that is a difficult life companion. It entails everything from making sure I take my medication in the optimal way, to eating healthily, getting enough sleep, to making sure I stay physically active. I also make an effort to learn as much as I can about my condition; my neurologist says that I know more about Parkinson’s research than he does. I don’t find that odd, since he needs to try to stay on top of research in probably hundreds of neurological diseases, whereas I focus on just one.

From Bathroom to Healthroom: How Magical Technology will Revolutionize Human Health by Juhan Sonin. A beautifully written and illustrated essay on the design of our  personal healthcare future.

Show&Tell
Experimenting with sprints at the end of exercise routines by Gustavo M. Gustavo is a person with type 1 diabetes. After reading that post-exercise high intensity exertion might have an effect on blood glucose he put it to the test.

On Using RescueTime to Monitor Activity and Increase Productivity by Tamara Hala. Tamara walks us through the last three years of her RescueTime data and how she used that information to understand her work and productivity.

How Do You Find Time to Write? by Jamie Todd Rubin. Jamie has been writing for 576 consecutive days. How does he do it? A mixture of data and insight of course!

Visualizations
ILoveYouMaps Say “I Love You” With Mapping by Daniel Rosner. Wonderful to see CHI papers ending up on Medium. This seems like a fun self-tracking/art project.

ShannonConnors_4yearsfood Cleaning up and visualizing my food log data with JMP 12 by Shannon Conners. Once again, Shannon displays a wonderful ability to wow us with her data analysis and visualization. Above is four years of food tracking data!


Two Trains: Sonification of Income Inequality on the NYC Subway by Brian Foo. Brian created this data-driven musical composition based on income data from neighborhoods the border the 2 train. Beautiful work.

Access Links
Walgreens adds PatientsLikeMe data on medication side effects
How Open Data Can Reveal—And Correct—The Faults In Our Health System
Big Data is our Generation’s Civil Rights Issue, and We Don’t Know It.

From the Forum
Creating Scales for Quantifying Action
Sharing Anonymized Data

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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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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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Tidings: QS Ann Arbor Meetup Recap

Today’s post comes to us from PF Anderson, Emerging Technologies Informationist for the Health Sciences, University of Michigan and a member of the QS Ann Arbor meetup group. It first appeared on her excellent Emerging Technologies Librarian blog and we’re happy to republish it here.

Cool Toys, Devices, Quantified Self

Last week, I felt really lucky that I was able to make it to the first Quantified Self Meetup of the New Year (thanks to Nancy Gilby for the ride!). This session was held at the UMSI Entrepreneurship Center. Roughly ten people came, and I’m not sharing names even though they said I could because I’m not sure I got the names down right. The group included a wide range of types of people: corporate folk, students, entrepreneurs, faculty, alumni, and independents. The conversation was fast, dynamic, and overlapping, so I couldn’t catch everything. I will talk about what I did catch of the IDEAS and the GADGETS. That’s what’s really fun, eh?

INTERESTS

What the Meetup group page SAYS they are interested in (as a sampling) is pretty extensive.

“Aging in Place Technology • Behavior change and monitoring • Caregiving of digital patients • Chemical Body Load Counts • Citizen science• Digitizing Body Info • Medical Self-Diagnostics • Lifelogging• Location tracking • Non-invasive Probes• Mindfulness and wisdom tracking • Parenting through monitoring/ tracking • Personal Genome Sequencing • Psychological Self-Assessments • Risks/Legal Rights/Duties • Self Experimentation • Sharing Health Records • Wearable Sensemaking”

What’s even more interesting is what people said they were interested in as they went around the table.

  • aging population
  • big data
  • biohacking
  • data visualization
  • diabetes
  • epigenetics
  • fitness
  • geofencing
  • legal advice
  • patient communities
  • personal genomics
  • sleep tracking
  • telehealth

Continue reading

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What We Are Reading

Philosophy, bicycles and brains, opinions on tracking sleep, learning from actually tracking sleep, and visualizing work through vigilant self-report – all these and more in our reading list below. Enjoy!

Articles
Sleep apps and the quantified self: blessing or curse? by Jan Van den Bulck. Here at QS Labs, we’re very interested in how the academic and research world is colliding with those of us using tools of measurement previously restricted to science. In this Letter to the Editor, published in the Journal of Sleep Research, the author lays out an interesting set of opinions about the increasing availability and use of commercial sleep tracking devices. (You can access the full pdf here.)

Mindrider

Measuring Brainwaves to Make a New Kind of Bike Map for NYC by Alex Davies. Readers of the QS website may remember a great show&tell talk we featured back in May of 2014. In that talk, Arlene Ducao discussed her MindRider Project, an EEG tracking bicycle helmet. In this short piece, we learn that Arlene has continued this awesome work and has produced MindRider Maps Manhattan, exposing the brain data of 10 cyclists as they transversed New York City.

