Topic Archives: Discussions

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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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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Counting Money

Yesterday was the first day in a month that I handled cash. For weeks everything I’ve purchased and paid for has been handled by digital means. Debit cards, direct debits and deposits, internet purchases – it’s all 1′s and 0′s flowing through the tubes, and it’s makes my life very easy. However, now that the flow of money in and out of my life is easier, I have to find new ways of being aware of what’s happening to the money. I’ve gathered up a few examples of QS projects, show&tell talks and articles related to money – please feel free to share your own favorites. -Ernesto

Show&Tell Videos

Amaan Penang: Making Data-Driven Financial Decisions
Amaan Penang was faced with a life change when he moved from Texas to California to start a new job. While preparing for the move he started to examine his financial health and was surprised by what he didn’t understand about his spending and income. Using the popular financial tracking software, Mint, he started to examine his historical spending. In this talk Amaaan explains what he learned and how he was surprised to find out how this data opened up the doors to exploration and better financial health.

Natty Hoffman: The Enlightened Consumer
Natty had a large amount of financial data, over 14 years of expenses and spending, that she was accessing from credit card and bank statements. Because of her work as a consultant she was experiences with understanding and reconciling her various accounts and reimbursements. It wasn’t until attending a QS meetup in Boston that she realized that there was more to her data than just historical financial documents,

“I didn’t really think much about this data until I went to a Quantified Self meetup a few months ago. And then I said to myself ‘You know, I have some pretty interesting data about myself as a consumer and I wonder what I’m going to find out.”

Natty started exploring her data by looking back at the last two years to better understand the where her money was going based on a broad categorization scheme. But, she didn’t stop there. She went on to explore exactly where she was spending her money and found that she was a customer of over 300 different businesses over the two years she examined. Intrigued by the the companies she frequented she went deeper and started to see how she did as a consumer and if her spending behavior matched her personal ideals.

Matic Bitenc: Manual Finance Tracking
Outside the US there aren’t many good options for automatically tracking personal finances. Matic and his partners created Toshl Finance, an application for manually tracking how he was spending his money. In this talk Matic describes what he learned about his expenses and lifestyle by using a simple tag-based system and easy to understand visualizations.

Examples of Personal Finance Tracking

Tracking, Classifying, and Comparing Expenses by Karsten W.
We featured this very interesting tracking project in 2012 when Karsten embarked on a experiment to track his spending via a simple Twitter tool. Not satisfied with just tracking, he also categorized and compared his spending habits to what a typical person in his country (Germany) spent in different categories.

How I track my personal finances and Keeping (financial) score with Ledger by Sacha Chua.
Two great posts by our QS Toronto co-organizer, Sacha Chua. In the first she describes how she sets up understanding her financial life, and in the second she describes her tracking methodology.

I Tracked Every Penny I Spent For One Year. Here’s What I Learnt by Todd Green.
As the title says, Todd tracked his spending for an entire year. In this post he describes the process and the top 10 lessons he learned.

Articles of Note:

The Quantified Self Movement Reaches Personal Finance
Key Quote: “Personal finance tools as they evolve will take this technology much farther. GPS-based navigational systems have both improved and become more ubiquitous as raw data have become more available and the cost for both devices and services has dropped. So too will personal finance apps begin to follow us around. They’ll live in our phones or on our wrists, pulling in real-time data to help us take control of our own short-term liquidity and solvency needs and long-term retirement goals.”

What Health and Finance Can Learn From the Quantified Self Movement and Each Other.
Key Quote: “Few domains of life are as quantified as your financial self — you have your credit score, savings and checking balances, 401Ks, stocks, bonds, funds and more aided by countless apps, reports and plans provided by banks, employers and financial advisors all available online, on the phone, in person and at your local ATM.”

Banking on you — how wearable tech could change finance.
Key Quote: “Historically, banks have been some of the richest repositories of data — but also the least likely to do something innovative with it. This is partly due to regulation, but mostly due to a self-limiting mindset prevailing in the banking industry. Till now, consumers have accepted this status quo, but not for much longer. As they find their ‘quantified selves’ no doubt their demand for insights into their finances will increase.”

