Search Results for: sleep

Meetups This Week

There are three very interesting QS meetups occurring this week. Chicago’s event will be fitness focused, with talks on what it’s like to work out with a weight system that changes it’s resistance in real-time based on your performance and effort and learning from DXA body composition data. Shanghai will have a researcher talk from Preston Estep on using genetic data to improve health.

Ashland will have an amazing sharing of progress on current n=1 projects. Projects include exploring deep sleep with Beddit, looking at the difference between breath-based and blood-based ketone readings, and testing the effects of berberine on postprandial glucose rise. The last one is interesting is because it is placebo-controlled and double-blind, which can be difficult to pull off. I would love to hear more about his experiment design.

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!

Monday, July 20
Chicago, Illinois

Tuesday, July 21
Shanghai, China

Sunday, July 26
Ashland, Oregon

Photo from QS Montreal’s meetup last week

What a beautiful venue. If you organize a QS meetup, please post pictures of your event to the Meetup website. We love seeing them.

QSMontrealJuly
Photo credit: Maxime Chabot

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What We Are Reading

Enjoy this week’s list!

Articles

Big Data for the Spirit by Casey N. Cep. Interesting piece here on SoulPulse, a study using text messages to examine spirituality. Can faith be measured and quantified? These researchers are trying to find out.

Big Data Not Doping: How The U.S. Olympic Women’s Cycling Team Competes On Analytics by Bernard Marr. Nice short article on Sky Christopherson and the personal data-driven training program that resulted in a silver medal at the 2012 Olympics for the Women’s track cycling team.

The Quantified Cow: Wearables Will Monitor Animals As Closely As Humans by Ben Schiller. First we put sensors on ourselves. Then we started putting them on our pets. Now, we’re working on putting them on our cattle. What’s next?

Show&Tell

stepCountingQuantified Self: Step Counting by Chad Lagore. Chad wrote up a great analysis of what he learned from analyzing step data natively tracked through his iPhone. Of course, special kudos to him for using our QS Access app to download his data.

Visualizations

JobsVisualizationWhere Are the Jobs? by Robert Manduca. Robert took data from the Census Bureau’s Longitudinal Employer-Household Dynamics dataset and visualized each job as a dot on the map. Fascinating to see where different industries cluster around the United States.

OkavengoHRHippoHippo Attack! by Jer Thorp. Ever wonder what happens when you’re attacked by a hippopotamus? Above is the plot of Dr. Steve Boyes’ heart rate during the attack. Make sure to click through for an amazing account of the event.

From the Forum

Resting Heart Rate Tracking
REMzen Sleep Tracking
Basis Peak

This Week on QuantifiedSelf.com

Mark Moschel: Parasites and Gut Repair
Steven Zhang: Concussions, Headaches and the Whole30 Elimination Diet
2014 QS Visualization Gallery: Part 2
QS Radio: Episode #3

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2015 QS Visualization Gallery: Part 2

We’re back again with another round of visualizations from our QS15 Conference and Expo attendees. In today’s batch you’ll see a variety of representations of different tracking projects, from tracking biometrics while watching a movie to running distance over nearly 13 years. Enjoy!

interstellar-hr-hrv-gsr-1024x635 Name: Bob Troia
Description: I tracked my heart rate, HRV, and galvanic skin response while watching the movie Interstellar (in IMAX!), then plotted the data to understand how my body reacted during the 3+ hour movie. (Check Bob’s blog post about this data here!)
Tools: Polar H7 chest strap, SweeBeat Life app (iPhone), Basis B1 band, Excel.

 

Sleep for a week Name: Tahl Milburn
Description: This shows sleep over a week. The overall height of the bar is the time in bed. The part above the baseline is actual sleep whereas the part below 0 is restless sleep or awakening during the night. The line above the bars is the goal number of hours. The bar itself is green is all okay, turns yellow if overall duration is short or awakened too much. Red is even worse.
Tools: Google Charts with data from Fitbit.

