  The distinction between "healthy" and "unhealthy" patients is commonly based
on single, discrete values taken at an isolated point in time (e.g., blood
pressure or core temperature). Perhaps a more robust and insightful diagnosis
can be obtained by studying the functional interdependence of such indicators
and the homeostasis that controls them. This requires quasi-continuous
measurements and a procedure to map the data onto a parsimonious control model
with a degree of universality. The current research illustrates this approach
using glucose homeostasis as a target. Data were obtained from 41 healthy
subjects wearing over-the-counter glucose monitors, and projected onto a simple
proportional-integral (PI) controller, widely used in engineering applications.
The indicators quantifying the control function are clustered for the great
majority of subjects, while a few outliers exhibit less efficient homeostasis.
Practical implications for healthcare and education are further discussed.
