A long list for this week’s What We’re Reading. I actually had to stop myself from adding in even more visualizations and show&tell examples! We’re always on the lookout for more though, so make sure tweet us your favorite links!
Fitted by Moira Weigel. A very thoughtful essay on gender, identity, and confession – all while using the Fitbit as the narrative backdrop.
What kind of love does the FitBit prepare us to feel? Is it self-love? Or is even the self of the exorexic a kind of body armor?
How to Build a Smart Home Sensor by Dave Prochnow. If you have 2 hours, $95, and know how to solder, then you too can build this DIY sensor to measure the temperature, humidity, light, and noise for any room in your home. If someone builds and tests this please let me know (Would love to see air quality sensors included too!)
It’s Hard to Count Calories, Even for Researchers by Margot Sanger-Katz. New research shows Americans are eating less, but can we really trust the data? Margot does an excellent job here of rounding up the various ways we measure food consumption in the United States while coming to a commonly heard conclusion – food tracking is just plain hard.
Hadley Wickham, the Man Who Revolutionized R by Dan Kopf. If you’re knee deep in data analysis, or just like poking around in stats software, you’ve probably heard of and used R. And if you’ve used R, then there is a good chance you’ve used many of the packages written by Hadley Wickham. Great read, if for nothing else you learn what the “gg” in ggplot2 stands for.
Heart patient: Apple Watch got me in and out of hospital fast by Neil Versel. When Ken Robson wasn’t feeling well he turned to his Apple Watch. After noticing lower than normal heart rate readings his checked himself into the emergency room and soon found out his hunch was right, he had sick sinus syndrome.
New Australian experiment rewards joggers with 3D printed chocolate treats based on exercise data by Simon Cosimo. Sign me up!
How Does Giving Blood Affect Your Iron Levels? by Ryan W. Cohen. Simple and to the point blog post by Ryan explaining how he discovered elevated iron levels in his blood, and the simple test he tried to find out why.
The Quantified Athlete by Matt Paré. Matt is a minor league catcher in the San Francisco Giants organization. In this post, the second in a series (read Part 1 here), Matt discusses how he became interested in tracking his biomarkers, and what he’s experimenting with.
What I Learned When I Stopped Wearing a Fitbit After Seven Years by Michael Wood. Michael writes up a brief post on how he felt when he was separated from his Fitbit activity tracker.
How I tracked my house movements using iBeacons by Joe Johnston. Joe uses a few iBeacons to find out where he spend time in his house. Fascinating idea, makes me want to play with this technology as well!
Visualizing a Simpler RunKeeper Training Plan by Andy Kriebel. Andy presented his running data, and how he uses a few tools to keep track and visualize his data as he trains for a marathon. Follow the link and you can see his Tableu workbook, which includes a screencast of his presentation, and links to his workflow.
I decided to take a peek at my Netflix viewing data by Reddit user AmericanPicker69. This enterprising individual decided to take a peak into his user account to understand his Netflix viewing habits. Turns our a simple copy/past is all you need to do to get the raw data. Who knew?!
My weight loss journey by Reddit user IMovedYourCheese. Loved this graph and the implementation of BMI categories, a moving average, and lower/upper bounds for weight loss. He even provided the excel template if you’d like to use it with your own weight tracking.
We’re excited to share another round of personal data visualizations from our QS community. Below you’ll find another five visualizations of different types of personal data. Make sure to check out Part 1 and Part 2 as well!
Name: Siva Raj
Description: After 6 months of regular exercise failed to improve my fitness and blood pressure levels, I switched to training above my endurance limit (anaerobic threshold). This was higher intensity but half the cycling time, yet my fitness and blood pressure improved within weeks.
Tools:Revvo – tracking fitness and intensity of workout; Withings – weight; iHealth BP Monitor – BP. Visualization created by overlaying Revvo screenshot with other information in photoshop.