 

LifeGauge Name: Tahl Milburn
Description: This is a very simple but powerful chart. T his is a “Life Gauge” which show how much of my statistical life has already been used. The ultimate age is based on the consensus estimate from several sources. Note the yellow and red markings indicating that one might be running out of life soon.
Tools: Google Charts for the graph itself. Several sources for computing the ultimate age.

 

BigGraph Name: Julie Price
Description: My running miles per week plus marathons since 2002.
Tools: Tracked running miles using various methods and recorded both on paper and, in the past few years, on a Google sheet. Summarized & graphed in Excel before manually adding in marathons.

 

IMG_8244 Name: Allan Caeg
Description: ”How much did you win today?” is one of the most important questions I ask myself every day. This pre-sleep question constantly gets me to reflect on what I did with my free will, inspiring me to ensure that I’d make the most out of every day.
Tools: Reporter

Stay tuned here for more QS Gallery visualizations in the coming weeks. If you’ve learned something that you are willing to share from seeing your own data in a chart or a graph, please send it along. We’d love to see more!

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Steven Zhang: Concussions, Headaches and the Whole30 Elimination Diet

StevenZhangViz

 

At every conference, a synchronicity will occur where a few talks cover a similar, but previously unexplored topic. At QS15, we were surprised to see an increased discussion of concussions. It’s hard to know whether this is due to random chance or a glimmer of the zeitgeist, but we like to take note of these little waves of how people are finding new ways to understand themselves, or in this case, overcome strife.

Though Steven Zhang had a history of sleepiness and headaches, he never tracked them prior to his concussion. But during his recovery from post-concussion syndrome (which worsened his sleepiness and headaches), he wanted a clear record of his progress. He tracked headaches using the Tap Log android app and tracked his sleep using Sleep As Android, manually logging in and out in the app as he prepared for or woke from sleep. That he naps often and has many unsuccessful attempts to sleep meant that automatic methods for tracking sleep, like wrist-worn activity trackers, were ineffective, an important lesson considering that good sleep data is still sought after by many in the QS community.

Visualizing his data in Tableau, he gained a sense of norms for his headache frequency and nap lengths, allowing him to test the effectiveness of a dietary intervention, the fascinating result of which you can watch in the video of his talk below:

Steven presented this talk last month at the QS Global Conference in San Francisco. To see more great talks like this, you should join us at our Europe Conference on September 18 and 19th in Amsterdam. We have a limited number of early bird tickets available, so make sure that you don’t miss out by registering!

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What We Are Reading

Hello again! Here we are with another list of articles, links, and visualizations for you. Enjoy!

QS15 Reactions
We’ve started to see a few great blog posts and articles describing the experience of attending the QS15 Conference and Expo. For the next few weeks we’ll be highlight a few here.

My Data, Your Data, Our Data by Murray Grigo-McMahon
Notes from the 2015 Quantified Self Conference by Arpit Mathur
Quantified Self 2015 by Phoebe V. Moore
QS15: Measurement with Meaning by Ben Bending

Articles

The Future of Food Data: Toward Transparency, Personalized Design, & Re-Thinking the Concept of a ‘Food Label’ by Sam Slover. We highlighted Sam’s work on visualizing his food last year and it nice to see that work is continuing. I’m interested to see where this goes.

An Evening with the Consciousness Hackers by Nellie Bowles. Brain tracking and augmentation is definitely on the rise. Great to see the Consciousness Hacking group get some attention. (We were honored to have Mikey Siegel and Ariel Garten participate at the QS15 Conference and Expo. Look for their talk soon!)

Make people the controllers of their data to help the NHS go digital by Andrew Chitty.

There’s a solution to this too. Make it the default assumption that the patient is the owner or controller of all data relating to them. They can then share this data with whichever parts of the health service they wish.

This might sound slightly outlandish but think about it: we’re increasingly going to see digitized records become the norm, with many of them self-generated by citizens as part of their self-care – which we want to encourage, not only because it engages people with their own care but because it short circuits the technical barriers around information sharing.

What if We Really Set Data Free by Elizabeth Nelson. I had the pleasure of speaking at length with Elizabeth about Quantified Self, data, and data access. Make sure to also check out this great interview with Josh Berson.

