Luteal phase, viral illness, alcohol, heavy training blocks, travel, and sleep debt produce biomarker signatures almost identical to underfueling. Here's how to tell them apart.
ACWR catches load ramps but misses the shape of the training week. Monotony and strain fill the gap by flagging weeks without enough hard/easy variance.
Rolling-average ACWR creates week-boundary artifacts. EWMA ACWR smooths the signal, handles missed sessions, and produces trend information that matches physiology.
Self-reported RPE drifts with fatigue, stress, and experience. Calibration against objective markers (velocity, RIR, recovery) catches the drift.
How per-muscle volume tolerance, recovery half-lives, and Bayesian load models translate raw wearable data into actionable training prescriptions.
The acute-to-chronic workload ratio is the single best predictor of training-related injury. Here's what it measures, where the 0.8-1.3 sweet spot comes from, how Omnio calculates yours, and the mistakes that get people hurt.