Glucose needs to be monitored continuously in blood but this is-unfortunately- impossible due to many reasons related to blood environment so, CGM monitor it within ISF (liquid located between cells and and bathing them) then applies certain algorithm and mathematical equations based on retrospective data and glucose rate of change so the obtained results could be -somehow-close to the actual value in the blood .
Glucose monitoring is done through various mechanisms ,all aim to create a signal which can be -figured out - and -in the same time -proportional to real glucose concentration, and the most a common method is the the amperometric method which involves a chemical reaction characterized and controlled by specific substrates in order to yield electrical current related to glucose concentration,
Usually ,this current may result from interference as well ,consequently results could be misleading ,so order to minimize unreal contrition , certain process need to be applied like smart algorithm ,noise reduction software like "kalman filter" and retro analysis of previous readings: before representing final reading .
moreover,sensor stability manufacturing plays paramount role in order to make sensor readings more reliable and stable during wearing :
certain polymers and mediators may be involved in sensor miniaturizing to enhance its overall performance .
In conclusion;
Two variables govern glucose movement :
the first one is:
glucose dynamics which are so complex cannot anticipated all the time since many factors affect its movement like cell wall,food glycemic impact ,permeability and concentration gradients (remember that :glucose is transferred passively and actively within and between different compartments !!)
while the second one:
sensor structure which is -somehow - can be modified and enhanced by extensive research and utilizing plenty of (insilico data and virtual scenarios )
it seems the second one can be much well controlled than the first one ; so CGM/FGM companies concentrate more on that part for the sake of attaining acceptable accuracy level :"
smart sensor concept" : denoising , accuracy enhancement and prediction improvement. in addition to calibration times reduction or even diminishing since calibration done by users are considered a major source of sensor signal drifting .
keep in mind that there is uncertainty impedes within glucose behavior so anticipation is not -always -optimum solution-; careful observation ,data mining are needed most of the time ....
Glucose- usually- follows similar trend within the same person and under ideal circumstances - !!
To be honest -when talking about glucose sensors we can view discuss them from different angels :
1-mechanism of action
2-invasiveness
3-adjunctive and non adjunctive
in general, lets review this article -in brief - :
Glucose Sensors: A Review of Current and Emerging Technology