The Crying Baby and the Sympathetic Fitbit by Jocelyn Wiener. A great article by a mother with a new baby who learned how sleep tracking can be useful.

My sleep didn’t get any better just because Fitbit started quantifying how crappy it was. But I felt validated, if only by someone with a rechargeable battery for a heart. While I received plenty of clucking sympathy from family and friends, my new device gave me something arguably better: evidence.

Show&Tell
Basis_Sleep Is drunk sleep less restful than sober sleep? How much so? Why or why not? by Justin Lawler. Not sure where I saw this, probably in the #quantifiedself stream on Twitter, but this Quora answer is pretty fantastic. Justin takes the time to explain what he found when he ran a test on how alcohol affected his sleep using his Basis watch.

Quantified home birth by Morris Villarroel. A beautiful post by our friend Morris, who describes his tracking experience during the day his son was born.

Visualizations

New-image-web-FCP Food Chain Project by Itamar Gilboa.

The Israeli-Dutch artist kept a diary of everything he ate and drank for the duration of a year. He meticulously kept track of his daily consumption. Some three years later, the results can be seen in a sculpture installation, the Food Chain Project. His installation, a traveling pop-up supermarket consisting of more than 8,000 white plaster sculptural groceries, physically represents Gilboa’s yearly consumption.

 

2014_running From a Net to a Harpoon: 2014 Annual Review by Michael Anthony. I cannot stress how beautiful this annual review is. Maybe it’s the focus on running that gets to me, but the whole this is worth looking through. You can even go back in time and view Michael’s reports from 20112012, and 2013.

From the Forum
Quantifying Caloric Intake
How to Quantify Myself

This week on QuantifiedSelf.com
2015 QS Visualization Gallery: Part 1
2015 QS Europe Conference: Scholarship Application Now Open

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Meetups This Week

After a brief break to focus on the QS15 conference, Meetups This Week is back. So many of our best talks at the conference were first done at a local meetup, so it’s with great pleasure that I get to highlight all of the great QS groups that are getting together around the world.

Groups like Montreal, who will feature a talk by Francois-Joseph Lapointe on how his microbiome changed in response to sexual activity. They will also discuss how the Muse can help with meditation.

Manchester will discuss how “what to do with all that QS data” and Indianapolis will have a research talk on sleep with Dr. Steve Green.

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!

Monday, July 6
Oxford, England
Manchester, England

Tuesday, July 7
Reno, Nevada
Montreal, Canada

Saturday, July 11
Indianapolis, Indiana

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What We Are Reading

After a few weeks of we’re back for another round of What We’re Reading. As you may know, we just wrapped on amazing three day conference and expo in San Francisco. Thank you to all that came, participated, and helped make QS15 such a wonderful experience!

Couldn’t make it to QS15? You’re in luck! We just announced our fourth Quantified Self Europe Conference. Join us in Amsterdam for an intimate and engaging event. You don’t want to miss it! Early bird tickets are on sale now.

Now, on with the show!

QS15 Reactions

We’ve started to see a few great blog posts and articles describing the experience of attending the QS15 Conference and Expo. For the next few weeks we’ll be highlight a few here.

Quantified Self ’15 Day 1 Recap by Tim Hanrahan
Quantified Self Expo, Part 1 by Karl Etzel.
What you can learn from the 2015 Quantified-Self Conference by Guillaume Tourneur
What I learned at Quantified Self 2015 by Richard Sprague

Articles

Is Direct Access to Lab Results Helpful or Harmful? by Patricia Salber. Patricia updates a post, first written in 2011, about the pros and cons associated with having direct access to medical testing and lab results.

Jaguar wants to monitor its drivers’ brainwaves, heart rate, and breathing by Jacob Kastrenakes. Sounds a bit far-fetched, and we may never see this research project in our cars, but I was intrigued by this:

But Jaguar says that it should be able to monitor for brainwaves through sensors embedded in the steering wheel. It’s apparently looking into adapting tech that’s already used by NASA to monitor pilots’ concentration.

Sounds interesting, but I’m also left I’m also left wondering if it will be worth measuring my concentration when the cars of the future will be driving themselves!