Financial Wearables – Part 1: Can high-tech wearables solve underserved people’s financial problems?
Key Quote: “Managing money in cash is time consuming—time to get cash, calculate it, record your every transaction. Banks do most of those actions, but do not teach you how to spend better and save money at the same time. The potential power of wearables is not in presenting you with “transactional information” about how many steps you took on a given day, but rather in showing how you can improve those steps over time with alerts, recommendations and visual elements. Banks could use the “wearables” power to incentivize users to better their financial health, deliver liquidity management tools and foster strong banking relationships and maximizing customers’ assets instead of their fees. It not only helps individuals but the bank as well.”

YOUR MONEY-Financial obsessives track every penny, every minute
Key Quote: “Australian academics Ken Cheng and Megan Oaten of Sydney’s Macquarie University once had volunteers write down every single purchase for four months, which led to marked improvement in their financial lives. They also found that positive financial habits started bleeding into other areas, with the volunteers improving their behavior in everything from house cleaning to exercising.”

‘Quantified Self’ Movement Now Lets You Track Your Money Too
Key Quote: “Cozy Cloud co-founder Frank Rousseau was originally inspired to invent the self-hostable personal cloud platform because he wanted an open source alternative to Mint.com, he told us earlier this year. But the hard part is that most banks don’t provide APIs to help users get their data out of the banks, according to the project website. To do this, Open Bank Manager is relying on a tool called Weboob (WeB Outside Of Browser) to scape data from banking sites.”

ToolsNot a complete list, so please add more in the comments and we’ll update here
Mint
Money, by Jumsoft
Personal Capital
Expensify
DollarBird
Spending Tracker
Checkbook
Also make sure to check out the long list of personal finance apps people are talking about on Product Hunt

Additional Reading
Why Wesabe failed: Marc Hedlund’s Challenge
An interesting look back at how another personal finance tool failed in the face of competition from Mint.

How can new interactions with digital money make us more aware of our spending? Chris Woebken talks about this design experiments here.

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How To Access & Export MyFitnessPal Data

MFP

MyFitnessPal is one of the leading dietary tracking tools, currently used by tens of millions of people all around the world to better track and understand the foods they consume every day. Their mobile apps and online tools allow individuals to enter foods and keep track of their micro- and macro-nutrient consumption, connect additional devices such as fitness trackers, and connect with their community – all in the name of weight management. However, there is no natively available method for easily accessing your dietary data for personal analysis, visualization, or storage.

With a bit of digging in the MyFitnessPal help section we can see that they have no official support for data export. However, they mention the ability to print reports and save PDF files that contain your historical data. While better than some services, a PDF document is far from easy to use when you’re trying to make your own charts or take a deeper look into your data.

We spent some time combing the web for examples of MyFitnessPal data export solutions over the last few days. We hope that some of these are useful to you in your ongoing self-tracking experiences.

Browser Extensions/Bookmarks

MyFitnessPal Data Downloader: This extension allows you to directly download a CSV report from your Food Report page. (Chrome only)

MyFitnessPal Data Export: This extension is tied to another website, FoodFastFit.com. If you install the extension, it will redirect you back to that site where your data is displayed and you can download the CSV file. (Chrome only)

ExportMFP: A simple bookmark that will open a text area with comma-separated values for weight and calories, which you can copy/paste into your data editor of choice.

MyFitnessPal Reports: A bookmarklet that allows you to generates more detailed graphs and reports.

 Web Apps/Tools

MyFitnessPal Analyser: Accesses your diet and weight data. It requires you to input your password so be careful.

Export MyFitnessPal Data to CSV: Simple web tool for exporting your data.

FreeMyDiary: A recently developed tool for exporting your food diary data.

Technical Solutions

MyFitnessPal Data Access via Python: If you’re comfortable working with the Python language, this might be for you. Developed by Adam Coddington, it allows access to your MyFitnessPal data programmatically

MFP Extractor and Trend Watcher: An Excel Macro, developed by a MyFitnessPal user, that exports your dietary and weight data into Excel. This will only work for Windows users.

Access MyFitnessPal Data in R: If you’re familiar with R, then this might work for you.

