10 Months at Harvard, Quantified

I will soon reach the one-year mark of my fellowship at HMS, which seems like a fitting time to examine how effectively I have spent my time here so far. I have been a practitioner of self quantification long before the movement acquired its name, having tracked some aspect of my life since I was 16. Given the movement’s growing popularity, I thought it appropriate to share some of my life hacking experiments. My approach has cyclically peaked and waned in sophistication, something that I will expound upon later in the post, but I believe that the overall trajectory of my effort has been that of increasing usefulness. Any lifestyle change, particularly one that involves compulsive tracking of one’s behavior, ought to result in actionable information that is demonstrably useful and not merely be a quantitative exercise in vanity. In this post I hope to show that this can in fact be the case for self quantification.

There are two broad ways in which I use self tracking. The first and more important is as a tool to adjust my behavior in semi real-time, on the order of hours or days, to increase the efficiency and quality of my work. The second is more retrospective, where I analyze months or years worth of data, to reach conclusions about the long-term trajectory of my career and work habits. Below I will try to give examples of both.

Hours Worked

The first and simplest question to examine is the amount of time worked. Prior to the start of my tenure at HMS, and several times since, I set a target number of hours to work per day (varies depending on the day of the week). Below I plot the number of hours worked during each month of my job so far.

Hours Worked

For a given month, the “filled up” part of the rectangle represents the number of hours worked. A white band at the top indicates the number of hours I fell short of my target, while a red band indicates the numbers of hours by which I exceeded my target. For real-time behavioral adjustment, I have similar indicators for hourly, daily, and weekly tallies, and so I am able to adjust my work ethic almost in real-time; to work harder if I’ve been slacking off, or take a break if I’ve been overworking.

Since I’m showing several months worth of data, let’s take a retrospective look. So far this year I have more or less hit my target. In fact, summing the white and red bands above I find that I am off only by 5 hours or 0.3%, which is quite remarkable. The only way that I could meet a target I set almost a year ago with such frightening accuracy is because of the constant adjustments I make thanks to quantitative tracking. What is also interesting about the above picture is that the time-span of my adjustments can take months. I started off lazy, probably because I was still adjusting to the new job and to a new city, but as time went on I managed to make up for lost time and increase my efficiency. If I were to dig within a given month, I would find a similar tendency but on the timeframe of weeks. For example months 6 and 8 above look very tranquil, as if I hit my targets without skipping a beat, but digging inside reveals a very different picture:

Months 6 and 8

Month 8 looks reasonably calm. I started off a little bit under, but by the last two weeks I made up for it, similar to the months-long trajectory of my overall tenure so far. Month 6 is very different though. I overworked the first two weeks (week 1 is short because it’s just the weekend), crashed in week 3, and recovered in weeks 4 and 5. Lest someone laughs at the juxtaposition of “overworked” and “56 hours”, I should note that the above numbers represent hours of distilled work. Distractions like bathroom breaks, facebook/reddit, stretches, etc, are accounted for, and so in practice an “8” hour day corresponds to me being in the office for about 11 hours, and a “13” hour day corresponds to me working every waking hour of the day, including the daily bus ride. This in itself is interesting because it suggests that my efficiency, averaged over all types of work, is around 72%, something to which I will return in a bit.

A similar picture holds for hours within a day. Thus my behavioral adjustments are multi-scale, made in increments ranging from minutes to weeks, but in a basically subconscious way. I have a panel of indicators always open, and my brain internalizes what it sees and automatically adjusts.

Project-Based Analysis

Now that I know how many hours I spent working, the second obvious question is how those hours were spent. At any given day I am usually working on several projects, and I have set goals in the short-term (months) and medium-term (year+) for how my time should be distributed across these projects. Below I plot my intended targets (inside wheel) versus the actual distribution 10 months in (outside wheel). Numbers correspond to percentage of total time:

Untitled

On the whole these numbers look quite good. The agreement, if you ignore the yellow and green slices for a moment, is quite notable. I must reiterate that on a day-to-day basis the actual distribution looks nothing like this. It is all very chaotic. I do set aside time on weekly and monthly bases to make sure I’m not veering too far off from track, but looking at the aggregate 10-month data for the first time, I am taken aback by how much quantitative tracking has helped me meet my targets. For reference, this is what things look like plotted as a function of time, where each time point is the weekly average for each project.