Google Reveals Health-Tracking Wristband by Caroline Chen and Brian Womack. Interesting to see that Google X is getting into the wearables game. Anyone know the difference between this device and other similar tools like Basis?

Biggest winner of the Finals? Rest! by Tom Haberstroh. What helped the Golden State Warriors have one of the best seasons in NBA history and capture the championship? Quantified Self of course!

They Warriors are as nerdy as it gets. As clients of wearable technology provider Catapult Sports, they monitor their players’ workloads in practice with GPS monitors and analyze the data with acute attention to maximizing performance while minimizing injury risk.

Show&Tell
Sorry_RobinW
Sorry by Robin Weis. A fascinating and beautifully articulated exploration into apologies between Robin and her parter. (Note: This was first posted by Robin on our Quantified Self Facebook Group. Join us forfor some great conversation!)

TimeTracking_MelanieP
How Tracking What I Do Every Day Helped Me Find Better Work-Life Balance by Melanie Pinola.

Most importantly, time tracking has helped me think more clearly about how I spend my time. I can see at a glance where I’m spending too much time in one area and not enough in others and also find patterns in my behavior.

Visualizations

TimeSleep
Find Out How Much Less Sleep You’re Getting Than Everyone Else by Dave Johnson and Alexander Ho. Time and Withings paired up to create some great interactive visualizations on sleep.

RyanB_appswitch
Visualizing “Productivity” with Elasticsearch, Logstash and D3 by Ryan Brink. Ryan wanted to get “look into my life at the keyboard” so he decided to gather some data and use D3 to visualize it. Click for the graphs, stay for the in-depth how-to explanation.

JawboneMood
What Makes People Happy? We Have the Data. by Sukrit Mohan. Jawbone takes a peak into their data to see what impacts mood. Above we see “a clear relationship between steps on the previous day and the mood of the user in the morning: better moods correlate with more steps.”

From the Forum

Food choice motivation experiment – Looking for a few people
Recommendation for Unique HR Monitoring Situation
Looking for the Zeo app for iPhone

QuantifiedSelf.com

QS15: What Happened?
Comparing Apple Watch and Fitbit One for Step Tracking
Announcing the 2015 Quantified Self Europe Conference
Anand Sharma: Aprilzero, Gyroscope, and Me

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Comparing Apple Watch and Fitbit One for Step Tracking

ApplevFitbit

When the Apple Watch was announced I started waiting with bated breath to see how it could be useful for Quantified Self and self-tracking purposes. Of course this means staying up late and making sure I had one on order as soon as possible. I put in my order shortly after midnight on launch day for a 42mm Space Gray with the black sport band.

On May 19th my Apple Watch arrived, coincidentally just after we wrapped on our first Bay Area Apple Watch Users Group meeting (which was fantastic and I highly recommend joining). I set it up and started figuring out how it worked as an activity tracker. I have a keen interest in activity tracking, not just as a self-tracker, but also as a graduate student studying how people use activity tracker data to understand and impact their lives. In that vein, I’ve been a consistent Fitbit user for over four years, transitioning from the original Fitbit to the Ultra, and then to my current Fitbit One. I’m a big fan of the Fitbit and use it as my personal “gold standard” for activity tracking. It’s accurate, consistent, and easy to use. Does that hold true for the Apple Watch? Let’s find out.

What did I do?

I wore my Apple Watch every day, from the moment I woke up to when I went to sleep at night. I set up my charging station on my nightstand, which is also where my Fitbit One spends its nights. I wasn’t thinking about this data analysis when I first started wearing the watch, but looking back over the past month I am confident saying that if I was wearing my Fitbit I was also wearing the watch.

This data analysis includes data from May 20th to June 23rd, or 35 days of data collection. My activities varied as a normal function of my work and life, meaning I didn’t purposefully mix things up or engage in activities just for testing purposes. Many days were sedentary, some days had longer walking periods, and in the 35 days I ran seven times at distances between four and nine miles.

How did I do it?

Exporting the data from both the Fitbit and the Apple Watch is not a trivial task, but thanks to a few pieces of software I was able to access and analyze both data sets.

