| Factor | Effect | Impact / SD | Strength | r | Conf. |
|---|
Effect: whether more of this factor tends to raise or lower pain, from a multi‑factor regression (controls for the others).
Impact / SD: pain points (0–10 scale) change for a 1 standard‑deviation change in the factor — a fair way to compare units.
Strength: random‑forest permutation importance — how much the model relies on this factor, capturing non‑linear effects.
r: simple pairwise correlation with pain.
Conf.: our confidence, blending statistical significance, agreement between models, forest importance and how much data you have.
Same‑day analysis misses anything that hits with a delay (post‑exertional crashes, migraine build‑up). A separate pass checks each factor a few days back, after controlling for your own recent trend, so a slow drift isn't mistaken for a driver. A confound note appears when two factors move together closely enough that we can't confidently credit one over the other from observation alone.
How your habits cluster, and how much of the day‑to‑day variation a few patterns capture.
Log ~15 days to unlock experiments.