Ernesto Ramirez

Ernesto Ramirez
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QSXX Boston Meetup Recap

This post comes to us from Maggie Delano, an organizer for QSXX Boston- the Boston Women’s QS Meetup group

The QSXX Boston Chapter held our fifth meetup on March 3rd, 2014. We had a guest Amy Merrill from The Hormone Project talk about the direction of this new project. As a group, we discussed how something like the Hormone Project might be beneficial to us. We all agreed that it would be awesome if we could track our hormones at home, instantly, without going through a doctor (also, unicorns.) Relevant hormone research (if it exists at all) can be difficult to understand, and isn’t always driven by those it might have the biggest impact on. Concrete examples about the types of hormones and ways we can track these hormones would be very helpful.

We also talked about how sharing stories is a key aspect of QS, and how we might be able to facilitate further discussion around both hormones and QS in general. We discussed the potential benefits of not only being able to track our own data related to hormones, but also to see data from other people. While there are clear merits in sharing information, we also discussed the potential privacy implications therein. Some possibilities for collaboration between QSXX and The Hormone Project might include group tracking projects and/or “hormone dinner parties.” It was exciting to have The Hormone Project here for QSXX and we hope to have them attend a meetup again in the near future.

A lot of topics come up during each QSXX meetup. Here are a few concrete items that our group found interesting:

This article in Model View Culture on QS and feminism. We discussed how QSXX does and doesn’t address what the author is calling for here.

Glow. This is one example of a period tracking app. We discussed how most period tracking apps today are primarily fertility based, and it would be nice for new apps and tools to think more critically about what their users might want.

MetaMed and Patient-Centered Outcomes Research Institute . Two examples of organizations that might be models for future work on hormones and QS.

Bringing Back My Real Self With Hormones. An interesting article from the New York Times on the potential impacts of hormones on the self.

Reporter App. We talked about this (relatively) new app for “reporting” on your day, and the advantages and disadvantages of services that ping you to enter data and services that you use to manually enter data when you remember to.

AliveCor iPhone ECG. We discussed this cell phone ECG case, which is now available for purchase on Amazon.

Lift’s The Quantified Diet. We examined how this experiment is an interesting first step toward “Quantified Us” and how we might apply something similar for a group tracking experiment of our own.

Pact. This came up as we were discussing motivations for tracking and maintaining habits. In this case, you can earn money when you reach your goals

The next QSXX Boston meetup will be held early this Summer. Stay tuned!

 

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What We Are Reading

Click, read, enjoy!

Articles and Posts

Meet the Teams Who are Building the World’s First Medical Tricorder by George Dvorksky. We’ve been following the Qualcomm Tricorder XPRIZE since it was announced. Now that only 10 teams remain it’s nice to get a feel for what some of the groups are working on. (Disclosure: Scanadu, one of the teams competing for the prize is a sponsor of QS. We are grateful for their support.)

How One Retailer’s Employees are Using Wearables by Andy Meek. Self-tracking technology is pushing into every corner of society. It’s no surprise that we’re seeing it being deployed in the the workplace. This is definitely something to keep an eye on and I look forward to more conversations about what it means to be “efficient and productive” at work.

The Great Discontent – Nicholas Felton by Ryan and Tina Essmaker. A great in-depth interview with designer, and personal data visualization specialist, Nicholas Felton.

I’m trying to lift the veil on the size, power, humanity, humor, and narrative potential of our data by making tools that allow people to leverage it.

What Your Activity Tracker Sees and Doesn’t See by Albert Sun and Alistair Dunt. If you’re wearing an activity tracker (Fitbit, Jawbone Up, Withings Pulse, etc.) this is a must read (and watch). The interactive elements do a great job of showing you how accelerometers work to translate movement data into information.

Me, My Quantified Self, and I by Kevin Nguyen. For some reason the release of the Reporter app has created a steady stream of philosophical explorations of what it means to track and understand “the self.” Add this to you reading list if you want to ask yourself, “Would David Hume use a Fitbit?”

Life through a camera by Carmen Pérez-Lanzac [SPANISH]. A fantastic exploration of the history and possible future of camera-based lifelogging.

Let’s get physical: Discovering data in the world around us by Anushka Patil. A nice post here recapping some of the work presented at the recent National Institute for Computer-Assisted Reporting conference. I especially enjoyed .

A DIY Artificial Pancreas System? Are we crazy? by Scott Leibrand & Dana Lewis. Some of the more technically minded people in the diabetes community are not waiting for the promised Artificial Pancreas Systems of the future and have set out to test and learn from a DIY solution. Absolutely amazing stuff here.