Name: Kurt Spindler
Description: Grafana is a common tool in the Software community to create beautiful dashboards to visualize server health (network, requests, workers, cpu, etc.) and therefore more easily diagnose problems. I created a custom iOS app that allows me to publish metrics to the same backend as Grafana, giving me Grafana dashboards for my personal health.
Tools:Custom iOS app, Grafana, Graphite
Name: Ryan O’Donnell
Description: This semi-logarithmic graph is called the Standard Celeration Chart (SCC). It’s beauty is that anything a human does can be placed on this chart (i.e., standardized display). This also allows for cool metrics to be developed that lend well to predictability. I charted the number of pages that I read for my field of study, Behavior Analysis. I wrote a blog post on the display to speak some to the reading requirements suggested by professionals in the field. There were many variables that led to variations in reading rate, but the point of this work was to try and establish a steady reading repertoire. A recent probe in May of 2015 was at 2800 pages read. Essentially, I learned how to incorporate reading behavior analytic material almost daily in my life, which indirectly aids in the effectiveness I have as a practitioner and supervisor.
Tools: Standard Celeration Chart and paper-based data collection system (pages read each day on a sheet of paper).
Name: Francois-Joseph Lapointe
Description: This *Microbial Selfie* depicts the gene similarity network among various families of bacteria sampled from my gut microbiome (red) and oral microbiome (black). Two bacteria are connected in the network when their gene sequences are more similar than a fixed threshold (80%). The different clusters thus identify bacterial families restricted to a single body site (red or black) versus those inhabiting multiple body sites (red and black).
Tools: In order to generate this data visualization, samples of my oral and gut microbiome have been sequenced on a MiSeq platform by means of 16S rRNA targeted amplicon sequencing, and the resulting data have been analyzed using QIIME, an open-source bioinformatics pipeline for performing microbiome analysis. The gene similarity network was produced with the open graph viz platform Gephi, using the Fruchterman–Reingold algorithm.
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!
After going through a life crisis during his teenage years, Damien Catani turned to tracking his dreams to help “rebuild his sense of self.” Eighteen years and seven thousand dreams later Damien shared his tracking process and what he’s been learning at the QS15 Conference and Expo. In his data he found patterns for the number of dreams he experienced and remembered according to the day of the week, season of the year, and the affect of different lifestyle factors.
Have you registered for our QS Europe Conference? Makes sure to do so soon as early bird ticets (€149) are almost sold out. Register today!
Why Cities Need More Technology To Improve Low-Income Citizens’ Lives by Ben Hecht. Can technology create meaningful impact for the disadvantaged in communities across the United States? In this brief article, Ben Hecht describes a few exemplary projects, which are building and using technology to for social impact.
We are data: the future of machine intelligence by Douglas Coupland.
I sometimes wonder, How much data am I generating? Meaning: how much data do I generate just sitting there in a chair, doing nothing except exist as a cell within any number of global spreadsheets and also as a mineable nugget lodged within global memory storage systems — inside the Cloud, I suppose.
New open source uBiome github repository for data analysis tools by Alexandra Carmichael & Richard Sprague. Have you started testing your microbiome and want to do a more in-depth analysis? Check out this post and the open source tools.
You Shouldn’t Trust Me by Mike Lazer-Walker. A brilliant post by Mike describing a coffee tracking application that he released as both a paid and open source application.
It’s more important to me that we seriously think about our privacy, and what trust means in context of software that handles our personal data. We need to think about the repercussions of trusting large corporations that don’t have our best interests at heart and have no incentives or obligations to be transparent.
Track It! by Dave Mierau. A short post, but Dave does a great job of describing the power of monitoring and tracking. Go spreadsheets!
Currently Tracking by Chris Campbell. What is your tracking routine? In this post, Chris describes a day in the life of his quantified self. I’m sure you’ll learn about one or two new apps/tools. I did!