Untitled

Nonetheless, what the summary distribution does show is that I blew it big time when it came to “Planning & Logistics”, on which I spent far more time than I had budgeted, and “Project 3” took the brunt of the hit. This information will be very useful to me in the coming months as I adjust my habits to correct this error. Coincidentally, when I split open the “Planning & Logistics” category, I see that a significant fraction went to one-time projects (daggered), an issue that I will come back to later:

P & L Breakdown

Work Type Analysis

Another way to slice the data is by the type of work done. Below is a breakdown, averaged over the 10 months, of how my time was spent in terms of work type.

Work Type

The categories are a little abstract and merit some explanation. “Thinking” involves the process of thinking/creating my own ideas, and includes actual thinking, coding, doing math, etc. “Reading” involves thinking too, but is restricted to the consumption of other people’s work, and includes, beyond reading papers, attending talks, watching online lectures, etc. “Writing” similarly involves thinking, but only of the type that is restricted to turning existing thoughts into the written word. The same is true for “strategizing”, and the rest are self-explanatory.

The above is a little grim or not too bad depending on your perspective. The core set of activities required for my scientific output are just “reading” and “thinking”, and constitute 48% of my time. Thus in a prefect world free from all obligations, including that of the dissemination of my work, I would be about twice as productive. Even if one concedes that dissemination is an integral part of the scientific mission, it would bring up the total to 59%. That leaves “logistics” (emails, meetings, …), on which a depressingly large amount of time is wasted, and strategizing, which while a fun mental activity, has consumed far too much of my time.

Instead of taking a summary view, one can also plot the number of hours spent as a function of time. I have a set of interactive gadgets that allow me to visualize all sorts of things. Below, on the left, I plot the number of hours spent per month on each work type category (color coding same as before), and on the right I plot the hours spent per week just strategizing.

Untitled

What’s clear is that most of the strategizing was done early on, when I first began my job and had to plan a number of projects simultaneously. It was more or less an upfront, one-time investment. Nonetheless, the above picture tells me something very important that I did not know before. That despite strategizing being a one-time investment, it was such a hefty one that even 10 months later, it shows up as 14% of my total time. This is an issue that I repeatedly notice. Things that appear innocuous, for example spending a week here or there focused entirely on doing one thing and thinking “it’s just a week”, can in fact throw off the balance of my work distribution for months to come. My mind is preconditioned to think that a week is not a large amount of time, at least in academia. But this type of tracking quantifies what a serious hit it is to dedicate an entire week, let alone a month, to a single task.

On the left plot one also sees two dips in thinking activity, one corresponding with a peak in reading activity and the other with a peak in logistical work. The first corresponds to a significant investment I made in learning a new mathematical method, and the second corresponds to a lot of bureaucratic work that was needed to enable students to join my group. This effort begins to bear fruit as my mentoring activity begins to grow in June, coinciding with the time when a few students joined my lab. Of course, I did not need to look at these plots to know what happened, but they do allow me to quantify the amount of time spent in each case.

Work Type Targets and Distributions

I do not generally set targets for work type allocations like I do for the projects. The only hard targets I currently have is to spend >10% of time writing, and >15% of time reading, which I have hit. This was something I struggled with tremendously before, and I only succeeded when I started tracking work type specifically.

I do however have a number of soft targets. For one, I try to do intellectually heavy work in the morning when my brain is freshest (thinking and reading), and leave the less demanding work for the evening. Below is the distribution, as a function of time of day, for each type of work.

Untitled

On the right I plot all aforementioned work types except reading, and on the left I zero in on thinking, strategizing, logistics, and writing. The first revelation is that my soft target is a complete fantasy. If anything, I spend more time on logistical work earlier in the day than I do on thinking. Writing and strategizing do have slightly higher peaks earlier in the day, which is news to me. These are all previously unknown facts about my work habits that I will now work to correct. In the right plot very different patterns emerge for mentoring, which is an activity that is still very much in flux, and socializing/networking, which as expected dominates in the afternoon. Just for fun, I radially plot the information on the left above on a 24-hour clock below. I do not find this visualization very useful, but I like to constantly experiment.

