Podcast about Quantified Self

I have a podcast with my friend Ivanka and this week I convinced her to do an episode about QS.

I wanted to share it here as I’m sure people will have something to say!

I was especially interested in the discussion of happiness tracking at 21:00. I agree that it is better to have consistent, specific tracking practices rather than lots of detail that’s taxing and tends to break down. One issue I face in my mood tracking practice is that I’ve become curious/skeptical about the period of time I’m evaluating when I log my mood. Is it how I feel right at the moment I’m recording? Is it how I’ve been feeling in the past 5 minutes? How my day has been so far? I’ve started to notice how different my rating would be if I were to make the evaluation range wider or narrower. Currently I’m using https://www.imoodjournal.com/, but I think I may move to a single rating once in the morning: How do I feel upon awakening?

Thanks for listening @Agaricus. The happiness tracking consideration is something I’ve put a lot of thought into and I’m a big proponent of randomised sampling so that it is only ever necessary to think about the present moment. Once we start trying to retroactively report emotions we will start getting very cloudy data. This means that sometimes intense emotions get missed but over long enough timescales, you’ll hit enough to create a representative data set.

I agree that there is a good, coherent concept in random sampling, which is why I’ve been using imoodjournal; it has a convenient design that allows you to specify daytime hours and total # of samples per day, and then it randomizes the notifications. It also has good data access. I learned about it from @madprime, who has also used it.

However, in practice, doubts are gathering. Here’s what happens when I get a notification:

I ignore at least 50% as ill timed. (Busy, don’t have phone handy, etc.)
When I open the app, I look at the scale and think: How do I feel? After several months of doing this, I noticed that my ratings were dependent on how I handled this reflection. If I thought to myself, how do I feel at this very moment, am I good, ok, bad; I had almost the same rating all the time, with the exception of a few situations of highly noticeable distress from illness, injury, or a bad context of some kind. I don’t really need mood tracking to tell me that if a family member is injured, for instance, I’m going to feel bad. AND YET… though I’m grateful to be fairly stable emotionally as measured by these samples, I also have a sense that my emotional life has a much greater range in fact. When I’m not measuring, I notice a lot of ups and downs. And I’m still curious about these. In terms of a protocol, if the act of measuring itself has a calming or “averaging” effect on my observations, then I might as well measure less often. That’s why I may go to a 1x per day measurement as a regular practice. BUT, I’m still very interested in lability, and I don’t have a measurement approach to that yet.

Hi, I’ve been thinking about this for a few days now. My feeling has always been that a lot of the friction comes down to subtleties of design. I’ve always worked hard to minimise the effect of measuring mood on mood itself. No smiley faces on icons, no prompting of emotions that might not exist, being careful not to show any particularly miserable older entries without the user indicating that they want to see them. I’ve relaxed this a little with Changes with some greyed-out smiley faces but I still try to be careful. Something that gives you a little bit of work to do every day needs to use design to absolutely minimise and possible friction.

A key benefit of mood tracking is precisely the ‘leveling-off’ effect that you describe. I’ve tried lots of different cadences and while multiple times a day is fun to start with, I’ve found that a single, randomised, daily measurement is the optimum frequency.

Meanwhile, I think our brains are pretty good at remembering rapid changes in mood and extreme events, so it’s the more day-to-day, slow and insidious changes that an app can help us keep an eye on.