QS Access + Apple HealthKit

If you’re an iPhone user, you can connect MyFitnessPal to Apple’s HealthKit app to view your MyFitnessPal data alongside other data you’re collecting. You can also easily export the data from your Health app using our QS Access app. Data is available in hourly and daily breakdowns, and you should be able to export any data type MyFitnessPal is collecting to HealthKit.

As always, we’re interested to hear your stories and learn about your experiences with exploring your data. Feel free to leave comments here or get in touch via twitter or email.

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Quantifying in the Classroom

VL_improvingdata

Victor Lee makes data fun for kids. Victor is an assistant professor at Utah State University, where he’s been working on ways to bring the Quantified Self experience into high school classrooms to improve data literacy and expose students to “more authentic forms of inquiry.”

I first met Victor at the 2013 Quantified Self Global Conference, where we had a great conversation about QS and education. A few weeks ago, I saw this wonderful presentation of his research (a video is embedded below). I couldn’t resist the chance to ask him a few questions about his work.

If you are a teacher or a parent interested in using QS as a way to get kids into math and science, we want to especially invite you to QS15: The Quantified Self Global Conference and Exposition, where QS and Education is an important theme.

QS: What inspired you to work on data literacy as a subject area?

victor_profileVictor Lee: On the one hand, the first time I had a wearable device (a heart rate monitor, several years ago), I was impressed with the data I could receive and what kind of inferences I could make from them. This made me aware of an opportunity with the technology. At the same time, I know from my training and work as a learning scientist that data can be notoriously difficult for students (and adults) to understand and use. I have done a lot of work in the area of science education previously, and finding ways for kids to meaningfully work with and learn from data is one of the big challenges that have been documented for quite some time. Plus, the topic of data literacy is timely in that we have much greater access to data in so many forms, whether it is charts showing how the Earth’s climate is changing or if it is infographics that make the rounds on the internet. There is a lot of talk about “big data”, and if we are going to have the next generation of workers and citizens be ready to work with all of that data, we really need to work on supporting that literacy now.

QS: Your research involves students using wearable devices to generate and explore their own data. Why is having their own data an important part of this process?

VL: Informally, I talk about the benefit of wearables as helping to make it so that students have some “skin in the game”. Data that is about you is consequential because at some level, it reveals something about who you are and the things you do. In some sense, there is something inherently interesting about learning more about yourself and also comparing yourself against others or against the goals that you have set. But beyond that, there is a lot to leverage in the way of learning. One of the big ideas out of education and the learning sciences, and one that I can’t reiterate enough, is the importance of building on prior knowledge. When you look at data about yourself, you don’t strictly see points or dots or bars. You see a depiction of an experience or activity that is already intimately familiar. In some sense, you know why the data look the way that they do. If it’s exercise data, you remember what certain moments felt like. You have some expertise on how your body works and that creates some expectations and support for thinking with data. That is a really important bit of personal knowledge to leverage.

Wearables are also useful in that they expose students to some of the messier things that come along with data. You have to realize that a tracking device is doing some form of measurement and measurements are prone to some error. You get so much data that you can start to see typicality and patterns. Data are hard to collect, but being able to wear something that collects data in the background means you can collect a lot of data and see what regularity looks like. You get to see what noise looks like. You get to know what is an outlier and how that fits against the larger set of data. Too often in school, we are given these very sanitized data experiences and students don’t get to think through these things or experience them in a familiar or meaningful way.

QS: Each of your examples that you discuss during your talk were student-led and defined experiments. What role does allowing students to ask the questions play in the learning process?

VL: Students are inherently curious. They have their own interests and things they care about that speak to their experiences and their concerns. A student-centered approach to instruction tries to capitalize on that. That means looking to students for questions. It also helps to put some more skin in the game. But beyond that, it helps in learning how to actually do science. If you ask most science educators, the big goal is not to memorize facts and terms but to know how science is done and how things move from questions to data to conclusions to new questions and so on. This is critically important, and there is an even greater push for new technologies and new models in education to support this. It certainly is not easy, but I think that it is worthwhile and that the kids who get to really do it find it worthwhile too.

QS: It appears that your central thesis is “When you give them the opportunity, kids can learn hard things.” Clearly from your examples this is true. Outlier analysis, data visualization, and pattern recognition are all present. What makes the methods you’re exploring so impactful?