Big Data and Human Rights, a New and Sometimes Awkward Relationship by Kathy Wren. Earlier this year the AAAS Science and Human Rights Coalition held a meeting to discuss the intersection of personal data collection and human rights. This short article describing some of the key discussion points is a great place to start if you’re exploring what “big” and personal data means to you and your use of the tools and services that collect it. (Videos of the meeting are also available.)

How Theory Matters: Benjamin, Foucault, and Quantified Self—Oh My! by Jamie Sherman. A very interesting and thought-provoking essay here on the nature of self-tracking and data collection framed against the works of Michel Foucault and Walter Benjamin. We count ourselves lucky to have Jamie as an active member and observer of our QS community.

But taken together, Foucault and Benjamin suggest that the penetration of data into daily life is part of a larger shift underway, and that changes we can already see in social life, politics, and labor are not unrelated, but rather intimately linked.

Compulsory Quantified Self by Gwyneth Olwyn. I think it’s good practice to try and expose ourselves to all sides of the conversation around self-tracking, the positive and the negative. In this blog post Gwyneth describes a few ideas about the purpose and outcomes of self-tracking, especially when the self is superseded by the demands of others (such as in a workplace wellness program).

Show&Tells
RyanQuan_sleep-cycle-analysis-03
Sleep Data Analysis with R by Ryan Quan. Ryan has been tracking his sleep with the Sleep Cycle app for the last two years. In this excellent post he explores and plots his data (yay export!) to see when he goes to sleep, how long he sleeps, and what really makes up “quality sleep.” Love the fact that he included his R code and sample data. Go Ryan!

Quantifying Goals Using Key Performance Indicators (KPIs) by Bob Troia. No data in this post, but I found it particularly inspiring to see how Bob was planning on keeping track of his goals for this year. If you’re looking for ideas for tracking your 2015 goals and Key Performance Indicators this is a great place to start.

Visualizations

EricBoam_Resume
The Resume Of The Future by Eric Boam. The above is one of the two beautiful visualizations created by Eric to explore his daily work activity and interactions. This visualization shows what he was actually spending his time on. How did he collect the data? Well, he used the Reporter App to ask himself three questions: “where are you, what are you doing, and who are you with?” Make sure to read his post, he developed very interesting insights through collecting this data.

JawboneWeightLoss
Weight Loss: What Really Works? by Emi Nomura and Laura Borel. Another fascinating data analysis project here by the Jawbone data science team. They examined the behaviors of a group of users who lost at least 10% of their starting weight vs users with no weight loss and found that the biggest difference in behavior was tracking meals.

ER_RunRide2013-14
Mapping my Last Two Years of Runs and Rides
While browsing the r/dataisbeautiful subreddit I stumbled upon this interesting tool/company that visualizes the maps of your runs and bike rides by connecting to your Runkeeper or Strava account. Above I’ve included my 2013 and 2014 maps. Clearly I need to find some new running routes in my neighborhood. (click through to enlarge)

QS Access Links
As part of our new work highlighting stories, issues, and innovations related to personal data access we’re going to start publishing a short collections links in this space. As this works grows be on the lookout for a new Access Newsletter from QS Labs.

Who Should Have Access to Your DNA?
What FDA developments in Diabetes mean for FDA approval in Digital Health
Open consent, biobanking and data protection law: can open consent be ‘informed’ under the forthcoming data protection regulation?
WTF! It Should Not Be Illegal to Hack Your Own Car’s Computer
Unique in the shopping mall: On the reidentifiability of credit card metadata
Majority of Consumers Want to Own the Personal Data Collected from their Smart Devices
Who Owns Patient Data
Los Angeles County Supervisors OK Creation of Open-Data Website

From the Forum
Jawbone Up
How to find all major volunteer bioscience projects I can partake in?
Bluetooth pulse oximeters…
Best Heart Rate Monitor that syncs with Withings Ecosystem

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QS Access: Data Donation Part 1

New sensors are peeking into previously invisible or hard to understand human behaviors and information. This has led to many researchers and organizations developing an interest in exploring and learning from the increasing amount of personal self-tracking data being produced by self-trackers. Even though individuals are producing more and more personal data that could possibly provide insights into health and wellness, access to that data remains a hurdle. Over the last few years a few different projects, companies, and research studies have launched to tackle this data access issue. As an introduction to this area, we’ve put together a short list of three interesting projects that involve donating personal data for broader use.

DataDonors.org
Developed and administed by the WikiLife foundation, the DataDonors platform allows individuals to upload and donate various forms of self-report and Quantified Self data. Data is currently available to the public at no cost in an aggregated format (JSON/CSV). Data types includes physical activity, diet, sleep, mood, and many others.

OpenSNP.org
OpenSNP is an online community of over 1600 individuals who’ve chosen to upload and publicly share their direct-to-consumer genetic testing results ( 23andMe, deCODEme or FamilyTreeDNA) . Genotype and phenotype data is freely available to the public.