Apple Watch
The Apple Watch stores the data it collects in Apple’s Health app using Healthkit. A quick glance into the Health app indicates that it is storing minute-level step data from the Apple Watch. Apple built in a data export function for the Health app, but it’s in a proprietary XML format that I’m not super familiar with. Thankfully there is QS Access. Our team at QS Labs created simple app that connects to Apple Health and allows you to export your data in a easy to use .csv file.

To export my data I first made sure that the Apple Watch had the highest priority for the data sources that feed the “steps” data for Apple Health. This is important because all newer iPhones (5s, 6, 6+) also natively create step data and store it in the Health app. I then used QS Access to create a data export for steps. I chose the hourly function as it’s the highest level of granularity the QS Access app currently offers for data export.

Fitbit
Fitbit recently introduced a data export feature. While this is a great step forward for them, and for their millions of users, the export feature is a bit limited. You can only export daily aggregate data and only one month of data is exportable at a time. Since I had access to hourly data from the Apple Watch I wanted to match that granularity.

I turned to my good friend, and past colleague, Aaron Coleman. Aaron runs a unique startup called Fitabase, which was built to help researchers, organizations, and individuals get easy access to activity tracker data. I spun up my account at Fitabase, which has been collecting and storing my Fitbit data for the last few years, chose the date range and downloaded my hourly step data.

I wanted to get right to my core question, “How accurate is the Apple Watch compared to the Fitbit One?” so I imported both data files into Google Spreadsheets, did a bit of data formatting, created a pivot table, then made some simple graphs. The full data set is available here if you want explore more complex statistics or visualizations.

What Did I Learn?

When compared to the Fitbit One, the Apple Watch is fairly accurate for step tracking. What do I mean by fairly accurate? Let’s dive into the data.

Daily Steps

When I explored my daily step totals it appeared that the Apple Watch counts more steps than my Fitbit One, but not that many more. Here’s the data you need to know:

  • Fitbit Total Steps: 308,955
  • Apple Watch Total Steps: 317,971
  • >Difference: 9,016 or 2.91% of the total steps (counted by Fitbit)

image-5
I created a difference category by subtracting Fitbit steps from Apple Watch steps for each day. This allowed me to see how different the data was day over day. The mean difference indicated that Apple Watch counted 258 steps more per day on average. Important to note that the daily difference was highly variable with a standard deviation of 516 steps. Looking at the scatterplot and histogram below you can see a few clear outliers, but what appears to be an otherwise normal(ish) distribution for the difference in step counts.

image-8

image-9

Hourly Steps
What about when we look at a higher level of granularity? I also explored the hourly steps data and compared the Fitbit and Apple Watch. On average the Apple Watch counted 11 more steps per hour than the Fitbit One during this period. Again, this was highly variable with a standard deviation of 85 steps, and a range from overcounting by 462 steps to undercounting by 696 steps. I haven’t yet filtered out sleep time (0 steps) so the mean difference per hour in this data set is likely skewed low.

image-12

image-13

I also looked into one more question that I though was interesting. Is there a significant difference in daily or hourly step data as a function of the total steps? Or, more simply, when I’m more active does the Apple Watch still stay consistent?

It appears that being more active doesn’t have a significant impact on how accurate the Apple Watch is tracking and counting steps. I created scatterplots for this relationship and added a simple linear trendline. In both cases, the trendline indicated that only a small amount of variability in the difference between the devices was accounted for by the total steps taken.

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So What?

I’m not ready to give up my Fitbit just yet, but I was happy to see that the Apple Watch is an accurate step tracking device. Of course there are caveats to this data set. It’s somewhat small, a little over a month of data, and I didn’t do any “ground truth” testing where I counted my actual steps. However, I feel more confident now that whether I’m walking around my apartment, my nieghborhood, or going on runs, the Apple Watch will accurately reflect those activities.

What’s Next?

Like most other runners who are using the Apple Watch I’m interested to dive into the heart rate data to test it’s accuracy. I’ve already collected a few runs, but will doing a bit more testing to compare to other common heart rate trackers.