VL: I suspect it has to do with how the students are able to leverage their own experiences and interests. It is memorable but also consequential to something that they already do or encounter. It lets them sit in the driver’s seat when they often are put in situations that makes them more of a passenger. In fact, I think that is one of the interesting things about the Quantified Self movement. In some respects, it is increasing access to data and making it possible for people to do aspects of science or mathematics or statistics in ways that are meaningful to them.

I do also want to credit some really remarkable teachers and schools that allow for activities like this to happen. Especially in this day and age with an intense focus on testing, students don’t get as many opportunities to be curious in this way. Having motivated and flexible teachers on board certainly helps make this impactful.

QS: I was really struck by the anecdotes you shared that showed how strongly the students were affected by the lessons plans (Note: fast forward to 42:40 in the video above for a great example.) Are there any other stories that come to mind that help illustrate how students engage with these type of personal data based curriculums?

VL: We recently finished a project with a school we had never worked with previously. There were some students at this school who were really disappointed that the unit ended and they would not be able to keep working with the wearable devices that we provided (in this case, Fitbit Flex wristbands). I know one student that we worked with who was so enthusiastic that he wanted to keep on doing data collection and analysis using the kinds of tools we provided and would pull a member of my research team aside to help him plan how to keep working with data after the unit had ended. I know another student was really distraught that he was going to be absent on a day that he was set to share what he had did with activity data with the rest of the class. The teacher ended up extending things another day so that student could be there and still share his discoveries.

I may have mentioned this in the presentation, but the students who discovered outlier sensitivity were so enthusiastic about what they learned that after we shut down the cameras and were leaving, they began to boast to the other students what they had figured out and proceeded to show them how outlier sensitivity worked.

VL_quantifyingrecess

QS: You’ve done some work with trying to implement QS meetups in schools and in younger age groups. What have you learned from that experience?

VL: This has been an interesting side project. Basically, I wanted to give some students who did not have the opportunity to experience QS (in this case, some very talented and motivated high school Latina girls). Those students were terrific to work with, but the experience raised some interesting challenges and concerns about how much background infrastructure that needs to be in place to be a QSer. While they all had access to mobile devices, they were not the cutting edge. Some did not have wifi at home or bluetooth. They also felt that the latest and greatest wearable devices, while cool, didn’t fit with their aesthetic. And they had some constraints on their life circumstances that limited how much they could experiment with the devices. I presented some of these findings at a workshop as part of a recent ubiquitous computing conference. There is the potential for several potential benefits with QS being accessible to youth, but I think that this is a population with very different needs and concerns than those who are early adopters. If we want QS to be something that could be of value to youth beyond a classroom curriculum, we need to do some more targeted research and development. That’s generally something that I would be glad to pursue more in the future, as I imagine are many of my professional colleagues.

QS: Lastly, what new ideas and projects are you excited about?

VL: I am excited to do a more detailed analysis of what students learned from our most recent launch of a wearables-based data unit in the sixth grade. I am excited to potentially extend some of our findings to other grade levels and finding the best ways to address how self data could be useful in supporting rich student learning. I have been generally intrigued by the QS movement and have been trying to understand why people self-track and what they end up doing with the data they collect. There are other project in my field that I think are outstanding. At UC Davis, there is some work to get self data as input into digital games. We are actually starting to explore similar issues at Utah State. There is a neat project at CU-Boulder with kids building their own infographics, and I would love for self collected data to gradually become a part of that. I have had some side conversations with some organizations who have been thinking about wearable sensors in schools, and if those conversations continue and we are able to share what we have learned, I think there is much to be excited about in that area.

We want to thank Victor for taking the time to speak with us about his work. If you’re interested in learning more about Victor’s research we invite you to visit his faculty page and read some of this great research papers, a selection of which are linked below:

Quantified Recess: Design of an activity for elementary students involving analysis of their own movement data.
Integrating physical activity data technologies into elementary school classrooms
The Quantified Self (QS) Movement and Some Emerging Opportunities for the Educational Technology Field

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What is a Quantified Self Conference?

