Tag Archives: Work
Philosophy, bicycles and brains, opinions on tracking sleep, learning from actually tracking sleep, and visualizing work through vigilant self-report – all these and more in our reading list below. Enjoy!
Sleep apps and the quantified self: blessing or curse? by Jan Van den Bulck. Here at QS Labs, we’re very interested in how the academic and research world is colliding with those of us using tools of measurement previously restricted to science. In this Letter to the Editor, published in the Journal of Sleep Research, the author lays out an interesting set of opinions about the increasing availability and use of commercial sleep tracking devices. (You can access the full pdf here.)
Measuring Brainwaves to Make a New Kind of Bike Map for NYC by Alex Davies. Readers of the QS website may remember a great show&tell talk we featured back in May of 2014. In that talk, Arlene Ducao discussed her MindRider Project, an EEG tracking bicycle helmet. In this short piece, we learn that Arlene has continued this awesome work and has produced MindRider Maps Manhattan, exposing the brain data of 10 cyclists as they transversed New York City.
Big Data and Human Rights, a New and Sometimes Awkward Relationship by Kathy Wren. Earlier this year the AAAS Science and Human Rights Coalition held a meeting to discuss the intersection of personal data collection and human rights. This short article describing some of the key discussion points is a great place to start if you’re exploring what “big” and personal data means to you and your use of the tools and services that collect it. (Videos of the meeting are also available.)
How Theory Matters: Benjamin, Foucault, and Quantified Self—Oh My! by Jamie Sherman. A very interesting and thought-provoking essay here on the nature of self-tracking and data collection framed against the works of Michel Foucault and Walter Benjamin. We count ourselves lucky to have Jamie as an active member and observer of our QS community.
But taken together, Foucault and Benjamin suggest that the penetration of data into daily life is part of a larger shift underway, and that changes we can already see in social life, politics, and labor are not unrelated, but rather intimately linked.
Compulsory Quantified Self by Gwyneth Olwyn. I think it’s good practice to try and expose ourselves to all sides of the conversation around self-tracking, the positive and the negative. In this blog post Gwyneth describes a few ideas about the purpose and outcomes of self-tracking, especially when the self is superseded by the demands of others (such as in a workplace wellness program).
Sleep Data Analysis with R by Ryan Quan. Ryan has been tracking his sleep with the Sleep Cycle app for the last two years. In this excellent post he explores and plots his data (yay export!) to see when he goes to sleep, how long he sleeps, and what really makes up “quality sleep.” Love the fact that he included his R code and sample data. Go Ryan!
Quantifying Goals Using Key Performance Indicators (KPIs) by Bob Troia. No data in this post, but I found it particularly inspiring to see how Bob was planning on keeping track of his goals for this year. If you’re looking for ideas for tracking your 2015 goals and Key Performance Indicators this is a great place to start.
The Resume Of The Future by Eric Boam. The above is one of the two beautiful visualizations created by Eric to explore his daily work activity and interactions. This visualization shows what he was actually spending his time on. How did he collect the data? Well, he used the Reporter App to ask himself three questions: “where are you, what are you doing, and who are you with?” Make sure to read his post, he developed very interesting insights through collecting this data.
Weight Loss: What Really Works? by Emi Nomura and Laura Borel. Another fascinating data analysis project here by the Jawbone data science team. They examined the behaviors of a group of users who lost at least 10% of their starting weight vs users with no weight loss and found that the biggest difference in behavior was tracking meals.
Mapping my Last Two Years of Runs and Rides
While browsing the r/dataisbeautiful subreddit I stumbled upon this interesting tool/company that visualizes the maps of your runs and bike rides by connecting to your Runkeeper or Strava account. Above I’ve included my 2013 and 2014 maps. Clearly I need to find some new running routes in my neighborhood. (click through to enlarge)
QS Access Links
As part of our new work highlighting stories, issues, and innovations related to personal data access we’re going to start publishing a short collections links in this space. As this works grows be on the lookout for a new Access Newsletter from QS Labs.
Who Should Have Access to Your DNA?
What FDA developments in Diabetes mean for FDA approval in Digital Health
Open consent, biobanking and data protection law: can open consent be ‘informed’ under the forthcoming data protection regulation?
WTF! It Should Not Be Illegal to Hack Your Own Car’s Computer
Unique in the shopping mall: On the reidentifiability of credit card metadata
Majority of Consumers Want to Own the Personal Data Collected from their Smart Devices
Who Owns Patient Data
Los Angeles County Supervisors OK Creation of Open-Data Website
Debbie Chaves is a science and research librarian at Wilfred Laurier University and was interested in understanding her job and the various demands placed on her time. Using methods she’d employed previously she set about tracking different aspects of her work. The data she gathered allowed her to advocate for new changes and policies within her library. In this video, presented at the 2014 Quantified Self Europe Conference, Debbie explains her tracking, what she found, and what she was able to accomplish.