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A Computer on Your Finger: OURA at QS15

One June 18-20 we’re hosting ourQS15 Conference and Expo and we’re delighted that so many great toolmakers will be joining us to show off their devices, apps, and services. We’ve  asked each of our toolmakers to give us a bit more background information about their company and what they’re excited about. If you’d like to meet these innovative companies and the amazing people behind them then make sure to register today!

How do you describe Oura?
Ōura makes a self-tracking ring that helps you be your best in everyday life. By sensing your body’s physiological responses, it guides you to adjust sleeping behavior and activity level to improve your sleep quality and optimize mental and physical performance. We want to help people stay balanced, improve quality of life and overall mental and physical performance.

What’s the backstory? How did you get started?
Our passion comes from our own need to stay balanced and performing well in the middle of hectic business and family life and our desire to provide the same opportunity for others so that people can live up to true their potential.

Several members of our team have created consumer products, either medical, wellness, sports and/or mobile and/or embedded systems in different contexts. A few of us have done research and created algorithms, one for more than 17 years at Polar, the leading manufacturer of heart rate monitors. Several members of our team have monitored themselves for a long time in different ways. We knew that to get a long-term view on physiological changes in the body we had to find a unique way to access the data with high accuracy but in a very desirable comfortable design. We found the finger to be an optimum place for measurement and through years of extensive R&D and prototyping, eventually managed to fit a full featured computer into a small enough form. The ŌURA ring was born.

What impact has it had? What have you heard from users?
The ŌURA ring will be available for pre-orders in July-August, so extensive feedback from users/customers is yet to come. However, within a limited group of people and our own team, we have been using ŌURA rings – some of us wearing two of them continuously – for more than half a year. And with the earlier prototypes, we’ve collected data for much longer.

Since launching the product at Launch Festival in San Francisco in March 2015, we have met hundreds of people, showing them the ring and the App and sharing the science and technology. The feedback has been very positive. People love the design and especially appreciate our focus on providing the user with an understanding of how to improve sleep quality, adjust activity, and balance them to optimize their mental and physical performance.

What makes it different, sets it apart?
ŌURA ring amplifies the voice of the body. Through the App it gives an opportunity to better understand what choices are good for us. Since our lifestyles are unique and the physiological reactions of our bodies are unique, by listening to our own bodies we can adjust our behavior to stay balanced and perform well.

ŌURA ring is simple and effortless to use. There are no buttons or lights, it requires nothing from you. It just senses your body and its reactions when you wear it. It combines style and comfort with high-end technology, applying over three decades of research on human physiology and behavior.

What are you doing next? How do you see it evolving?
Over the few months, we are creating connections – our target audience and especially early adaptors – mostly in the USA, to deepen our understanding of how we can refine the benefits of using the ŌURA ring in every day life.

ŌURA will be evolving in many ways, based on the feedback and ideas coming from the backers of the early adaptors in the pre-order campaign as well as from those partnering with us to develop their own solutions and applications based on ŌURA ring.

How can people find out more about you?
Check out our website and follow us on Twitter, Instagram, and Pinterest. And of course come see us in person at the QS15 Conference and Expo!

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Announcing the QS15 Conference Program

It’s finally here.

Next Thursday we’re welcoming over 450 self-trackers, inventors, artists, toolmakers, researchers, and scientists to the 2015 Quantified Self Conference. Over two days were hosting over 130 different talks, sessions, and demos that showcase the ingenuity and expertise of our community. We create our program from the ground up, soliciting ideas from each individual that registers, and this year we’re excited to have over 100 different attendees contributing to the program. It’s going to be great.

View and download the QS15 Program here.

Here’s just a few examples of the amazing Show&Tell talks, Breakout Discussions, Lunchtime Ignites, and Office Hours we have planned.

Show&Tell Talks
THREE YEARS OF LOGGING MY INBOX COUNT – Mark Wilson
The number of emails in my inbox correlates very well with my stress level. After passively tracking this number for three years, I explore what this and other data says about how I’ve controlled (and been controlled by) this stream of angst.