Carl Apstein: Annual Reports by Nicholas Felton. It’s no surprise that Mr. Felton is always on the lookout for Annual Reports from around world. In this post he posts a few photos from a hand-drawn report by the German zoologist, Carl Apstein, from the 1930s.
From the Forums
This Week on QuantifiedSelf.com
Our Data, Our Health: Thoughts on using mHealth for the Precision Medicine Cohort
Quantified Self Public Health: Stephen Downs on Building a Culture of Health
Runkeeper & Research: The Keeping Pace Study
Yesterday evening I laced up my running shoes, connected my bluetooth headphones, turned on my Spotify playlist, and most importantly, hit “Go Running” on my Runkeeper app. About an hour later and I had run 6 miles at a decent pace of around 8 minutes per mile. And I knew this thanks to Runkeeper.
Founded in 2008, Runkeeper is designed to assist individuals who want to track their activities with GPS precision, whether that is walking, running, hiking, or cycling. If you’re moving outdoors, Runkeeper and similar apps, such as Strava or MapMyRun, use your smartphone’s GPS to pinpoint exactly where you are and how fast you’re moving. With all that data, you can train for your next marathon, discover new routes, and now, thanks to efforts by New York University researchers, take part in advancing public health research.
“We know from the existing literature that spatial characteristics like walkable neighborhoods and green spaces encourage exercise, but a lot of the details are still unknown.”
Last week, Dr. Rumi Chunara and her colleagues launched the Keeping Pace study. Over the next few months they hope to enroll participants who are willing to share their geo-located exercise data from Runkeeper. Because Runkeeper keeps a log of not only what you did, but where you did it, researchers hope to use the large amount of aggregated data to better understand physical activity patterns in communities around the United States.
“Typically, this type of research takes a long time and includes long, ardorous surveys or giving out GPS devices to participants,” said Dr. Chunara. “But with this type of data from apps people already use, we will be able to understand how the environment and exercise are related over more rapid and longer time periods.” With this data being contributed, the research team hope to understand differences in exercise choice between commuting and recreational activities, variation in activities among neighborhoods, and where people spend their time while being active.
Participants who enroll in Keeping Pace will be asked to complete a short demographic survey and then connect their Runkeeper account so researchers can access the type of activities they do and the GPS-based map associated with the activity. The Runkeeper data connection is being handled by a unique research platform, Open Humans.
A few years ago, Dr. Rumi Chunara was at a meeting hosted by the US Department of Health and Human Services. She was there to present and speak with colleagues about the growing importance of citizen science and crowdsourced data. There she met Jason Bobe, Executive Director of PersonalGenomes.org. They got to talking about some of their common insterests in open data, research, and new models for research participation. Later, when Dr. Chunara was designing GoViral, a project to examine how to leverage crowdsourced flu symptom information and diagnostics to predict illness risk, she ran into some issues with hosting and handling the amount of data participants were contributing. “It was obvious we needed some sort of platform to handle data,” said Dr. Chunara. She got back in touch with Jason, who helped her think about the issues and how to solve them.
This year, when it came to build out the infrastructure for the Keeping Pace study, Dr. Chunara decided to get back in touch with Jason and his colleagues, who were now developing OpenHumans.org. As we’ve written before, Open Humans represents a new way of thinking regarding researcher studies, participants, and the data being transferred between the two. The two teams, Open Humans and Dr. Chunara’s lab at NYU, worked together to develop an easy method for individuals to simultaneously allow researchers access to their Runkeeper data, and also maintain control over where and how that data flowed. Specifically, each individual who chooses to participate in the Keeping Pace study will be asked to create an Open Humans account, connect their Runkeeper account, and then authorize the Keeping Pace study to access their data. It sounds like a lot of work, but thanks to the designers and the use of Runkeeper’s API, it takes no more than five minutes to complete.
But why go through that trouble at all? Why not just have participants export their Runkeeper data and send it to the researchers? Why didn’t Dr Chunara and her colleagues build that data connection themselves?