We’ve all come face to face with tracking some aspect of our life only to realize that we’re not quite sure how to get started. Enrico Bertini encountered this roadblock when he began thinking about tracking the amount of time he spends engaging in “focused work.” As an information visualization researcher at NYU he decided on a simple rule that would give him the most accurate data that represented his interests: if it wasn’t tracked then it wasn’t focused work. In this talk, given at the New York QS meetup group, Enrico explains his process and shares his findings (including some great visualizations).
Slides available here.
(Editor’s Note: Enrico also co-hosts a great podcast on data visualization and information design called Data Stories. I highly recommend listening. If you’re looking for a place to start try Episode 17: Data Sculptures.)
Nick Winter is a tracker, self-experimenter, and builder of popular tools (like Quantified Mind). Nick sent us this amazing visualization of his percentile feedback system he uses to keep track of his work efficiency.
My percentile feedback graph of my development productivity helps my motivation
Here is another peek behind-the-scenes at Quantified Self Labs, explaining how we work and why we have so much fun.
There are two basic principles we follow in a pretty hard-core way as we grow and nurture our community. They are tied tightly together, and make it really stress-free to do this QS work. These are minimalism and sustainability.
Contrary to common perception, minimalism is not about having and doing as little as possible of everything. It’s about having as much as you need of things you value, and not spending money on one thing extra. It’s only doing your highest value work that feels good and is needed, and not using up time on anything that’s not necessary or fun.
So for instance, we don’t have office space, because we’re happy working from home and libraries and coffee shops. That’s how I get my five miles of walking in every day, by having a coffee shop just far enough from my house that it gives me a good, regular workout. But we do have really good computers, because they’re our tools for making all this possible, and we need to work with the best tools (otherwise it’s a waste of time and productivity.)
Obviously, minimalism requires knowing what it is that you value, and learning how to recognize opportunities that fit your values. So for example, sitting on a conference call is neither enjoyable nor an effective way to get things done, compared to our other methods. We therefore say no to anything that requires us to participate in conference calls. But long one-on-one walks are both connecting and inspiring, so even if they’re not strictly necessary to get work done, we do them because we value them.
Does it seem too foolish to use conference calls as a filter for involvement in a project? Isn’t this letting a minor detail get in the way of bigger issues? Surprisingly, no. A request to submit to conference calls is a great clue that we won’t be able to use our best minimalist methods. We will have to substitute process for true organization, and waste precious time. Using simple assays like “no conference calls” to inspect opportunities for minimalism is itself a great tool of minimalism.
Minimalism is related to another principle that means a lot to us: sustainability. If you only spend time and money on essential things, and get really good at saying NO to everything else, you can keep your project going pretty much indefinitely.
All three of us here at QS labs: me, Gary, and Kevin, have watched many venture funded companies come and go over the years. We understand why people take this route, and we always root for people in our community to succeed with their startups. But for ourselves, we’ve decided that this method is not the best. A truism of the startup culture is that investors only expect one in ten of their companies to succeed. While it’s good that people have a chance to fail, and failure isn’t held against people who take risks, we don’t particularly want to fail. It hurts us to see friends rush headlong towards failure, afraid to be honest with themselves because of the burden of the financial obligations they’ve accrued. We’d like better odds for ourselves, and for our community members and collaborators.
One way to improve the odds is to be able to start small, take time, listen, experiment, and learn. Not taking any investment funding allows us to do this. We considered making QS Labs a nonprofit, but when we looked into it we found that even this approach involved more overhead (paperwork, board of directors, meetings) than we felt was necessary. In the end, we decided just to articulate our social vision and get to work.
Also, though it isn’t talked about very much, emotional sustainability is as important as financial sustainability. If you consistently do things you don’t like because you feel like they need to be done for some reason, you are likely to burn out. Why not think about other ways to get the same results, that are also enjoyable for you? For us, focusing on being gentle with ourselves and taking good care of our emotional and physical well-being is a priority. This has many good effects, including allowing us to imagine continuing to do this work for a long time.
I hope this inspires you to consider what you value in your work, and find ways to bring more of that into your daily routine in a simple, sustainable way.
(Thanks to lisbotk for the great photo!)
This is the first of a series of posts to give you a sneak peek behind the scenes at Quantified Self Labs, for a bit of flavor on how we work.
In case you’re thinking about it, it’s best not to try to reach us by phone or ask us to schedule a call. It’s nothing personal, and we’d love to hear from you! We just don’t use phones unless absolutely necessary (and I don’t use phones even then).
So how do we get things done? First of all, email and Google Docs can go a long way, and are usually sufficient to make progress on most things.
If a real-time conversation really needs to happen, Google chat works surprisingly well for 2-, 3-, or 4- person chats. It’s fast and clear, everyone can talk and think at the same time, and you have a helpful transcript of the chat when you’re done.
Phone calls are low-bandwidth, often hard to understand because of poor reception, and if there are more than 2 people involved, you can be pretty sure the others are checking Twitter instead of listening.