TRANSCRANIAL DIRECT CURRENT STIMULATION (TDCS) TO MANAGE MY STRESS. – JD Leadam
Transcranial Direct Current Stimulation (tDCS) is an emerging “at-home” method for influencing the brain using very low voltage electrical current applied to the scalp. I’ll show how I’ve used tDCS in conjunction with self tracking methods to assist in controlling my stress.

TIME AND INTENTION TRACKING – Allan Johnson
Does tracking my intentions affect how I spend my time? Using an app for self-reporting, I compared how I spent my time when tracking both my intentions and time.

CAN’T YOU SEE I WAS FALLING IN LOVE? – Shelly Jang
As I struggle with the iron discipline required for keeping consistent logs, I am often forced to look into what I call passively collected data sets. I explored whether I can excavate data artifacts from past and correlate them with known life events. Using Google hangout conversations, I ask “can’t you see I was falling in love?”

28 YEARS OF TRACKING: BUT WHAT HAVE I LEARNED? – Nan Shellabarger
I’ve got lots of data – weight, activity, sleep, and health. I find as I keep reviewing it, visualizing it in different ways, always looking for patterms, there are still things to be learned.

USING HEART RATE VARIABILITY TO ANALYZE STRESS IN CONVERSATION – Paul LaFontaine
I measured stress during conversations using off-the-shelf technology. The results were unexpected and at times funny; with some lessons for me about my “fight or flight” response.

IN PRAISE OF BAD DATA COLLECTION DURING EARLY FATHERHOOD – Thomas Richardson
Sleeplessness and the pressures of birth and postpartum life drove me to to collect information and quickly discard methods that appeared wasteful. Looking back, did the kinds of information I collected tell me more than the data itself?

RE-LIVING MY LIFE WITH MOOD TRACKING. – Kouris Kalliagas
I used an email-based mood tracking service for several months. I never used the data in any way till I noticed something which triggered me to look back at my mood tracking data and search for patterns.

Breakout Discussions
TRACKING BABIES! – Morgan Friedman
Like many parents, I tracked my newborns. By comparing my records with those of other parents using the same app I learned some interesting things about my son. I’m curious to see how they play out as he grows up.

HACKING OUR MICROBIOME – Alexandra Carmichael, Richard Sprague
Today it’s possible to get data on the microbes that live in our gut using personal genomics. We’ll lead a breakout workshop on understanding and hacking our microbiome.

THE QUANTIFIED SELF AT WORK – Joost Plattel, Phoebe Moore
More than 13 million wearable fitness tracking devices will be incorporated into employee wellbeing and wellness programs 2014-19. We will discuss how self-tracking and monitoring are used in working spaces whether traditional or freelance. What are the advantages/disadvantages of quantifying the self at work?

AGGREGATING MULTIPLE DATA SOURCES FOR SELF-KNOWLEDGE – Anne Wright, Randy Sargent
We’ve been working on aggregating, visualizing, and analyzing data for personal benefit, using multiple self-tracking sources. We’ll share our methods, and invite you to comment, ask questions, or share your own.

SEX, SEXUAL HEALTH & QUANTIFIED SELF – Ilyse Magy
Cycles, lovers, positions, kinks, symptoms, stats, safety: how can tracking sexual activity benefit our experiences? We’ll talk about what tools you’re using but mostly dream up the tools we would want to use. This is a sex-positive, feminist, inclusive space open to all gender identities.

QSXX: WOMEN-SPECIFIC QS CONVERSATIONS – Amelia Greenhall, Maggie Delano
Women-centered QS meetups in SF, Boston, and NYC have created space for important conversations. Nicknamed “QSXX” (though not all women have two X chromosomes), this breakout session is specifically for people who identify as a woman to talk about QS experiences.

THINKING THROUGH DATA ACCESS AND PRIVACY – Kendra Albert
How do you view third-party access to your data: either by governments, advertisers, or corporations? Are certain types of data okay to share but others make us feel icky? We’ll focus not just on privacy “in general” but on specific types of circumstances in which data might be shared, trying to draw lines between types of data and uses.

