What outside factors affect your productivity?

Around the RescueTime offices, we’ve been talking a lot lately about the external factors that influence your time on the computer. RescueTime is pretty good at helping you understand what you’ve been doing, but there’s a bit of a blank spot when it comes to the question “why were you doing it?”

Last week, I saw this tweet by one of our users:

A similar sentiment is echoed in this article from the Wall Street Journal a couple weeks ago, which poses questions like:

“Suppose they (workers) could tell how much an afternoon workout boosts their productivity, or how much a stressful meeting raises their heart rate.”

It got me thinking about all the different data streams that are currently piling up around our activities, and how there’s probably a ton of interesting information that jumps out if you can mash them all together. It’s getting easier and easier to amass these piles of data, but unfortunately they tend to be fairly siloed off. Here’s a few that seem really interesting to me:

Physical activity:

I’ve used a Fitbit to track more or less every step I’ve taken over the past 2 years (just about 6 million steps). Lately I’ve been using it to keep a really close eye on the time I spend sitting (turns out it’s WAY more than I’d like). I’ve noticed a somewhat counter-intuitive insight with my RescueTime data. I actually do more fulfilling work on days when I’m the most active.


I’ve tracked as much of my music listening history as possible since sometime in mid-2005. I haven’t gotten around to doing it yet, but I’d love to do some analysis and see how my activities impact my listening habits, of vice versa.

Sleep cycle:

There’s a bunch of devices that have come out recently to measure your sleep. Everything from free apps you can download on your phone to headbands that monitor your brainwaves. Personally, I use my Fitbit. It comes with a wrist-strap that you wear while you’re sleeping that measures your movement. I learned that I don’t really sleep as much as I’d like. I haven’t uncovered any unexpected insights about how that affects the rest of my behavior… yet.


This one isn’t so much a personal data stream, but there’s ample data out there, and I think it’s pretty interesting. Especially living in Seattle with the long, dreary winters.

What data sources about yourself would you like to see mashed up? What do you think you would learn from it?


    1. I agree. It’s a shame that it takes so much effort to reliably track (at least it does for me). I haven’t found a great way to keep up with it, short of entering stuff in a food log after every meal. I forget about it way too often.

  1. Thanks for the post. Here’s my takeaway and a shortlist of what I track.

    It reminds me how valuable it is to look at time comparisons and not just frequency. For instance, Last.fm records most of my music listens, but I’ve only really paid attention to charts relating to frequency of a particular artist or song. If I started considering when I listened to certain music it might yield insight into concentration levels. And I don’t allow it to track podcasts or audiobooks which might also be a factor.

    In addition to Last.fm for music, I use Sleep Cycle (iOS) for simple sleep activity, Google Calendar as a daily task/accomplishment journal, and RescueTime while at work.

    —Sleep Cycle stays on without my phone locked at night and sits face-down on the edge of my bed to gradually wake me according to my sleeping activity.

    —Google calendar is used as a kind of daily journal with different calendars I can toggle on and off to view different types of work (as well as relative distraction levels while completing tasks). This has been a mainstay for many years but I have yet to do anything effective with the events history.

    —RescueTime has been invaluable in keeping me on task while at my work. I even manually do a weekly review to pull out efficiency percentages for each day and week overall. When it’s time for a review at work, I have great data to support successes as well as improvement areas.

    Hope this helps. I’m not a programmer, so my comparisons would involve a lot of hands-on work (image overlays for Sleep Cycle, ? for Google Calendar and Last.fm).

    I’d love to hear ideas how others compare the data they have.

    1. Last.fm has such an awesome data set. A couple years ago, I downloaded my complete history and mashed it up with a weather database to make a mix tape for rainy days. It was actually a really good mix. 🙂 It ended up being a bit of a nightmare getting the data out of last.fm in the way I wanted it, though. Hopefully that will get easier in the future. I can think of so many things I’d like to mash it up with.

      The Google Calendar example is really interesting. I think there’s a LOT of value in being able to layer qualitative data such as journal entries on top of a quantitative data set. Often times that’s where you’ll find the lightbulb that explains a weird blip in your data, or inversely, gives you a clue where to dig further into it for something interesting. In fact, the more I think about it, I can think of lots of cases where my RescueTime data would greatly benefit from at least some light annotations. hrm. Something for us to consider. 🙂

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