Tag Archives: pomodoro

2015 QS Visualization Gallery: Round 4

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 1Part 2, and Part 3 as well!

daily habits Name: Damien Catani
Description: This is an overview of how I have been doing today against my daily habit targets. Yes, I had a good sleep!
Tools: I used a website I’ve been building for the purpose of setting and tracking all goals in life: goalmap.com

 

tock_b_tock_goal_page Name: Bethany Soule
Description: This is my pomodoro graph. I average four 45 minute pomodoros per day on my work, and I track them here. This is where most of my productivity occurs! There’s some give and take.
Tools: The graph is generated by Beeminder. I use a script I wrote to time my pomodoros and submit them to Beeminder when I complete them. The script also announces them in our developer chat room, so there’s also some public accountability there as well.

 

 

qs1 Name: Steven Zhang
Description: This plot shows the time I first go to sleep, against quality of day (a subjective metric I plot at the end of every day). What this tells me is that if I get a full night’s sleep of 8 hours, for every hour I got to bed, I can expect a .16 decrease in my QoD rating, which, given my range of QoD around 2 to 4, is about a 5% decrease in quality of day.
Tools: Sleep as Android to track sleep and some python scripts for ETL.

 

qs2Name:Steven Zhang
Description: Log of all my sleep for the last 6 months, labeled by the types of sleep I most often encounter

  1.  Normal sleep
  2. Napping
  3. 3. Trying to achieve normal sleep, but failing to

Tools: Tableau for visualization. Sleep as Android for logging sleep.

 

Digits
Name: Eric Jain
Description: Benford’s Law states that the most significant digits of numbers tend to follow a specific distribution, with “1″ being the most common digit, followed by “2″ etc. But my daily step counts show a slightly different distribution: The fall-off from “1″ to “2″ is larger than expected, and the frequency of digits larger than “5″ increases rather than decreases. Is this pattern typical for step counts? Could suspicious distributions be used to detect cheaters?
Tools: Fitbit, Zenobase, Tableau

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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Brian Crain on Optimizing Productivity

There are many people in the QS community who are fascinated by understanding productivity. We’ve featured many different talks that explore different methods for tracking and hopefully improving productivity. At the 2014 Quantified Self Europe Conference we were happy to continue this exploration with a show&tell talk by Brian Crain. Brian has been thinking about his productivity since 2011. He tried a few different methods, but he’s found that using the pomodoro technique has been very helpful in understanding and improving his work. Watch his talk below to learn what he found by tracking the number of pomodoros he completes each day and what new methods he’s using to make sure he gets things done.


You can also view the slides here.

What did you do?
I started tracking my work time using the Pomodoro Technique in 2011 and have been logging all my sessions since September 2012. While, I have kept experimenting with different productivity methods, my consistent usage of the Pomodoro Technique has given me a great view of changes over time. I also discussed my experience with tracking my commitments over the past months.

How did you do it?
For the Pomodoro Technique, I would set a task, work on it for 25 minutes, then log the task. Over time, I built a large excel sheet that automatically updates with a variety of metrics that tell me how much productive time I spent working and how that has changed over time. For the commitment tracking, I would use an agenda, where I write down all commitments. I would then cross out completed commitments and track my compliance at the end of each day.

What did you learn?
I learned that having a continuous metric is enormously motivating since it allows you to continually improve yourself. These small, continuous changes make a huge difference over time. I also learned that building a user-interface is tricky, but very important to make tracking rewarding. This is something I successfully did with the Pomodoro Technique, but have found difficult to replicate with other methods. Finally, tracking commitments has taught me how critical one’s mindset is. When I would slip into thinking of commitments as simple tasks, my success with that method derailed completely. So for that method, I realized how important it is to build a system and user interface that helps maintain the commitment mindset.

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