If you’ve seen the announcement for our 2015 QS Conference & Expo and you’ve never been to a QS event before you may be asking yourself what our conferences are all about. From our very first meetup in 2008 through our six conferences and numerous events we’ve emphasized the role of the personal story and real-world experience. We do this in a variety of ways.

First, we run our conferences as a carefully curated unconference. When you register, you’re asked to tell us about the self-tracking projects you’re working on and other QS-related ideas you have. Our conference organization team goes through every registration, diving deep into personal websites, Twitter feeds, and blog posts. We love seeing individuals using self-tracking in new and different ways to find out something interesting about themselves and we work hard to surface truly unique and inspiring stories.

How does that manifest itself in the program? The core of our conference program is made up of the nearly two dozen show&tell talks where self-trackers get up and tell their story by answering our three prime questions: What did you do? How did you do it? What did you learn? It may seem simple, but these three questions provide a stable and consistent narrative to inspire you to learn and engage with your own tracking practice in new and different ways.

We’ve spent some time combing through our vast video archive to showcase some of our favorite talks from our previous conferences. We hope you find them enjoyable and they inspire you to join us on June 18-20 in San Francisco for our 2015 QS Conference & Expo. Who knows, maybe you’ll be on stage and we’ll be learning from you!

Sara Riggare on ‘How Not To Fall’
Sara Riggare is co-organizer of Quantified Self Stockholm. She is also an engineer, a PhD student and a tireless researcher of Parkinson’s disease. In this fascinating talk, Sara describes using body sensors to help her control her gait.

Vivian Ming on Tracking Her Son’s Diabetes
Vivienne Ming is an accomplished neuroscientist and entrepreneur. Two years ago her son, Felix, was diagnosed with Type 1 Diabetes. In this talk, presented at the 2013 Quantified Self Global Conference, Vivienne explains what they’re learning as they track and analyze his data

Chris Bartley on Understanding Chronic Fatigue
While on a research trip, Chris contracted Reiter’s Syndrome. After his recovered, something still didn’t feel right. Chris consulted his physician and started tracking his wellness along with his diet and supplement intake. What follows is an amazing story about what Chris learned when he started applying his knowledge of statistics to his own data.

Adrienne Andrew Slaughter on Tracking Carbs and Exercise
Adrienne Andrew Slaughter was testing out a new diet that included carbohydrate restriction. At the same time she was commuting to work on a bike. She started to notice feeling tired and slow during her commutes and wondered if her dietary changes had anything to do with it. Luckily, Adrienne was tracking her commutes and her diet and was able to run detailed data analysis to find out what happens when she goes carbless.

Bob Troia: Understanding My Blood Glucose
Bob Troia isn’t a diabetic and he’s not out of range, but he wanted to see if he could lower his fasting glucose levels. He started a long-term tracking experiment where he tested his blood glucose and began to explore the effects of supplementation and lifestyle factors.

Sacha Chua on Building and Using A Personal Dashboard
Sacha Chua started tracking her clothes to make sure she was varying her wardrobe on daily basis. This led he to ask, “What else can I track?” As she added time tracking, food, library books, and so much more (you can view the whole set on QuantifiedAwesome.com)

Robby Macdonnell on Tracking 8,000 Screen Hours
For the last six years Robby Macdonnell has been tracking his productivity and how he spends his time on his various computers (home and work) and even how he uses phone. Over those years he’s amassed 8,300 hours of screen time. Watch his great talk to hear what’s he learned about his work habits, productivity and how he’s come to think about time.

Sky Christopherson on Self-Tracking at the London Olympics
Sky Christopherson first shared his experience with tracking and improving his sleep in 2012. That tracking led him on a path to achieving a world record as a mastars level track cyclists. Later that year, Sky began helping other athletes us self-tracking and personal data to obtain their best performances, culminating in a surprise silver medal for the 2012 women’s olympic track cycling team, on which he served as a training advisor. In March of this year, Sky and his wife Tamara gave another QS talk at our Bay Area Meetup in which they told the wonderful story of how the 2012 Olympic team rode to their medal, a journey captured in the documentary, Personal Gold.

These are only a small sample of the amazing talks and self-tracking projects that are shared at our Quantified Self Conferences. We’d love to hear your story. Register today and let us know what you’re working on!

