Similar to @Agaricus and @madprime I'm currently preparing some DIY Experimentation I want to perform over the next weeks. My goal is to look into whether listening to music influences my mood. As an experimental setup I want to alternate between days in which I'm allowed to listen to music ad libitum and days in which I'll not listen to music at all.
- Test between two conditions:
- Condition A: listen to music during the day as I'd usually do
- Condition B: don't listen to music at all
- Alternate every day (ABABAB), but I haven't decided on a length of the experiment yet
- Track "adherence" (sticking to plan) by looking at my Spotify listening history that I'm already importing into Open Humans (This would also allow for a non-binary analysis of data as I'll have data about what I'm listening to and how much)
- Track "outcomes" via Mood tracking through iMoodJournal (I've settled on this thanks to the work of exploring mood tracking apps done by @madprime).
Over the last few weeks I've done so preliminary mood tracking already just to get a hang of it and get some baseline data. During this I've discovered a couple of things related to the mood tracking:
- First thing I noticed was that there seems to be a intra-day correlation between time of day and mood: Basically my mood seems to improve over the day. I think this is partially an effect of being stressed in the morning when waking up to the email deluge that came in from different time zones while I was asleep.
- my initial plan was to record only 3 mood data points per day (morning, afternoon, evening) triggered by notifications on my phone. I thought this would be easier to stick to, as it avoids notification fatigue. Instead I noticed that I still miss many of the prompts. I think it's most likely when the prompts come up at a time where I'm not having time to watch my phone. And by the time I get back to my phone the notification has virtually disappeared as it's so far down the list of notifications that I forget about it.
- To avoid this problem I've increased the frequency of reminders to an hourly notification between 08:00 and 22:00. This seems to work better as I get more data points per day.
- Another problem I encountered over the last few days: I'm highly likely to miss data points when I'm out for meetings or being around friends etc. I think this might become problematic, as my gut feeling is that these missing data points are not of a random mood but would be biased as I'm more likely to be in a good mood when hanging out with friends etc. This might not be a problem for this experiment as long as there's no difference in whether I'm meeting people between the two conditions though.
I was wondering how other people are approaching their mood tracking to get good data?!
For now my plan is to record some more days of base-line data with the higher sampling interval to get an idea of a 'neutral' distribution before starting the experiment. I'll keep you folks updated!