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Tag Archives: productivity
Stan James on Project Life Slice
Last December, Stan James started to wonder how much of every day he spent staring at glowing rectangles, and how he was spending that time. He set up his webcam to take a picture of himself every hour, as well as a screenshot of what he’s working on. In the video below, Stan talks about how he set up his project, shows some of his data, and reveals some interesting tidbits about his learnings. (Filmed by the Bay Area QS Show&Tell meetup group.)
Nick Winter on Productivity Tracking using Percentile Feedback
Nick Winter was inspired by Seth Roberts to track his productivity. He uses the method of percentile feedback, which compares his current productivity to past productivity as he goes about the hours of his days. Nick uses it to help prioritize his work projects, and he gives a short talk about his experience below. (Filmed by the Pittsburgh QS Show&Tell meetup group.)
Adam Loving on Featbeat
Adam Loving wanted a very lightweight way to track what he did each day, without tweeting it to the world. He built a simple system where he can tell Siri what he did, and it gets recorded in a database. Some data gets automatically entered through if this then that. Adam found that it has motivated him to continue his pushup/situp routine, and keeping his system simple has helped him uncover some funny problems for future improvement. (Filmed by the Seattle QS Show&Tell meetup group.)
Buster Benson: How I Use RescueTime
Buster Benson of Habit Labs likes to experiment with productivity, among other things. He uses RescueTime to see which apps and websites he spends the most time on each week. The winners are his text editor (for coding) and Gmail. In the video below, Buster talks about the ease of different kinds of tracking, from passive to binary to active entry, and previews some some Habit Labs apps. The folks from RescueTime are also present, adding to the audience discussion. (Filmed by the Seattle QS Show&Tell meetup group – first video from them!)
Percentile Feedback Update
In March I discovered that looking at a graph of my productivity (for the current day, with a percentile attached) was a big help. My “efficiency” — the time spent working that day divided by the time available to work — jumped as soon as the new feedback started (as this graph shows). The percentile score, which I can get at any moment during the day, indicates how my current efficiency score ranks according to scores from previous days within one hour of the same time. For example, a score of 50 at 1 p.m. means that half of the previous days’ scores from noon to 2 p.m. were better, half worse. The time available to work starts when I get up. For example, if I got up at 4 a.m., at 6 a.m. there were 2 hours available to work. The measurement period usually stops at dinner time or in the early evening.
This graph shows the results so far. It shows efficiency scores at the end of each day. (Now and then I take a day off.) One interesting fact is I’ve kept doing it. The data collection isn’t automated; I shift to R to collect it, typing “work.start” or “work.stop” or “work.switch” when I start, stop, or switch tasks. This is the third or fourth time I’ve tried some sort of work tracking system and the first time I have persisted this long. Another interesting fact is the slow improvement, shown by the positive slopes of the fitted lines. Apparently I am slowly developing better work habits.
The behavioral engineering is more complicated than you might think. My daily activities naturally divide into three categories: 1. things I want to do but have to push myself to do. This helps with that, obviously. 2. things I don’t want to do a lot of but have to push myself away from (e.g., web surfing). 3. things I want to do and have no trouble doing. But the recording system is binary. What do I do with activities in the third category? Eventually I decided to put the short-duration examples (e.g., standing on one foot, lasts 10 minutes) in the first category (counts as work), keeping the long-duration examples (e.g., walking, might last one hour) in the second category (doesn’t count as work).
Before I started this I thought of a dozen reasons why it wouldn’t work, but it has. In line with my belief that it is better to do than to think.
David Charron on Attention Tracking
Do you have the energy to do everything but the focus to accomplish nothing? David Charron of UC Berkeley studies multi-tasking, distraction and sustainable attention. He has experimented with quantifying his own attention, and compared himself to a long-time meditator. Check out his results and the interesting audience questions in the video below. (Filmed at the Bay Area QS Show&Tell meetup on 3/24/11 at TechShop.)
David Charron – Attention from Gary Wolf on Vimeo.
Percentile Feedback and Productivity
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.
Just do it? But HOW? 24 productivity experiments I tried, plus a QS time management recap
Some time ago I was asked for the ultimate productivity tip, and instead of giving a straightforward take-away, I said that in the end the answer is “it depends.” That wasn’t a cheap shot because what works for you might not work for the next guy, and vice versa. Sound familiar? It’s the same case for medications, meditation, and most anything else we humans do. That’s why it’s best to experiment, examine your results, and decide based on the data. In other words, quantify!
But there’s a complication. Coming up with metrics that reflect the value of what we do, rather than the individual efforts, can be a challenge. While the latter are simpler to measure, (there’s a reason that some jobs require you to clock in – “seat time” is an easy metric), the real test is more how effective we are, not just how efficient. I may be cranking widgets at a fast pace, but what if I’m making the wrong ones?
Until we have general-purpose and quantified framework for measuring value (“accomplishment units?”), we have to keep being creative. In this long post I want to seed some discussion by sharing two things: some specific productivity experiments I’ve tried, with their results, and a recap of the cool productivity experiments found here on Quantified Self. Please share techniques that you’ve found helpful.
Productivity experiments I’ve tried
Adopt a system. The single biggest productivity change I made was trying a system for organizing my work. In my case I got the GTD fever (Getting Things Done), and my results were clear, including getting far more done more efficiently, feeling more in control, and freeing up brainpower for the big picture. At the time (five years ago) I wasn’t thinking of it in terms of an experiment, but it certainly qualified. From a QS perspective it can function as a kind of tracking platform because it has you keep a comprehensive and current list of tasks (Allen calls them “actions”). I have used them for various tracking activities, mainly by characterizing or counting them.
Two-by-two charting. I’ve plotted 2D graphs of various task dimensions to analyze my state of affairs, such as importance vs. fun (a sample is here). These are a kind of concrete snapshot that I analyze over time. In the above example I decided that the upper right quadrant (vital + fun) was still a little sparse.
Discuss: The Quantified Worker
While much of our work here is focused on individual development, there are plenty of circumstances in our professional lives where we can apply the ideas of experimentation. Let me set the stage with some background and ideas, and then I’d love to hear from you on how you widen self-tracking to apply to your occupation.
First, experimentation at work is not new. Frederick Taylor‘s Scientific management popularized applying metrics to factory worker performance in the late 1800s. Later came W. Edwards Deming, who influenced the Japanese Lean manufacturing movement in the 50s, which integrated experimentation, measurement, and continuous improvement. A more contemporary thinker is Thomas Davenport and his ideas on How to Design Smart Business Experiments (an excerpt of a paid article).
Colin Schiller on Time Management Experiments
From the New York QS Show&Tell meetup group – Colin Schiller talks about how his productivity changed after having a baby. He experimented with using the Pomodoro Technique and only working eight hours a day. For four weeks, he tracked all the work activities he did in each 25-minute work segment. Watch the video below to see what Colin learned about his maximum productivity and the surprising reaction from his wife.
Colin Schiller – Time Management Experiments from Steve Dean on Vimeo.


