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Wrist Wearables: How Many Are There?

In response to the much anticipated reveal of the Apple Watch I did a bit of digging around to find out where we stand with wrist-worn wearable devices. I found over 60 different devices. The following list focuses on self-tracking tools, I intentionally left out those that work only as notification centers or secondary displays for your phone. I’m sure this isn’t all of them, but it’s as good a place to start as any. If you’re using one of these devices to learn something about yourself, or you’re just interested in these type of wearable tools we invite you to join us in San Francisco on June 18-20, 2015, for our QS15 Conference & Exposition.

(Thank you to all those who commented here, on Twitter, and on our Facebook group pointing us to additional devices to add!)

Adidas has two devices:
Fit Smart
Sensors: Accelerometer, Heart Rate (optical)
Smart Run
Sensors: GPS, Accelerometer, Heart Rate (optical)

Angel
Sensors: Accelerometer, Heart Rate (optical), Blood Oxygen, Temperature

Amiigo
Sensors: Accelerometer, Pulse Oximeter, Temperature

Apple Watch
Sensors: Accelerometer, Gyroscope, Heart Rate (optical)

Asus ZenWatch
Sensors: Materials state the ZenWatch houses a “bio sensors and 9-axis sensor.” I assume optical heart rate, accelerometer, and gyroscope.

Atlas
Sensors: Accelerometer, Gyroscope, Heart Rate (optical)

Basis
Sensors: Heart Rate (optical), Accelerometer, Perspiration, Skin Temperature.
(Note: Intel & Basis today also announced the new Basis Peak to be released this year.)

DigiCare ERI
Sensors: Accelerometer, Temperature, Pressure

Epson Pulsense Band/Watch
Sensors: Accelerometer, Heart Rate (optical)

Fatigue Science Readiband
Sensors: Unknown

Fitbit Flex
Sensor: Accelerometer
Continue reading

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Apple Announcement: QS Links, Comment, and Background

Here at QS Labs we’re very interested to see what Apple will be announcing today. The following post with reactions and discussion by Gary Wolf & Ernesto Ramirez will be updated as we learn more.

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Access Matters

Someday, you will have a question about yourself that impels you to take a look at some of your own data. It may be data about your activity, your spending at the grocery store, what medicines you’ve taken, where you’ve driven your car. And when you go to access your data, to analyze it or share it with somebody who can help you think about it, you’ll discover…

You can’t.

Your data, which you may have been collecting for months or years using some app or service that you found affordable, appealing, and useful, will be locked up inside this service and inaccessible to any further questions you want to ask it. You have no legal right to this data. Nor is there even an informal ethical consensus in favor of offering ordinary users access to their data. In many cases, commercial tools for self-tracking and self-measurement manifest an almost complete disinterest in access, as demonstrated by a lack of data export capabilities, hidden or buried methods for obtaining access, or no mention of data access rights or opportunities in the terms of service and privacy policy.

Now is the time to work hard to insure that the data we collect about ourselves using any kind of commercial, noncommercial, medical, or social service ought to be accessible to ourselves, as well as to our families, caregivers, and collaborators, in common formats using convenient protocols. In service to this aim, we’ve decided to work on a campaign for access, dedicated to helping people who are seeking access to their data by telling their stories and organizing in their support. Although QS Labs is a very small organization, we hope that our contribution, combined with the work of many others, will eventually make data access an acknowledged right.

The inspiration for this work comes from the pioneering self-trackers and access advocates who joined us last April in San Diego for a “QS Public Health Symposium.” Thanks to funding support from the Robert Wood Johnson Foundation, and program support from the US Department of Health And Human Services, Office of the CTO, and The Qualcomm Institute at Calit2, we convened 100 researchers, QS toolmakers, policy makers, and science leaders to discuss how to improve access to self-collected data for personal and public benefit.  During our year-long investigation leading up to the meeting, we learned to see the connection between data access and public health research in a new light.

If yesterday’s research subjects were production factors in a scientist’s workshop; and if today’s participants are – ideally – fully informed volunteers with interests worthy of protection; then, the spread of self-tracking tools and practices opens the possibility of a new type of relationship in which research participants contribute valuable craft knowledge, vital personal questions, and intellectual leadership along with their data.

