Why compare wearable data to personal baselines instead of population norms?

Population norms flatten exactly the differences that matter in behavioral health: age, medication effects, fitness, comorbidity, and the wide natural spread of human physiology. Comparing a client to 'people in general' produces false alarms for naturally-outlying bodies and false reassurance for people whose numbers look average while drifting far from their own normal.

NavixPulse builds baselines with robust statistics over each person's recent history, so what counts as high, low, or drifting is theirs alone. Every surfaced observation carries the baseline and the deviation — the arithmetic is shown, not asserted — so the treating professional can weigh it with everything else they know.

This is also why the platform asks for a small amount of context at device connection (date of birth, sex, ethnicity, with 'prefer not to say' always offered): not to grade anyone against a demographic average, but to give clinicians and — with explicit organizational opt-in, in de-identified aggregate form — researchers honest context for what they're reading.