By providing neither bias nor lag information, we can produce an uncorrected signal y F G M, s m o o t h e d that has been subjected to all the processing of our method but has not really corrected for anything. By not providing the bias to the smoother we can produce an only lag-corrected signal, y F G M, L c o r r, to investigate the effect on MARD of only correcting for lag. We used Equations ( 3) and ( 4) in a Kalman smoother that use the participant lag, bias and FGM data to produce lag- and bias-corrected FGM measurements, y F G M, B L c o r r. The lag correction is more complicated, and can be done in different ways. The bias correction was done simply by subtracting the estimated bias b F G M per participant from all measurements y i F G M from that participant, producing y F G M, B c o r r. We investigated the effect on MARD of correcting the day 1 data for only bias, only lag, and both. Although the common usage pattern of FGM is different from CGM, in parts of this paper we treat the FGM data as if it is from a CGM, since the measurement principle is common between the systems, and our FGM data are frequently sampled. A consequence of this user-initiated data transfer between sensor and display unit is that the FL cannot provide alarm functionality, like CGMs do. The current glucose level along with historic glucose data for the last 8 h is displayed on the scanner/smart phone. A main difference between FGM and conventional CGM is in its usage FL only provides a reading when the user scans the sensor using a hand held scanner device or a Near Field Communication (NFC) enabled smart phone. The FL uses the same minimally invasive electrochemical sensing principle as conventional CGMs. A recent addition to the family of glucose monitoring devices is the Flash Glucose Monitor (FGM), of which there is currently only one system on the market-Abbott’s Freestyle Libre (FL).
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