We have shared our lessons from this symposium in a full, in-depth report from the symposium, including links to videos of all the talks, and a list of attendees. We hope you find it useful. In particular, we hope you will share your own access story. Have you tried to use your personal data for personal reasons and faced access barriers? We want to hear about it.

You can tweet using the hashtag #qsaccess, send an email to labs@quantifiedself.com, or post to your own blog and send us a link. We want to hear from you.

The key finding in our report is that the solution to access to self-collected data for personal and public benefit hinges on individual access to our own data. The ability to download, copy, transfer, and store our own data allows us to initiate collaboration with peers, caregivers, and researchers on a voluntary and equitable basis. We recognize that access means more than merely “having a copy” of our data. Skills, resources, and access to knowledge are also important. But without individual access, we can’t even begin. Let’s get started now.

An extract from the QSPH symposium report

[A]ccess means more than simply being able to acquire a copy of relevant data sets. The purpose of access to data is to learn. When researchers and self-trackers think about self-collected data, they interpret access to mean “Can the data be used in my own context?” Self-collected data will change public health research because it ties science to the personal context in which the data originates. Public health research will change self-tracking practices by connecting personal questions to civic concerns and by offering novel techniques of analysis and understanding. Researchers using self-collected data, and self-trackers collaborating with researchers, are engaged in a new kind of skillful practice that blurs the line between scientists and participants… and improving access to self-collected data for personal and public benefit means broadly advancing this practice.

Download the QSPH Report here.

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Diabetes, Metabolism, and the Quantified Self

dougkanter2

This is a visualization of one month of my blood sugar readings from October 2012. I see that my control was generally good, with high blood sugars happening most often around midnight (at the top of the circle). -Doug Kanter

Richard Bernstein, an engineer with diabetes, pioneered home blood glucose monitoring. What he learned about himself contradicted the medical doctrine of his day, but Bernstein went on to become an MD himself, and established a thriving practice completely devoted to helping others with diabetes. We think of Dr. Bernstein as a hero because he used self-measurement to support his own learning, and shared what he learned for general benefit.

Tracking personal metabolism is a necessity for diabetics, and it is also something that will become increasingly common for many people who want to understand and improve their metabolism. Diabetics are also leading the fight for personal access to personal data, and we’re looking forward to meeting inspiring activists and toolmakers today at the DiabetesMine D-Data Exchange meeting in San Francisco. In honor of this meeting, we’ve put together an anthology of sort of QS Show&Tell talks about diabetes and metabolism data.

Jana Beck
Jana is a Type 1 diabetic and data visualization practitioner who has been working on creating new techniques for understanding that data from her Dexcom continuous blood glucose monitor. In this talk, she described some of her newest techniques and her ongoing work with Tidepool.org. You can also view her original QS show&tell talk here.

Doug Kanter
Doug has been featured here on the QS website many times. We first learned about Doug through his amazing visualizations of his own data (like the image above). At the 2013 QS Global Conference, Doug shared what he learned from tracking his diabetes, diet, activity, and other personal data and his ongoing work with the Databetes project.

We spoke with Doug about his experience with tracking, visualizing and understanding his diabetes data. You can listen to that below.

James Stout
James is a graduate student, professional cyclist, and a Type 1 diabetic. In this talk at the QS San Diego meetup group he talked a bit about how he manages his diabetes along with his near super human exercise schedule and how he uses his experience to inspire others. (Check out this great article he wrote for Ride Magazine.)

Brooks Kincaid
Brooks, a Type 1 diabetic, was tracking his blood glucose manually for years before switching to a continuous blood glucose meter. In this talk he describes what he’s learned from his data and why he prefers a modal day view.

Bob Troia
Bob tracked his fasting blood glucose, diet, and activity to find out what could help him lower his risk of developing type 2 diabetes.

Vivienne Ming
Vivienne’s son was diagnosed with Type 1 Diabetes two years ago and she’s applied her scientific and data analysis background to understand her son’s life.

Seth Roberts
Seth has a long history of tracking and experimenting with his metabolic data. In one of his last QS talks, he spoke about how alternate day fasting was impacting his blood sugar.

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