Chatting takes a bit of getting used to for some people, but they usually get the hang of it pretty quickly and are surprised to learn how much smoother it is. We’ve even converted some die-hard phone people to chatters! And we feel much more peaceful and happy working without phones. So if you ever send us a note and ask us when we can schedule a phone call, please don’t be upset or confused when we say: “we don’t use phones.” Consider yourself part of an experiment in making collaboration more effective and pleasurable.
In future Lab Notes posts, I’ll write about how and why we minimize meetings and presentations, how we keep our finances super lean, and why we’re very gentle with ourselves. Anything else you want to know about QS Labs? Feel free to ask in the comments below.
In January, after talking with Matthew Cornell, I decided to measure my work habits. I typically work for a while (10-100 minutes), take a break (10-100 minutes), resume work, take another break, and so on. The breaks had many functions: lunch, dinner, walk, exercise, nap. I wanted to do experiments related to quasi-reinforcement.
I wrote R programs to record when I worked. They provided simple feedback, including how much I had worked that day (e.g., “121 minutes worked so far”) and how long the current bout of work had lasted (e.g., “20 minutes of email” — meaning the current bout of work, which was answering email , had so far lasted 20 minutes).
I collected data for two months before I wrote programs to graph the data. The first display I made (example above) showed efficiency (time spent working/time available to work) as a function of time of day. Available time started when I woke up. If I woke up at 5 am, and by 10 am had worked 3 hours, the efficiency at 10 am would be 60%. The display showed the current day as a line and previous days as points. During the day the line got longer and longer.
The blue and red points are from before the display started; the green and black points are from after the display started. The red and black points are the final points of their days — they sum up the days. A week or so after I made the display I added the big number in the upper-right corner (in the example, 65). It gives the percentile of the current efficiency compared to all the efficiency measurements within one hour of the time of day (e.g., if it is 2 p.m., the current efficiency is compared to efficiency measurements between 1 p.m. and 3 p.m. on previous days).
I started looking at the progress display often. To my great surprise, it helped a lot. It made me more efficient. You can see this in the example above because most of the green points (after the display started) are above most of the blue points (before the display). You can also see the improvement in the graph below, which shows the final efficiency of each day.
My efficiency jumped up when the display started.
Why did the display help? I call it percentile feedback because that name sums up a big reason I think it helped. The number in the corner makes the percentile explicit but simply seeing where the end of the line falls relative to the points gives an indication of the percentile. I think the graphical display helped for four reasons:
1. All improvement rewarded, no matter how small or from what level. Whenever I worked, the line went up and the percentile score improved. Many feedback schemes reward only a small range of changes of behavior. For example, suppose the feedback scheme is A+, A, A-, etc. If you go from low B- to high B-, your grade won’t change. A score of 100 was nearly impossible, so there was almost always room for improvement.
2. Overall performance judged. I could compare my percentile score to my score earlier in the day (e.g., 1 pm versus 10 am) but the score itself was a comparison to all previous days, in the sense that a score above 50 meant I was doing better than average. Thus there were two sources of reward: (a) doing better than a few hours ago and (b) doing better than previous days.
3. Attractive. I liked looking at the graphs, partly due to graphic design.
4. Likeable. You pay more attention to someone you like than someone you don’t like. The displays were curiously likable. They usually praised me, in the sense that the percentile score was usually well above 50. Except early in morning, they were calm, in the sense that they did not change quickly. If the score was 80 and I took a 2-hour break, the score might go down to 70 — still good. And, as I said earlier, every improvement was noticed and rewarded — and every non-improvement was also gently noted. It was as if the display cared.
Now that I’ve seen how helpful and pleasant feedback can be, I miss similar feedback in other areas of life. When I’m walking/running on my treadmill, I want percentile feedback comparing this workout to previous ones. When I’m studying Chinese, I want some sort of gentle comparison to the past.
Yesterday I wrote about the inaugural QS Show&Tell, where the very first show-and-teller, Ka-Ping Yee, stood up and explained that he had been tracking most of his activities over the last three years. (I didn’t want to use his name or link to his entry until I asked permission, which he quickly granted.) Below is a chart of a recent period.
The orange is writing and the grey is sleep. (He was working on his dissertation at the time.) The large gap on the left hand side is when he went to burning man. Ping collects this data using a widget he wrote. There is a text box in the upper right hand side of screen, and he simply keeps a brief log of his activity. He writes a few words, and a time stamp is automatically added. Duration is calculated by subtracting the time one activity began from the time the subsequent activity began. He can edit the text, which allows him to note activity that takes place away from the computer; he just catches up on his list when he’s back. And by using some keywords – for instance, “fun” – in the text, he can easily write a script to graph the allocation of his time.
I liked this idea, because it permits Ping to log his actions in normal written language, but also allows him to gather data and graph it. No natural language processing AI needed!