Untitled

Another soft target is to maximize the length of uninterrupted time for any given activity. This too can be ascertained from the data, plotted below, where I show the distribution of every work type as a function of the length of uninterrupted time spent.

Untitled

This is also unfortunately somewhat depressing, as the mode occurs somewhere around the half hour line. That means most of my activities run for about half an hour before being interrupted, most likely by my losing focus. On the positive side, more mentally intensive activities, thinking, reading, and writing, cluster in a group distinct from logistics and strategizing. They have a fatter tale that extends all the way up to 6+ hours, a state of true Zen that I achieve for only 0.79% of my tasks. I do however attain a more realistic state of 3 uninterrupted hours 5.4% of the time that I am reading.

The Technology

I will end with some remarks on the software that goes into making this possible. First off I want to qualify everything by noting that by far the hardest part of self quantification is not building the tools but the lifestyle changes, the nearly OCD-like tracking of one’s behavior, that I think presents the biggest obstacle to most people. Having said that, good tools make this process easier, by seamlessly integrating tracking within one’s daily minute-ly habits. The success and longevity of my various efforts over the years have correlated very strongly with how well the tool in question worked. I suspect that as wearable computing begins to make a serious dent in our habits, self quantification will begin to flourish even more.

When I first started seriously tracking things as a teenager I had a very elaborate and completely custom-built system, probably in the 10k+ lines of code regime. It told me way more than I needed to know about things I could do little to change, and consumed an inordinate amount of time to maintain. As I grew older and my responsibilities increased, I found it increasingly more difficult to dedicate so much time to this effort. For a while I dropped self quantification completely.

Mathematica + Outlook

The system I’m discussing today is built in large part on existing tools. All the raw data is stored as events in an Outlook calendar, which acts as my database (previously I used a real database). The analysis is all done in Mathematica, using custom-built code that I developed. The only “real” software engineering that I had to do was a custom-built C# DLL that maintains an active link between Outlook and Mathematica, and that allows me to track changes in Outlook in near real-time using Mathematica’s Dynamic functionality. From the start, I wanted the system to be about practical utility instead of cute but useless statistics. As a result, I would only build a piece of functionality when I found myself asking the same question over and over again (e.g. how many hours did I spend on project X this week.) This has led to me having to refactor the code twice now, and probably many more times in the future, but I have found this organic approach to be far more effective than my previous (more structured) attempts.

Workflowy

Beyond the quantitative tracking system, I do have a large note taking and qualitative planning system, built on top of OneNote, Workflowy, and Visio. My experience here again has been of relying less and less on custom-code in favor of off-the-shelf tools. There is one feature that I would love to see built either into OneNote or Workflowy, but that is a post for a different time.

Concluding Remarks

I must admit to feeling some hesitation before writing this post, as I fear it may come across as self-indulgent. My motivation in writing it was to communicate how useful self quantification can be. It is a lifestyle change that I believe some can benefit from, and one that can reduce the chronic stress from which far too many of us suffer.

Update: Hello Hacker News!


28 comments

  1. This is impressive! Is the requirement of getting OCD like capability to track your time in fact worth it ?also have you been able to pintpoint when those a-ah moments would occur ?

    Igor.

    • Thanks Igor! In terms of whether it’s worth it, for me it certainly has been, but that may not be transferable. I’ve been tracking myself for so long and since I was relatively young that it’s second nature to me, and consumes very little time. If I were just starting out I would be a lot less efficient, and it may be harder to justify.

      As for aha moments, the tracking doesn’t particularly help with that, in part because I don’t track such moments. But I am quite aware of when they occur, just because I’m generally introspective. I could write a whole post on that, but basically it happens when I’m relaxed, either early in the morning (shower), or as I’m dosing off to sleep. It has to be very quiet and still around me. And more often than not, I would get an idea that my brain is working very hard to reject, that I’m almost in agony because I just don’t want to think about it, but decide, ok, let me see if there’s anything worthwhile here.

  2. Hi Mohammed,

    How do you think the ‘measuring’ of what you spend your time one influences what you spend your time (and concentration) on? I understand you employ your toolchain to keep the overhead as small as possible, but it will never be zero.

    I myself am not a star at context switches, and after focussing my attention elsewhere it takes a while for me to get back in the flow.