Thanks to the proliferation of sensors, wearables, and smartphones, the ability to generate data about our lives is rapidly expanding. Pair that data with new efforts like the Precision Medicine Initiative and it’s easy to see the potential for researchers to understand our lives and our health in new and interesting ways. But what about the people who create that data? People, like myself, who strap on their phones when they go out for a run or log onto a website to report their flu symptoms. What do they have a right to in regards to their data? This is the question many researchers and scientific institutions are grappling with. But some have already taken a stand.
“What data are collected and how varies across research studies, but the question remains, ‘Who owns it?’ If someone is spending time generating then they should have control over it.”
Dr. Chunara and her colleagues chose to work with Open Humans because they shared the same perspective — participants should be in control of their data. “Open Humans has created an infrastructure that makes it easy to share and learn while respecting the participant and their data. That’s a noble motive, and it’s important,” said Dr. Chunara. Today, Keeping Pace is the first study to use Open Humans for data access and management for a research study. If successful, researchers may not only learn about exercise and the environment, but also about how studies that place an emphasis on participants’ data access and control may engage the public in new ways.
Keeping Pace is currently enrolling participants. If you’re a Runkeeper user and want to contribute your data to research, please visit the study website to learn more.
Keeping Pace was funded as part of the Agile Projects grants by the Health Data Exploration Network. If you’re a researcher, company, or individual interested in personal health data, sign up to become a network member. Membership is free.
Quantified Self Labs is dedicated to sharing stories and insights about the role of data access for personal and public health. We invite you to share your data access stories and this article. Then follow along on quantifiedself.com and @quantifiedself.
On January 30th, President Obama announced the funding of a possibly groundbreaking research program — The Precision Medicine Initiative (PMI).
Launched with a $215 million investment in the President’s 2016 Budget, the Precision Medicine Initiative will pioneer a new model of patient-powered research that promises to accelerate biomedical discoveries and provide clinicians with new tools, knowledge, and therapies to select which treatments will work best for which patients.
Since the announcement the National Institutes of Health (NIH) have been hard at work convening a working group to build a foundation of rules, standards, and principles upon which they hope will generate meaningful outcomes: improving the health for all Americans by moving towards a more nuanced and individual view of health and wellness. As part of this project, the largest portiont of funding is being dedicated to the “development of a voluntary national research cohort of a million or more volunteers to propel our understanding of health and disease and set the foundation for a new way of doing research through engaged participants and open, responsible data sharing.”
As part of engaging this cohort the NIH is considering the role of patient generated data from mobile phones and sensors. Of course this is where we at Quantified Self Labs become intrigued. We have a long history of supporting individual’s ingenuity, insight, and expertise when it comes to personal data they collect on their own. Since 2008, we’ve been bringing together people to share their stories of self-tracking using a variety of different methods, some of which are no doubt being examined by researchers and NIH leadership for use in the proposed Precision Medicine Cohort.
We are excited to hear that the NIH is taking the time to listen to the American public through the use of online feedback forms. They are currently seeking comments on the use of mHealth for the Precision Medicine Cohort. Specifically, they want to know how people think that data generated by current and future biometric and physiologic sensors (such as heart rate and physical activity tracking devices) could be useful. Furthermore, the NIH isinterested in reactions to using smartphones to collect data on volunteer participants in the cohort. In the short description of the feedback request they highlight five key considerations:
1. Willingness of participants to carry their smartphone and wear wireless sensor devices sufficiently throughout the day so researchers can assess their health and activities.
2. Willingness of participants without smartphones to upgrade to a smartphone at no expense.
3. How often people would be willing to let researchers collect data through devices without being an inconvenience.
4. The kind of information participants might like to receive back from researchers, and how often.
5. Other ways to conveniently collect information from participants apart from smart phones or wearable devices.
We spent a little time browsing through the current crop of comments, which didn’t take long as there are only 52 at the time of this writing, to understand how people are thinking about mHealth, their data, and what it means to contribute personal data for public health research.
