This is the internet weblog of Bix Frankonis.

Mood Tracking For May 2018

Last month, I posted a comparison of how my daily average moods were working out between March, the final month at my six-month job placement which I left due to the psychological damage it was doing, and April, my first post-job month. Today I add the full month of May to the mix.

Since the end of February I’ve been using an app called Daylio to track changes in my mood during each day, or changes in the activities in which I was engaged which could have an impact on my mood. Each entry gives me five mood options: Awful, Bad, Meh, Good, and Great. I’ve never actually used the latter, and the former I reserved for actual emotional breakdowns of the type I’d never before experienced prior to this job placement.

In both the calendar view (above) and chart view (below), Daylio is listing the average mood experienced on each day. Over the course of these three months, I very much did had a number of Awful moods but since Daylio averages each day, no single day gets listed as Awful overall. Typically, days with Awful incidents have a signficant amount of Bad moments as well as some Meh, which together tend therefore to average those days into being “merely” Bad.

These averages shouldn’t suggest that I didn’t have any Awful moments; my breakdowns tend to last no more than half an hour, and usually they’ve lasted “only” for maybe fifteen minutes (I had some in both April and May that only lasted maybe five minutes), with the rest of day then passing as a string of Bad moods across the various remaining activities of that day. Still, the daily averages are instructive, as both the calendar and chart views show pretty clearly.

My days primarily remained Meh, and that count was higher than the prior two months. After my Bad days dropped during April, they remained steady in May, while Good dropped — only five Good days for all of May after eleven for April.

Remember: these are the daily averages. There were Bad and Awful moments within certain days. You’ll see the counts of moments at each mood later. Since I begin using the app, I’ve never listed a Great moment, but that doesn’t bother me as long as I’m avoiding Awful moments.

As I said before, I’m maybe more after a middling stability than worrying about having intermittent joy.

I find the chart view more easily-illustrative of the differences. It’s evident that April on average was much more stable, and far less dramatic in its fluctuations. May, however, fluctuated widely again, although within a narrower range of daily averages than March.

Finally, new this month I am adding a comparison of the total count of each mood for each month. One of the drawbacks of Daylio’s use of daily averages in its calendar and chart views is the potential for misrepresenting how any given month went. Since both the calendar and chart average each day’s mood, it can be unclear that Awful moods in fact do happen.

This view does help show the reasons for thr wild fluctuations back in March, with its sheer numbers of moods registered. It also shows that Meh is the most stable mood across April and May. What can’t be captured by any of these views in Daylio is that the two Awful moods in March — the mood I reserve for the emotional breakdowns (“depressive episodes” in the words of my psychotherapist during that period) that come when I hit the extreme reaches of autistic overload — is that March’s breakdowns lasted more than just a few minutes, whereas that tended to be the length in April and May.

There remain a number of fairly disruptive and therefore potentially stressful transitions in the coming months: my nonprofit will undergo a major relocation; I myself might be moving to accommodate that relocation; and I’ll need to be making some decisions about applying for Social Security Disability Insurance versus returning to Vocational Rehabilitation to try again.

I’m not entirely sure yet what tracking my moods will get me in the long term, but I also don’t believe that the short term yet provides enough of an aerial view to start to draw any real conclusions. I do, however, find the tracking process itself to be useful.