Open Paths
Open Paths is an Android and iOS geolocation data collection tool developed by the New York Times R&D Lab. It periodically collects, transmits, and stores your geolocation in a secure database. The data is available to users via an API and data export functions. Additionally, users can grant access to their data to researchers who have submitted projects.

We’ll be expanding this list in the coming weeks with additional companies, projects, and research studies that involve personal self-tracking data donation. If you have one to share comment here or get in touch.

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What We Are Reading

Below you’ll find this week’s selection of interesting bits and pieces from around the web. Enjoy!

Articles
Open Books: The E-Reader Reads You by Rob Horning. A fantastic essay about the nature of delight and discovery, and how that may (is) changing due to data collected from e-readers. For those interested in books and data this article By Buzzfeed’s Joseph Bernstein is also an interesting read.

Flashing lights in the quantified self-city-nation by Matthew W. Wilson. Quantified Self, smart cities, and Kanye West quotes – this commentary in the Regional Studies, Regional Science journal has it all. Read closely, especially the final paragraph, which gives space to think about the role the institutions and companies that provide cities with the means to “be smart” have in our in social and urban spaces.

Most Wearable Technology Has Been a Commercial Failure, Says Historian by Madeleine Monson-Rosen. This is a interesting book review for Susan Elizabeth Ryan’s Garments of Paradise which had me thinking about the nature of wearables, customization, and expression.

‘The Cloud’ and Other Dangerous Metaphors by Tim Hwang and Karen Levy. This was mentioned so many times over the last few days by so many smart friends and colleagues that I had to set aside time to read it. It was time well spent. The authors make the case that how we talk about data (personal, public, mechanical, and bioligical) is tied to the metaphors we use, and how those metaphors can either help or hinder the broader ethical and cultural questions we find ourselves grappling with.

Why the Internet Should Be a Public Resource by Philip N. Howard. This isn’t the first, nor will it be the last, argument for changing the way we think about and regulate the Internet. Worth reading the whole things, but in case you don’t consider this point:

And then we might even imagine an internet of things as a public resource that donates data flows, processing time, and bandwidth to non-profits, churches, civic groups, public health experts, academics, and communities in need.

Computers Are Learning How To Treat Illnesses By Playing Poker And Atari by Oliver Roeder. How does research into algorithms and AI intended for winning poker games morph into something that can optimize insulin treatment? An interesting exploration on the background and future implications of computers that can learn how to play games.

Data Stories #45 With Nicholas Felton. by Enrico Bertini and Moritz Stefaner. In this episode of the great Data Stories podcast Nicholas Felton talks about his background, his interest in typography, and what led him to start producing personal annual reports. Super fun to listen to them geek out about the tools Nicholas uses to track himself.

Increasingly, people are tracking their every move by Mark Mann. A great peak into some of our QS Toronto community members and how they use self-tracking.

Quantified Existentialism by Ernesto Ramirez. I’m putting this last here because it feels a bit self-congratulatory. Earlier this week I took some time to examine how common it is for people to express their relationship with what counts when they use self-tracking tools. It was a fun exercise.

Show&Tell
Insights From User Generated Heart Rate Variability Data by Marco Altini. While not a personal show&tell (however, I’m sure his data is in there somewhere), this great post details what Marco was able to learn about HRV based on 230 users and 13,758 recordings of HRV.

Quantify This Thursday: No Coding Required by Kerri MacKay. A bit different post here, more of a how-to, but I found it really compelling the lengths Kerri went to get get her Fitbit data to show up on he Pebble watch. I was especially drawn to her explanation of why this method is important to her:

The reality is, getting nudges every time I look at the clock or dismiss a text notification on my Pebble (via my step count) is yet another way to make the wearing-a-wearable less passive and the data meaningful.

Correlating Weight with Blood Pressure by Sam. A short and simple post detailing how Sam used Zenobase and his iHealth devices to see how weight loss was associated with his blood pressure.

Visualizations
WithingHolidays
The Effect of End of Year Festivities on Health Habits by Withings. The above is just one of four great visualizations from Withings exploring how the holidays affect how users sleep, move, and weight themselves. Unsurprisingly people are less likely to weight themselves on Christmas day (I looked at my data, I am among those non-weighers).

SimonData
Simon Buechi: In Pure Data by Simon Buechi. A simple, elegant dashboard intended to represent himself to the world.

MatYancy_Coding
Grad School Coding Analysis by Matt Yancey. The above is just a preview of two fantastic visualizations that summarize the coding Matt did while enrolled in the Northewestern Masters of Analytics program.

Fitbit_NewYears_Steps
News Year’s Eve Celebration in Steps by Lenna K./Fitbit. A fun visualization describing differences in how people in different age groups moved while celebrating the new year.

From The Forum
How do I visualize information quickly? (mobile app)
Monitoring Daily Emotions
Best Heartrate Monitor that syncs with Withings Ecosystem
Is the BodyMedia Fit still alive?
Capture Online Activities (and More) into Day One Journal Software (Mac/iOS)

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