    Did you notice an impact on your concentration since you started measuring so intently? Do you feel less ‘zen’, since you’re spending (a lot of? – is this measured as well?) time on the ‘meta’ question of what you’re doing, instead of actually doing it?

    Thanks for the read, it was really interesting.

    // Koen

    • Yeah this is a good question and I vacillate between spending too much and too little time on the tracking / planning side of things. I think that no tracking whatsoever would not be a good idea, at least for me. I do not feel zen when I’m just working away without thinking about the larger context. That would in fact stress me out. Tracking and seeing what I’ve been doing makes me much calmer, because at least it’s all there laid out.

      But it can be overdone, and I have overdone it, especially in the past in my late teens and early 20s where I became overly obsessive and this whole thing turned counterproductive. Nowadays I think the overhead, in terms of just the tracking stuff, is minimal, probably 1% of my time, and that’s something I’m trying to rein in.

      Having said all this when I’m focused on a particular task, for example reading a paper, I try to focus on that singularly and I don’t task switch, because that’s a terrible time waster. That’s why the figure on length of uninterrupted time distribution was rather disconcerting.

  3. Thanks for this post. You have me interested in self quantification.

    When you drill into each month, how is it that a month can have 5 weeks?

  4. Awesome and creative work! It’s very admirable that you chose to share this with the world, from those of us who understand and appreciate it Thank You =)
    You also did a great job explaining that in your blog with the especially nice figures. I can see many companies getting interested in this, which would probably upset people that prefer to slack around instead of do real work. Keep it up!

  5. What do you think of tracking tools like RescueTime? When you say work do you track both computer and non-computer activities? How do you track non-computer activities? Do you mostly track work or also other things like sleep, exercise, etc.? Do you worry about missing important factors due to flaw in data acquisition?

    Being a biology how did you get into programming and do you plan to use these tools in biology.

    • I actually didn’t know about RescueTime, but looking at it now it looks interesting. I have thought about relying less on subjective/manual categories and instead recording things like application open, document open, etc. The problem though is that the information that would come out of such data would be less actionable. A significant fraction of my time is spent in a handful of applications, and they do not fall cleanly into the categories I care about. So I have opted for manually recording and categorizing my tasks. But, it is something I may consider in the future, at least as a complement to what I’m already doing.

      As for your other questions, I track all work-related activities, computer-based or not. If I’m away from the computer I just use a mobile device to record things, and worse comes to worst, jot things down on a piece of paper! (which I would transfer later to the electronic calendar). I am not overly precise. Ultimately this is meant to be useful, not scientific, and if there are some inaccuracies here or there that won’t affect my conclusions then I don’t care.

      Finally, I’ve always been a computational person too (started programming when I was very young). My research is in computational biology, so the two go together for me.

  6. I would love to see a post or two on how you use OneNote. Googling for other researchers on how they use it to take notes, hasn’t given me much to go on. It would be highly appreciated.

    • I’ll try to do that at some point. There’s a lot to talk about and I’m not sure which topics are most interesting to people. I’ve seen lots of article online about note-taking tips, but I suppose none from researchers.

      • You are right, there is quite a bit of material online regarding OneNote.

        Yet I still can’t figure out how to use OneNote. So far I use a ‘database’ to store my PDFs and simple textfiles to store reviews of entire papers or just random bits on specific topics (e.g. I would have a single file on Feature Selection where I put everything on that topic in, no matter where it is coming from. Introduces redundancy if a bit is valid for more than one topic as I then ‘have’ to put it in more than one textfile). linking the bits in textfiles with the source is a bit of work too.

        And OneNote, while nice looking, does not seem to offer a solution that would combine those two things or make them easier to achieve.

        In case you ever get around writing about OneNote, here are some points that might be interesting:
        – how would the linking be done automatically/conveniently.
        – reviewing a research paper seems odd in OneNote too. Importing a PDF is cumbersome, extracting parts of it seems impossible (e.g. just a figure or table).
        – storing information on more than one topic seems to introduce redundancy or a very large array of topics on a single page
        – retrieval: it does not seem to search linked documents (even if its a PDF on my own disk), I never seem able to find anything quickly. ‘i died in 1950′, good luck searching for death ;)
        – do yo actually use it for creating content (blog posts, papers, …)?