Data Analysis: The Hard Parts by Mikio L Braun. If you think machine learning is easy or the cure for your data analysis woes, think again.

Show & Tells

Generation ‘Y’ Can’t We Sleep by Scott Fetters. If you look beyond the title you’ll find a really nice example of someone practicing to try and find a way to get better sleep.

Visualization

An Introduction to D3 by Sam Selikoff. We’re huge fans of D3 here at QS Labs. This is a great place to start if you want to learn more about this powerful data visualization package.

From the Forum

Over Stimulation

Quantifying Relationships

Mapping your Location With Moves

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How to Map Your Moves Data

In the Quantified Self community we focus on projects and ideas that help people access and get meaning out their personal data, including the information you can collect with your smartphone. If you have an iPhone, Android, or Windows phone you’re already have carrying of the world’s most sophisticated self-tracking tools. The GPS, accelerometer, the microphone, all of these tiny sensors make up a great set of tools you can use to understand how you move around the world.

I’m going to focus this short “how to” on geolocation data and mapping your movement, specifically using data gathered by the Moves application. Moves is a passive activity and location tracking tool available for the iPhone and Android. We’ve written a bit about it in the past and had a chance to interview their CEO, Sampo Karjalainen. I’ve been using it since May, 2013 and I wanted to share some neat tools and methods for getting a bit more out of the data Moves collects.

I find that visualizing my data on a map to be incredibly powerful. It might by my inner cartographer, but seeing my patterns of movement (or lack there of) in reference to known places and landmarks is a great mechanism for inducing recall and reflection on where I’ve been and what I’ve done. Hopefully you’ll use one of the tools or methods below to map you data and learn something new!

Moves Connected Apps
Like many self-tracking applications and devices, Moves has a API that many different developers have built services on top of. Here are just a few of the services that allow you to see your data on a map. Be advised that each of these services has access to your data. Make sure to read their Terms of Service before agreeing to the data transfer.

WebTrack. This is by far the most utilitarian data mapping tool. However, you shouldn’t get discouraged by the lack of fancy design because it gives you an very unique data view. When you use Moves on your phone you typically only see the “storyline” and the detected places you’ve spent time at. However, Moves is constantly pinging and recording your location when it detects movement. WebTrack allows you to see all those movement points by hovering over the associated timestamp.

WebTrack_MovesMap

Fluxtream. You might know Fluxtream as Friend of QS and a great open-source data aggregation tool. They’ve set up a “Moves Connector” that allows you to import and visualize your Moves data. Because Fluxtream is set up as an aggregation and visualization tool you can also map other interesting data sets. Want to know where you were tweeting last week? Fluxtream will map it for you. (You can see me tweeting on a CalTrain ride between San Francisco and Palo Alto below.)

Fluxtream_MovesMap2

Zenobase. Another interesting data aggregation service here. Zenobase treats your Moves data bit differently. Rather than importing all the movement geolocation data it focuses on your place data and visualizes those locations. I like the high-level view it start with, but make sure to keep zooming in to see more specific place data.

Zenobase_MovesMap

Resvan Maps. This mapping application adds a unique twist to the typical mapping visualizations. It will plot your places, paths, and categorize paths depending on the activity (transport, walking, running, and cycling). Additionally, you can create “analysis cirlces” and have the application compute the time you spent in a certain location you bound (it aggregates to hours:minutes per day).

Resvan_MovesMap

MMapper. This method for mapping your data, developed by Nicholas Felton, is by far the most technical, but it produces some really neat visualizations. You’ll have to download Processing and follow the instructions Nicholas provides on the Github repository page here. The great thing here is that the mapping and data access is all happening locally.

MMapper_MovesMap

Map It Yourself!
If you don’t want to trust your data to a third party, but you still want to explore your movement maps there is really great option for you. Our friend and co-organizer of the QS LA Meetup, Eric Blue, recently published a method for easily exporting your data: the Moves CSV Exporter. You’ll have to login and use the Moves pin system in order to download your data, but Traqs isn’t storing your data, just providing a way for you to access it. The tool allows you to download and explore your activity, summary, tracks and place data. We’ll focus on the place data for creating maps. You can also use your full tracks history for mapping all the geolocation points Moves collects.

Because this data is based on latitude/longitude coordinates there are many different methods available that you can use to map your data. I’m going to focus on two here: Google Fusion Tables and CartoDB (if you know of others share them in the comments or our forum).

