A temporal hierarchy is not a causal proof
Behavior can change within minutes or hours. Autonomic and sleep-related signals may respond over hours to days, while aerobic fitness reflects a longer adaptation window. Cardiometabolic measures can reflect both acute variation and slower underlying state, and are often observed less frequently.
Putting these domains in temporal order is therefore an interpretive framework. It should not be read as a deterministic claim that every behavioral change causes a predictable next-day physiological response and then a cardiometabolic change.
Why one undifferentiated score can hide information
If all data are immediately collapsed into one number, the user may not know whether a movement came from a modifiable exposure, a Functional signal, or a slower clinical anchor.
Domain decomposition preserves that distinction while still allowing a composite summary.
Digital biomarkers make timescale explicit
Recent aging research increasingly treats digitally measured physiology and behavior as longitudinal biomarkers rather than isolated readings. The value of high-frequency measurement is precisely that trajectories, responses, and persistence can be studied over time.
That same richness creates a statistical obligation to distinguish short-term variation from durable change.
BioMIR’s framework
BioMIR organizes Adaptive BioAge as Behavioral → Functional → Cardiometabolic, with Clinical Long View outside the daily composite. The ordering describes characteristic timescale and analytical role, not a proven causal mediation pathway.
A future validation objective is to test whether the temporal decomposition improves interpretation, reliability, and longitudinal convergence with established clinical measures.