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September 26, 2011

3

Mood-Scoping

by brainoids

As a latest foray into lifelogging I’ve been playing with a tool called “Moodscope“, a lightweight daily quiz which tracks excursions into positive and negative moods.  It appears to be rooted in the established psychometric metric of the Positive and Negative Affect Schedule (PANAS).  In short, you take a short quiz, and get back a 0-100 score, with 50 being neutral, >50 being “positive affect” (er, “good mood”), <50 being “negative affect” (“bad mood”).   Faced with such a temptation, I couldn’t resist seeing if this was all hooey or if there would be signal in the noise…

To cut to the chase … much to my surprise, I could actually track long term trends.  Shown at left are both my daily and weekly-averaged data for the last three months.  Even without the weekly averaging the ‘envelope’ of the raw data suggested trends.   (I also have a pretty good intuitive understanding of the related causes, but there’s only so much I’m going to dump onto a public blog!)

Since much of my mood is driven by what’s going on at work (both negative, on the frustration side, and positive, on the “things that feed me” side), I next wondered if there were any weekly trends buried in the data.   Switch the smoothing filter from 7-day to 3-day and … voila! :

There’s the expected weekly cycle, hiding out in the raw data.   While I hypothesized there might be such a signal, I never anticipated it would be that strong.   But in hindsight it makes sense, my work-weeks, while not routine, do tend to share some general characteristics week-to-week.  Switching to a distribution-based view of the raw scores, the story becomes, “Tell Me Why I Don’t Like Monday’s” (that’s an easy one actually; it’s all staff meetings, ‘sync tags’, etc, with very few opportunities to interact with people in motivating or inspiring ways – all activity, no energy).

(For stats junkies, the indented boxplots are a cool graph the Mac stats program Aabel generates … fatter boxes at a given score mean more occurrences; diamonds show the mean value, solid horizontal bars show the median value).

Anyway, back to the data.   After lousy-Mondays, I typically get to work a lot more with people on, well, actually creating stuff, rather than managing it.   Whether or not I’m doing it myself or just coaching, this is what fires me up.   Of course, then the week trudges along, energy gets drained, stress goes up, backlogs grow, opportunities for conflict increase, etc, leading to not-so-pleasant Fridays.   By the weekend, I’m mostly in “recharge” mode (“meh”), hovering more around neutral values.  Unfortunately the PANAS positive affect questions all seem to be skewed towards very “active” positive values (“determined”, “inspired”, “interested”, etc); there’s no way to get positive affect credit for things like “cozy”, “content”, “relaxed”, etc).

As a final exercise I took the weekly data and simplified it down to a simpler user’s guide; for any given day of the week, how likely is it that I’m going to have a good day (mood score > 60), a bad day (mood score < 40), or a “meh” day (40-60).   Arbitrary thresholds, I just picked ones that divvied up the data well.   And yes, these are now posted on my office door (how’s that for biofeedback?!)

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3 Comments Post a comment
  1. Hi Dennis,

    Thanks to Google I’ve just seen and read this really great blog post on your experience with Moodscope.

    I’m the founder of the Moodscope project and I was totally intrigued with the analyses vou’ve carried out, and what you’ve discovered about your mood patterns.

    Actually I’m keen to learn more from you (especially as you really are a rocket scientist!) and was particularly intrigued with your second graph where you explain that you ‘switched the smoothing filter from 7 days to 3 days’. There’s clearly a very interesting rhythm showing up here but I must confess to not really understanding the smoothing filter idea. My math skills are definitely not up to NASA levels I’m afraid.

    I’ve no doubt you’re a busy man, but any light you can shed on this for me would be greatly appreciated. I have a feeling it could help me with the development of the Moodscope concept itself.

    Thanks Dennis, and thank you again for the super work you’ve already done.

    Best wishes

    Jon

    PS I tried emailing you but couldn’t get through, hence posting this as a comment – hope that’s OK.

    Jon Cousins
    Moodscope Limited
    St John’s Innovation Centre
    Cowley Road
    Cambridge
    CB4 0WS
    United Kingdom

    http://www.moodscope.com

    Reply
    • Sep 28 2011

      Hi Jon – wow, thanks for writing! Actually I may have oversold the “filter”, all I really did was take a simple, unweighted 7-day or 3-day moving average (e.g., 3-day Smoothed Day X Score = ((Day X-1 Score)+(Day X Score)+(Day X+1 Score)) / 3). There are other types of smoothing functions available which less strongly weight the endpoints of that window, but a simple average seems to work fine for me.

      I’m guessing the results will depend strongly on everyone’s own weekly or longer term patterns. In the first part of my record there was no cycle – I think I know the reasons for that, I was starting a new at our Center and there was a rush of energy, followed by a big crash when I first collided with our Center’s bureaucracy 🙂 What’s amazing to me is that once I settled into a semblance of a routine the signal became so pronounced, even despite a *lot* of spread in the day to day values. Averaging over three days was all it took to bring the clues out. My weeks are still pretty unpredictable so I think there really is something there! I’m really curious if there are also longer term trends – I read (glancingly) some stuff about longer 30-dayish cycles in males but don’t know how legit it is. But now there’s a tool to experiment with …

      Thanks again for providing the tool! … and for writing. I’m tickled to have caught the attention of the founder! I’ll Direct Message you my email on Twitter as well … just followed you there as @den_wa …

      Cheers,

      Dennis

      Reply
  2. Thanks Dennis, I think I can see what you’ve done – and it’s fascinating. For some reason we’re not connecting via DMs on Twitter. Maybe you could send me a quick one liner email so we can chat a bit more offline? I’m jon (at) moodscope (dot) com.

    Cheers

    Jon

    Reply

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