Privacy & Confidentiality
A common theme was a concern over what type of privacy protections would be implemented to protect volunteers who contribute their data. Comments ranged from outright fear of the government “tapping into personal computers, phones or other devices for collecting health information” to thoughts on access, control and protection of data contributed as part of this project.
As long as citizens can remain in control of the collection, flow and use of their data — and the government can guarantee anonymity, much benefit can be had from this. If it is going to be a government initiative, then standardized collection methodologies and protections will be required and the data should not “also” be collected for commercial usage. Clarity surrounding use models, sharing permissions and general privacy and security are a must.
I would also like to make sure that it is clear who ‘owns’ the data. Many times health professionals collect data about patients and then use it–often without letting the person know the data is aggregated and studied.
Diversity and Representativeness
As with any research study, there is a call to make sure that the sample being studied is representative of the population. This is especially important given the expressed interest in using different types of technology to track, measure, and engage with research participants. Those who have offered feedback have clearly picked up on the need to make sure that even those who are not current users of wearable sensors and/or smartphones are considered.
This will leave out the severely ill and disabled who are bedridden, unable to move, and definitely unable to manage a smart phone (as well as anyone whose illness causes cognitive challenges). Yet these are the patients who really need to be studied. So while it’s not a bad idea, please factor in selection bias and please, please, accommodate the most ill if you decide to implement this.
Interestingly, there are a few comments on how dependence on a smartphone may limit the diversity of the cohort.
Smartphones, etc. will limit the diversity of the sample. For example, large areas of Appalachia will be excluded. Internet access is limited in rural areas; even if available, the technology is not adequate to support many applications.
While the diversity of the sample is something that must be considered, I’m left wondering about the true impact of using smartphones for data collection as recent data from Pew suggests that nearly two-thirds of Americans own a smartphone. It appears that economic diversity may be the limiting factor when it comes to using smartphones as part of the cohort. Clearly there is a gap in smartphone ownership across income and education levels, but also when we consider geographic location.
However, one of the considerations clearly states that an “upgrade to a smartphone at no expense” may be part of this research initiative. What is the true cost of this free upgrade? It appears to be unknown, but it’s going to be important to think about the recurring costs of data plans associated with these devices, especially when data transfer is part of participating in the research.
The issue probably depends on the participant and what is being provided. I’m assuming providing the smartphone would include providing the data plan, which can be expensive.
Barriers to Using mHealth
While using wearable sensors, apps, and smartphones to collect personal health data is growing trend, there are still concerns regarding how long individuals will actively use the tools. While the Fitbits of the world are selling millions of devices, we don’t really know how long people are willing to use them, and if they’re willing to contribute that data to research (although preliminary studies are promising).
There is concern that adding devices, measurement, and data collection to the everyday lives of individuals may present a burden and that the data will suffer from inconsistent engagement. The “life gets in the way” of participating in research is a common, and justified, refrain.
In our experience, conducting a number of trials using devices, many people do not carry or use devices for very long that do not fit into their existing habits. People may use new devices provided by researchers for short periods of time, if provided research support, monitoring, and prompting, but for large scale trials and longer assessment periods, adherence will fall off considerably.
Benefits of mHealth
It’s not all doom and gloom and negativity though, there is an overwhelmingly positive outlook on the use of wearables and mobile-based data collection for informing personal and public health. From researchers to individuals already using these devices to understand their own health, the current comments are full of support for exploring new technologies, sensing capabilities, and personal data collection methods to deliver personal precision medicine.
Integration of data from personal sensors and mobile devices has the potential to change the role of the patient in their health and care, to improve the accuracy and value of behavioral data in healthcare, and to provide unparalleled insights into the how and why of behavior change in health.
I am 60 but use a Withing Blood Pressure Cuff, Basis Peak and Alive EKG. I am a cancer survivor of Hodgkin’s Lymphoma and Breast Cancer. Due to my treatment I am left with complications that need to be monitored. I feel more secure using these devices. Knowledge is power.