Google Fusion Tables
Fusion Tables are a new Google Drive tool that you can use to store, analyze, and visualize many different types of data. Once you download your Moves places.csv file you can upload it to a new Google Fusion Table. Once you upload your data, which takes about 2 minutes, you’ll see a menu bar and three tabs: Rows, Cards, Map of longitude. Just click on the “Map” tab and you’ll see your data already placed on a map. If you want to see a heatmap rather than a point map just navigate to Tools -> Change Map and you’ll see an option for a heatmap on the lefthand side. This is just the tip of iceberg for mapping fusion table data. You can learn more about different mapping methods and tricks here.

MovesPointMap
MovesHeatMap

CartoDB
CartoDB is a visualization and analysis engine for geospatial data. I’ve been using it to play around with a few of the different geolocation datasets that I have (I actively keep three). Although it is paid service, they do offer a free plan for smaller datasets, which is perfect for your Moves data. Again, you’ll have to upload your places.csv file to a new table once you set up your account. Once the data is uploaded there are quite a few different map visualization wizards you can use to view your data in different ways. Pesonaly I like playing with the “Torque” visualization that gives you a real feeling of space-time to your data.

CartoDB_MovesMap

TileMill
TileMill is an interactive map design tool from the folks over at Mapbox. If you’re looking to create custom maps with your data that you can format, style, and share then this is a wonderful tool to use. At first glance it’s a little daunting because it looks like a mashup of a CSS editor and map tool. That actually gives it the unique power to drive customization. Don’t be afraid, it’s not too hard to get started with. Mapbox has provided a great “crashcourse” to get you started with importing data, saving it as a new layer on your map, and then manipulating how it looks on your screen. If you want to go just a bit farther you can also add legends and informative popups to describe your data points. Mapbox also offers a free hosting plan if you want to share your interactive maps on a webpage. For example check out my MovesMap here, where I added a quick styling to manipulate the point size in relation to the time spent at a location.

TileMill_MovesMap

Hopefully you’ve learned something new from this. If you map your Moves data (or any other geolocation data) we want to see it! Leave a link in the comments, post it in the location mapping thread on the QS Forum or get in touch on twitter!

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Max Gotzler on Tracking Testosterone and Diet

Max Gotzler was smack dab in the middle of a long Berlin winter and he started experiencing reductions in this mood, energy levels, and sleep. After getting a blood test he found out he had low levels of vitamin D and testosterone (among other biomarkers). His prior reading and research led him to experimenting with his diet (primarily with carbohydrates). In this talk, filmed at the Berlin QS Meetup group, Max describes his diet experiments and the results he found over six months of tracking.

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The Quantified Self Institute

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

QSI logo

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

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

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

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

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Submit Your Quantified Self Research

QSEU14_small
We’ve been holding Quantified Self Conferences since 2011. Every year since then we’ve been approached by scientists and researchers in the academic community to help them find a way to incorporate their work and their ideas into our structure. After a few years of holding back, listening, and watching the research community become engaged with other scientists and the real-world QS practitioners we’re ready to take that next step.

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

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

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

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

How to submit a poster

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

Details

Posters should contain the following elements:

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

Format:

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

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

Dates & Deadlines

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

Submit your poster now!





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What We Are Reading

Another collection of thought-provoking items from around the web.

Articles & Posts

Plan to move from #quantified self to Qualified Self by Inga de Waard. Every now and then someone writes something that causes me to pump the brakes and really reflect on self-tracking and personal data collection. This is one of those time. Inga does a nice job here setting up her experience with self-tracking to understand her type 1 diabetes. She moves on to explore how “qualified data” might be a better source of information for personal growth, “I am more than my body, I am mind. So I want to understand more.”

The Bracelet of Neelie Kroes (in German) by Frank Schirrmacher. Can machines be trusted? Are we building and willingly wearing the handcuffs of the future by strapping tracking devices to our wrists? These questions are explored in this article. (If you’re like me you are probably wondering who Neelie Kroes is. Here’s some background info.)

Biggest Gene Sequence project to launch by Bradley J. Fikes and Gary Robbins. J. Craig Venter is at it again. Now that genome sequencing has passed the $1000 barrier he has set up a new company in order to recruit and sequence 40,000 people per year.

