I’d like to see more examples of A/B testing, which I think is one of the best techniques. Something like Seth Roberts’ butter experiments (or my replication using fish oil) where you try something for a while, then a flush period, then try somethings. One nice thing about A/B testing is that there are easy statistical analyses you can do to measure how likely any effect is due to chance.
Sleep tracking is one of the most popular QS projects. I really like this summary from Tomáš Baránek, unless you’re you’re trying to stick to content from quantfiedself.com (I’d have to search, I’m sure there are great examples there).
That Azure Grant study of cholesterol throughout the day is a QS classic that shows the importance of sampling frequency. If that’s what you mean by “cadence”, perhaps that section could include that study as a warning about the importance of measurements at the right times of day, frequency, etc.
Instruments might include a discussion about the importance of consistency across devices. You can’t assume “heart rate” means the same across Fitbit and other devices for example.
A section on spurious correlations, plus something about how even a negative result is a good learning experience. (Any of the Zenobase home page examples are good: eg. phases of the moon).
This sort of a book begs for more discussion of the *how – people will want specifics about which app, which which device, nuts and bolts for specific experiences – but maybe that’s the next book!
p.s. note typo in Features section “explore it’s features”