Awais Hussain on Keeping Track of Time

Awais Hussain is a student at Harvard, and he found himself asking the age-old question, “Where does my time go?” Using his online calendar Awais started tracking his daily activities. This tracking has given him some interesting insights into how he really works and accomplishes tasks. Watch this great talk to hear more about what Awais learned by keeping track of his time. (Filmed by the Boston QS Meetup Group.)

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6 Responses to Awais Hussain on Keeping Track of Time

  1. twinsenwu says:

    there are plenty of time event logging apps out there, I have been doing time event logging for more than two years now. you may want to check out iphone app “atimelogger 2″

  2. Gustavo says: makes this kind of tracking very convenient. I’ve been using it for a few weeks now, and there’s very little overhead to it.

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  4. Joe Zaczyk says:

    It always amazes me that we spend ~25 years of our life sleeping. Imagine what we could accomplish if we were instead awake?! For time-based tracking, check out Loggers are tracking the time they spend sleeping, watching TV, meditating, earning continuing professional education, exercising and my personal favorite… calling their parents.

  5. Aaron Bohannon says:

    It’s interesting to hear someone else’s experience with time tracking. I did it for a full year — all of 2012 — using an app for Android called Dr. Timer (which is no longer supported and doesn’t work on newer phones). I think that logging your time in the moment is probably a somewhat different, more intrusive experience. I made it work by carefully defining about 12 clear-cut categories of activity and using an app that made it easy to do fix-ups after the fact.

    As the speaker noted, the biggest challenge is getting meaning out of the data — both deciding what visualizations would be useful and figuring out how to make them easily. Eventually, I decided that I cared more about trends then absolute numbers. To summarize and visualize a big-picture trend I decided I should calculate the percentage of time I spent on each activity over increasingly larger, non-overlapping periods of time in the past. For instance, I would compute this kind of sequence: What % of my time did I spend sleeping over the past week? Over the past 2 weeks before that? Over the past 4 weeks before that? Over the past 8 weeks before that? etc. That way you know if things are out of whack with what is normal, and if so, whether it is a due to a trend. I really like seeing things broken down like that, but I never found a great way to visualize that information while simultaneously visualizing the relative time spent in each category — let alone a way to quickly churn out those visualizations each week.

    Currently, I’m only tracking my time spent sleeping at night. I’m using Gleeo Time Tracker (for Android) because it integrates closely with Automagic (, which allows you to set up arbitrarily complex triggers to perform actions on the phone. So for instance, it will start tracking my time asleep when my phone reads a particular NFC tag that I have on my bedside table _and_ my phone is currently plugged into a charger. It will stop tracking my time in bed when my phone is unplugged from the charger. Automagic has a vast array of possible triggers (virtually any event your Android phone can detect) and is a killer app for any kind of self-tracking since it can, among other things, send an email or SMS in response to a trigger. In fact, you can have it send an SMS to IFTTT (, which will in turn add a row to a Google spreadsheet (that includes the current date/time), so you can probably get by entirely without the Gleeo time tracker on your phone if you feel inclined. Moreover, if you use Google’s chart API, you can build a web page charting the data on any Google spreadsheet. I don’t do that for time tracking but I’m starting to do it for some other self-tracking. So, getting custom-tailored immediate visual feedback for self-tracking actually _is_ possible if you’ve got enough patience to set up this sort of cloud-based Rube Goldberg machine.

    – Aaron

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