WHAT IS THE SELF IN QUANTIFIED SELF? – Natasha Schull
How do digital tracking technologies engender new modes of introspection, understanding, and self-governance?

Lunchtime Ignite Talks
THE DIGITAL HEALTH COACH – Glennis Coursey
You might have everything you need to be healthy – wearables, health apps, a wireless scale. But without the motivation and support to actually get healthy, change can be hard. That’s where digital health coaches come in. Glennis shares what she’s learned building digital health coaching programs at Sessions and MyFitnessPal.

FIGHTING PARKINSON’S DISEASE WITH DATA: ROUND THREE – Kevin Krejci
Round three in the proverbial boxing ring between Kevin and Mr. Parkinson, as he updates us on his progress tracking multiples symptoms and therapies with multiple gadgets to slow the progression of this progressive neurodegenerative disorder. Sleep and biome discoveries highlighted in this round…

A QUEST FOR HIGH FIDELITY ACTIVITY TRACKING – Jamie Williams
Jamie will show us how he is building tools to capture a timeline of his daily activities and explore his habits through data visualization.

AM I BEING INTENTIONAL? – Beau Gunderson
The challenges of tracking (and defining) intentionality.

WHY I WEIGHED MY WHISKERS – Jon Cousins
When I was diagnosed with bipolar affective disorder, I noticed that my libido seemed to, er, rise and fall as my mood changed. Could this be due to a variation in testosterone? And might the rate of growth of my beard be one way of measuring this? I borrowed accurate laboratory scales and started daily mood tracking and whisker-weighing.

YOU HAVE NO IDEA WHAT YOU’RE DOING – Cara Mae Cirignano
Whatify allows you to collect data in a mindful way in pursuit of a specific question, instead of just gathering reams of data and then rooting around for insights. We use the most powerful tool of professional researchers, randomized experimentation, to help you easily isolate and understand one decision at a time. No experience whatsoever required.

FRICTIONLESS TRACKING WITH BEEMINDER AUTODATA – Danny Reeves
Beeminder is Quantified Self plus commitment contracts: data-oriented behavior change. But mustering the discipline to enter data can be a catch 22. We’ll discuss the myriad ways you can automatically collect data about yourself with Beeminder, highlighting our partnerships with other QS mainstays like RescueTime, Fitbit, Withings, Zapier, and IFTTT.

Office Hours
SHERBIT – Alexander Senemar
Your apps and devices are constantly generating data about you. Sherbit puts it all together so you can easily understand and analyze your information, keeping the integrated day firmly under your own control.

REVVO – Siva Raj
Revvo is a smart exercise bike. Unlike apps and wearables that track activity (steps, calories, distance etc.) Revvo actually tracks your fitness – and helps you train smart – so you see quick results.

EXPLORING TOMORROW – Ryan O’Donnell
Exploring Tomorrow focuses on teaching students how to quantify their daily interactions and goals through the use of self-management tools developed through the science of behavior to align each student’s values with their daily actions.

HEADS UP HEALTH – David Korsunsky
Heads Up Health helps consumers combine medical, wearable and self-collected data with personalized analytics and insights.

SIREN – Ran Ma
At Siren we believe that prevention is the best medicine – we combine smart textiles and user-centric software to give people actionable data in order to make informed decisions about their health. The first product that we are working on is a sensor embedded sock that tracks temperature, combined with a smart wearable anklet tracking motion that connects to a smartphone via BLE.

PERSONAL DATA BANK – Arkadiusz Stopczynski
Personal Data Bank with SafeAnswers allows users to collect, store, and give fine-grained access to their data all while protecting their privacy. With this infrastructure available as a service, developers can create applications powered by personal data in an easy and scalable way.

PROACTIVE LIFE – Daniel Gartenberg
I work on a variety of projects to track and improve sleep. This includes smart phone sleep trackers, providing different types of auditory stimulation during sleep, and figuring out alertness using simple reaction time tasks.

FITABASE – Aaron Coleman
My company helps researchers use Fitbit data to make discoveries in public health and behavioral science. Stop by and I’ll show you how.

That’s just a sample of the over 130 different sessions at the conference. We’re nearly sold out so register today!

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