This Mediated Life by Christopher Butler. Another amazing piece of self-reflection spawned by the recently released Reporter App. Rather than reviewing the application, the author addresses what it means to self-track when we know we are our own observer. Do we bias our reflection and data submission when we know that each answer, each data point is being collected into a larger set? (This post reminded me of one of my favorite movie lines, “How am I not myself.” from I Heart Huckabees

The Open Collar Project. At a recent meeting I learned of this project to create an open-source dog tracking collar. Pet trackers are becoming more prevalent in the market, but the purpose of this project goes far beyond just understanding pet activity. I learned from the lead researcher, Kevin Lhoste, that they’re using this as a method to encourage and engage children in science and mathematics. Very neat stuff.

Twitch Crowdsourcing: Crowd Contributions in Short Bursts of Time [PDF] by Rajan Vaish, Keith Wyngarden, Jingshu Chen, Brandon Cheung, and Michael S. Bernstein. This research paper describes the results of a really interesting project to gather information from people using micro-transactions during the phone unlocking process. It appears that we can learn a lot from people in under 2 seconds.

The Open FDA. Not an article here, but I wanted to call attention to the new open initiative by the FDA. This new effort was spearheaded by Presidential Innovation Fellow, Sean Herron. If you’re interested in doing this type of work you can apply to be a fellow here.

Show&Tells (a selection of first person stories on self-tracking and personal data)

200 days of stats: My QS experience by Octavian Logigan. Octavian recounts the various data he’s collected including activity, sleep, email behavior, and work productivity. I really like how he clearly explains what tools he’s using.

A Year in Diabetes Data by Doug Kanter. We’ve featured Doug here on the blog before. From his amazing visualizations to his talks about his process, we’ve been consitently impressed and inspired by this work. In this post Doug recounts 2012 – “[...] the healthiest year of my life.” (Full disclosure: Doug sent me the poster version of his data and it is beautiful.)

Visualization

timkim_map
This visualization comes to us from Tim Kim, a design student based in Los Angeles.

The map shows different collections and documentations made during my cross country trip. Posts made during the trip on various social media sites are orientated and placed by the geological locations. The states are elongated by purely how I felt about the duration of going across the specific state. For example, driving through texas sucked (no offense). Different facts are layered and collaged across the map to create and express a collective, over-all image of the trip. Some quantifiable information, some quantitative information to create a psych-geolocal map.

Thumbs Up Viz A really nice website that highlights and explains the good pieces of data visualization popping up all over the web these days.

From the Forum

Tracking emotional experience
Test our a new app for sleep improvement
Measuring emotions through vital signs

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Mark Drangsholt on Understanding His Heart Rhythm Disorder

Mark Drangsholt has been dealing with an issue with his heart since he was a young man. Since his early twenties, when he as diagnosed with paroxysmal atrial tachycardia he’s had to deal with irregular heart rhythms. In this talk Mark explains how the transition into adulthood negatively impacted his health and then how he used self-tracking and a focused athletic program to help him reduce his weight and improve his health. Most show&tell talks would end there, but Mark still had the irregular rhythm issue to deal with. After what he describes as an episode that made him think, “This is it. I’m going to die.” he decided it was time to apply his self-tracking process in order to understand his heart rhythm disorder and possible triggers. Mark also decided to go one step further and apply the principles of case-crossover design to his tracking methodology. Watch his talk below and keep reading to learn a bit more about why you might want to consider using case-crossover design in your self-tracking projects and experiments.

The following excerpt from the QS Primer: Case-Crossover Design by Gary Wolf provides a great background for his method:

Mark’s self-tracking data didn’t naturally fit with any of these approaches. To understand whether these triggers actually had an effect on his arrhythmias, he used a special technique originally proposed by the epidemiologists Murray Mittleman and K. Malcolm Maclure. A case-crossover design is a scientific way to answer the question: “Was the patient doing anything unusual just before the onset of the disease?” It is a design that compares the exposure to a certain agent during the interval when the event does not occur to the exposure during the interval when the event occurs.

Using this method, Mark discovered that events linked to his attacks included high intensity exercise, afternoon caffeine, public speaking to large groups, and inadequate sleep on the previous night. While these were not surprising discoveries, it was interesting to him to be able to rigorously analyze them, and see his intuition supported by evidence. “A citizen scientist isn’t even on the conventional evidence pyramid,” Mark notes. “But you can structure a single subject design to raise the level of evidence and it will be more convincing.”

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

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

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

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

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

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What We Are Reading

An extra long list for you to this time. Enjoy!

