The only app you will ever need is “Google Sheets”.
Its UI is easy to use on a phone and syncs instantly, so you can use a computer as well.
It is completely customizable. You enter any sort of info you want and analyze it later over time.
I started using it ~3 years ago as a time-sheet to track my work, activities, sleep, mood etc. I tried to do what you did—see how X impacts my mood and look for trends. Here’s some of my findings over the past three years in that regard:
The most helpful thing has been a daily “end of day” entry for:
What went well?
Where did I come up short?
What one easy step could I take to improve where I came up short?
What is something I am grateful for?
What is my plan for tomorrow morning? (Food, Work, Routine, etc.?)
What improves my overall mood the most?
Gratitude journal entries 2x/day (morning & evening)
What influences my energy throughout the day?
Carb-heavy lunches. Obvious in hindsight.
30-mins of relaxation (e.g. web-surfing) are as fulfilling/energizing as 2-4 hours. Use Pomodoro (or the time-sheet) to shame you into getting back on task.
Weekly reflections on your time sheet and what went well/bad are very helpful and make you remember much more of your life that you would otherwise forget without realizing it.
“Chaining” tracking how many days in a row you’ve maintained a new habit can be very helpful to keep the new habit going.
Tracking too many things can derail you so keep it small. (e.g. Ben Franklin style “virtue journal” was not much of a value-add for me and takes too much time to be worth it).
Correlation is linear. Many causal functions can be non-linear.
Think of medicine. X is the dosage, Y is the improvement of health. If the dose is too low, you will get no response. If the does is within a good range, health improves. If the does is too high, you will begin to get even sicker. If data was gathered all along this inverted parabola, the correlation might be zero. But there is still a causal relationship between health and dosage.
Thus you can have causation without correlation.
You can probably think of many such functions with diminishing or negative returns as the dosage increases, e.g. years of education vs. lifetime earnings.
Whether you see a positive, negative, or null correlation can depend on where you sample from the response function. In the “real world” data might be bunched up around certain regions of the response function. Thus for the “average person/instance” you can determine if there is a correlation or not, and then say this is basically the causal effect (for the average person/instance).
But if you want accuracy and precision over concision you will use a more complex model.
Concise models are better memes than complex models, however, and so we are flooded with linear models or binary models.