        • Sorry I should’ve been clearer. In terms of paper-related notes, my system is a little more complicated. Notes specific to technical issues within the paper that are on the sentence/paragraph level all go onto the original pdf. I just use Acrobat’s note tools. In terms of managing/categorizing/tagging papers, I use Zotero for that.

          If a paper is sufficiently interesting that I feel the need to summarize it or just write about it in an extended format, then I have a specific section in OneNote for that. The key to make this work is that I use a single referencing number across all systems. The file name is just number.pdf, in Zotero the “Call Number” field is the number, and in OneNote/Workflowy/anywhere else the paper is [number]. So I can (logically) refer to it without ambiguity. The physical file is in a huge folder that contains all my other papers, but all the metadata is in Zotero and so it’s irrelevant where the file is physically.

          On occasion, but this is rare, I do find the need to aggregate notes about several papers in one place. This typically lands within a OneNote page for some project I’m working on, for example “Methods to do X”, and there I would just have a table or whatever that compares/contrasts the papers, referencing them using the [number] notation.

          I use OneNote for creating outlines and such, but if something is getting sufficiently complicated, it usually moves into its own separate file in the right file format, like a Word document for a paper or the relevant file format for code, etc.

  7. Thank you for sharing this. I find your approach very fascinating and would like to start something similar. Would you share your code? Another question I have, do you run any statistical analysis on your data, for instance to find out if sleep patterns, exercise or temperature/season have an impact on your productivity and how you spend the day?

    • Thanks for your interest! I will probably release my code in the future, but I’m not comfortable releasing it just yet because it’s still pretty raw.

      I have not tried to dig into the data to find correlations of the sort you suggested. It is something I have been wanting to do, but I have a very long todo list! :-)

  8. Great post & thanks for putting yourself out there like that! I’d love to pick your brain on a few things, as I’m building a QS system for myself with a focus on managing my ADHD and changing/creating related habits. Would you be up for a phone interview or two that cover some of the unanswered questions in other comments, along with some other questions of my own? I could transcribe them into blog posts to save you the time writing.

    • For recording I generally just use Outlook directly, although I’m trying to find the time to build a programmatic interface to manipulate the Outlook calendar from within Mathematica (right now it’s a one-way street) as I’d love to automate some currently mundane tasks.

  9. Really impressive work!

    II’m interested in the productivity/distraction part.
    You said you had a “productivity” of about 72%. I’ll guess that you made a modification on the Outlook calendar each time you were distracted. Is it correct or did you use another way?
    Were you able to isolate moment in the day that are more suitable for some type of work, such as moment with longer attention span?

    Also, your 30 minute attention span coincide with the basis of the Pomodoro technique (plan and separate work as 30 min intervals with 5min breaks between).

    • Thank you! Yes you’re correct, whenever I get distracted I mark it in Outlook. I have ideas for making this more efficient but haven’t had the time to implement them. Your question about isolating the time of day when I’m more prone to distractions is a good one. I haven’t looked into, but it wouldn’t be hard to do. The data is all there. I will look into it.

      I looked up the Pomodoro technique and it sounds interesting. Here’s what I’ve noticed. Certain tasks, like writing or pure thinking, I am most productive when I am completely lost in thought and without any distractions. The longest I can usually go for is about 3-4 hours, and after that I’m typically so beat that I am basically a vegetable for the rest of the day, just doing routine tasks. But, those 3-4 hours are gold and I wouldn’t trade them for the world.

      For other tasks, in particular programming and for example graphic design, I do actually need breaks. But the breaks have to be really short, probably 90-150 seconds max. Longer than that and I lose my train of thought. But very, very short breaks actually help my mind recharge somehow. The trick is to be disciplined about keeping it within the 90-150 sec limit. What I typically do is have tasks that can be done very quickly, like checking my twitter feed, and that I can interrupt at any moment. Also, I sometimes have long articles open in the background, and sneak in a paragraph or two every 20 mins or so.

  10. It is very interesting to take the time it takes us to complete our tasks; but the main problem always is the tools that can facilitate this process.
    As you mention Workflowy is a nice tool also exists ATaskBucket (ataskbucket.com) that is a desktop application that has more options to help us to collect our tasks and control the time that we employ completing them.


Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s