In the name of transparency, below you’ll find the full text of our comments submitted to the NIH.
The world is changing, more information is flowing across what were once impassable borders. That information is changing the way we see the world, and how we understand ourselves. Health and healthcare is a big part of this evolution in information flows. At Quantified Self, we’ve seen hundreds of examples from individuals around the world who have used personal data collected through a variety of means to impact and understand their health. It’s with these examples in mind that I’d like to share my thoughts on using mHealth for the Precision Medicine Cohort.
The considerations mentioned above are clear, but vague enough to make the only appropriate answer, “it depends.”
Will participants carry their smartphones and wear sensors? It depends. It depends on how participants are recruited and what they are being asked to contribute to. Do they have a say in what questions are being asked? Is constant data collection a requirement for participation, or can participants engage inconistently, contributing data sparsely? What activities would researchers want to understand? Some individuals may be perfectly okay with contributing physical activity data, but not geolocation data supplied by a smartphone GPS.
Will participants be willing to upgrade to a smartphone? It depends. Will they also be compensated for an increase in costs associated with data plans so that their smartphone can send data to a researcher? Will they be able to use their smartphone for personal use, downloading apps and services freely? Is the smartphone upgrade dependent on participation in research for a given amount of time, and if so, how long? Is this form of compensation coercive for those who have never been able to afford a smartphone? Including a socially and economically diverse population in the cohort while not introducing increased costs and burden will be important to consider.
Will participants allow researchers to collect data, and how often? It depends. Will participants play an active role in the data collection, or will it be passively collected through sensors and background data transfer? Will participants be engaged in the full research process, helping develop the questions, data collection methods, and even the analysis? Will participants be able to choose the frequency of data transfer that makes sense to them and their lifestyle? It’s possible to envision participants contributing infrquently over a long timescale. Will these type of contributions help push precision medicine forward?
What kind of information would participants like to receive, and how often? It depends. Are participants able to access and control the data they create? Do they get to choose what researchers or studies are able to use their data? Will they receive the same information and data that researchers have access to? All participants may not actually want their data, but I believe that the research community has an ethical obligation to be open, honest, and transparent when it comes to what we collect. This includes giving data back to participants, especially with regards to health data. Participants should also receive timely access to any and all research findings. Published work that results from Cohort data should be published in open access and freely available outlets (and open data sets should be published simultaneously).
What other ways might their be to collect information from participants? It depends. What type of information is important to researchers? Many different research groups, and even commercial entities, have discovered the power of using text messaging for data collection. Text messaging should be examined as a possible large-scale and low-cost (financial and time) method for understanding many different aspects of personal health.
Smartphones, wearable devices, and personal data applications and services represent an unprecedented look into our daily lives. The Precision Medicine Initiative should continue to explore how best to incorporate the public in the process of crafting the protocols and methods. The public will be the participants, and we should remember that this research is being conducting with them, not on them. A spirit of openness and transparency should guide this important work.
The public comment period for input on using mHealth for the Precision Medicine Cohort closes on Friday, July 24. We invite you to add your thoughts and ideas.
Enjoy this week’s list!
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?
Quantified 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.
Where 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.
Hippo 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
This Week on QuantifiedSelf.com
After a bit of a hiatus, mostly due to our planning and production for the QS15 Conference and Expo, we’re back again with another episode of QS Radio. Join us as we discuss last month’s conference, including the great show&tell talks, breakout sessions, and some of the great exhibitors.
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!
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.
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.
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.
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.
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.
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!
Hello again! Here we are with another list of articles, links, and visualizations for you. Enjoy!
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
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.
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.
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.
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 2011, 2012, and 2013.
This week on QuantifiedSelf.com
2015 QS Visualization Gallery: Part 1
2015 QS Europe Conference: Scholarship Application Now Open