Articles & Posts

Beyond the Data Portal by Jed Sundwall. The open data refrain has been taken up by non-profits, local, and national governments around the world. Have we questioned what it really means to be good data stewards? A very nice post here that opens up a discussion about the role of data librarians to augment simple access with human-powered information wayfinding.

Could Behavioral Medicine Lead the Web Data Revolution? by John W. Ayers, Benjamin M. Althouse, and Mark Dredz. If you can look past the slightly antiquated use of the term “web data” here you’ll see a good critique of the current methods in behavioral health science and the role of personal data in medical and behavioral research.

Little data: Tracking your life through numbers by Dominic Smith. A nice short piece here on the art behind self-tracking,

Critics might ask why we should care about the aggregated, daily routine of a man most of us will never never meet. But fans would argue that these reports aren’t merely novelties for the coffee table—they represent data as art, a single year of human life parsed into graphs and charts.

Fitness Trackers Could Boost Kids’ Health, But Face Challenges, Experts Say by Tia Ghose. Activity trackers are all the rage these days, but can they be used to track and understand children’s physical activity?

Questioning the Quantified Self as it Marches Towards Mainstream by Matt Stempeck. A very thorough recap of a talk by Natasha Dow Shull given at the MIT Media Lab. It covers the history of self-tracking and the current trend towards algorithmic selfhood. Great read.

When quantified-self apps leave you with more questions than answers by Brendan O’Connor. The author takes at self-tracking and personal data through the lens of the newly released Reporter app. Reading this piece left me wondering, are questions the prominent artifact of a self-tracking practice?

Dan Hon’s Newsletter By Dan Hon. I know you get enough email already, but this is an exceptional project by Dan to express his ideas in the form of a daily newsletter. Covering the vast arena of techno-culture, it’s a great addition to my inbox. See his thoughts on Quantified Self in issue #15.

Why It’s OK to Let Apps Make You a Better Person by Evan Selinger. The ideas and considerations in this piece are as relevant today as they were when this article was published nearly two years ago.

Quantify Everything: A Dream of a Feminist Data Future by Amelia Abreu. A very interesting perspective on self-tracking and the Quantified Self movement by our friends at Model View Culture.

The Ethicist’s and the Lawyer’s New Clothes by I. Glenn Cohen [video]. An interesting lecture on the ethical issues surrounding the use and misuse of “smart clothing.”

Data Sharing Essay Competition by DNA Digest. A writing competition to explore themes around the positives and negatives of data sharing in the health research community.

Show&Tells (a selection of first person stories on self-tracking and personal data)

Quantify Yourself by Amo Utrankar. What happens when a medical student starts self-tracking so he can understand his future patients?

Between Week 1 and Week 4, my “compliance” fell from 96% to 63%. It takes a committed, conscious effort to record every meal, every vital sign, every exercise, every minute of the day. I hold a new-found respect for the diabetic patient who has to monitor his blood sugar, manage his appointments, and mind his meals; it’s a process that’s both distracting and exhausting.

I tracked every penny I spent for one year. Here’s what I learnt. by Todd Green. Ten lessons learned from a year-long meticulous tracking project.

I lost 1,000 hours of sleep in 1 year: My story as entrepreneur & new Dad by Nick De Mey. A father recounts his process of learning about his sleep, or lack thereof. (Editor’s note: Nick is a founding member of AddApp, a Friend of QS).

Visualizations

Screen Shot 2014-03-01 at 1.49.59 PMMy Facebook Messaging History by Person and Time. A great visualization and conversation with open source code so you can make your own.

 

 

 

 

Screen Shot 2014-03-01 at 1.50.20 PM

My Recent Exercise Log – Plotted. Another reddit user shares his exercise data from MyFitnessPal.

 

 

 

 

AekSBmnWhat can you learn from almost 3 years of Skype chat logs?. A simple, but nice word cloud visualization of chat logs.

In total there were 280114 words sent. Words that refer to oneself (such as: i, me, ich, my, mich, min, meiner, meine, meins, jag, mig, mir) were used 14995 times whereas words that refer to other people (like above list for others) were only used 6669 times! People in my Skype conversations like to talk about themselves… (which is mostly me. THERE, I did it again )

 

posegrid_ny_Selfiecity. An interesting exploration of new media visualization techniques and social media information processing by an outstanding group of researchers. Take a tour of the website then read Lev Monivich’s post about this new area of research and data visualization.

 

 

 

From the Forum

OPI Truesense for Sleep Tracking

Samsung Gear Fit

Tracking Pain/Discomfort – Thoughts?

Statistical Findings

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