BACKGROUND: Extensive past work showed that noninvasive continuous glucose monitoring with a wearable multisensor device worn on the upper arm provides useful information about glucose trends to improve diabetes therapy in controlled and semicontrolled conditions. METHOD: To test previous findings also in uncontrolled conditions, a long term at home study has been organized to collect multisensor and reference glucose data in a population of 20 type 1 diabetes subjects. A total of 1072 study days were collected and a fully on-line compatible algorithmic routine linking multisensor data to glucose applied to estimate glucose levels noninvasively. RESULTS: The algorithm used here calculates glucose values from sensor data and adds a constant obtained by a daily calibration. It provides point inaccuracy measured by a MARD of 35.4 mg/dL on test data. This is higher than current state-of-the-art minimally invasive devices, but still 86.9% of glucose rate points fall within the zone AR+BR. CONCLUSIONS: The multisensor device and the algorithmic routine used earlier in controlled conditions tracks glucose changes also in uncontrolled conditions, although with lower accuracy. The examination of learning curves suggests that obtaining more data would not improve the results. Therefore, further efforts would focus on the development of more complex algorithmic routines able to compensate for environmental and physiological confounders better.
BACKGROUND: Extensive past work showed that noninvasive continuous glucose monitoring with a wearable multisensor device worn on the upper arm provides useful information about glucose trends to improve diabetes therapy in controlled and semicontrolled conditions. METHOD: To test previous findings also in uncontrolled conditions, a long term at home study has been organized to collect multisensor and reference glucose data in a population of 20 type 1 diabetes subjects. A total of 1072 study days were collected and a fully on-line compatible algorithmic routine linking multisensor data to glucose applied to estimate glucose levels noninvasively. RESULTS: The algorithm used here calculates glucose values from sensor data and adds a constant obtained by a daily calibration. It provides point inaccuracy measured by a MARD of 35.4 mg/dL on test data. This is higher than current state-of-the-art minimally invasive devices, but still 86.9% of glucose rate points fall within the zone AR+BR. CONCLUSIONS: The multisensor device and the algorithmic routine used earlier in controlled conditions tracks glucose changes also in uncontrolled conditions, although with lower accuracy. The examination of learning curves suggests that obtaining more data would not improve the results. Therefore, further efforts would focus on the development of more complex algorithmic routines able to compensate for environmental and physiological confounders better.
Authors: Mattia Zanon; Giovanni Sparacino; Andrea Facchinetti; Michela Riz; Mark S Talary; Roland E Suri; Andreas Caduff; Claudio Cobelli Journal: Med Biol Eng Comput Date: 2012-06-22 Impact factor: 2.602
Authors: Katharine D Barnard; Timothy Wysocki; Vanessa Ully; Julia K Mader; Thomas R Pieber; Hood Thabit; Martin Tauschmann; Lalantha Leelarathna; Sara Hartnell; Carlo L Acerini; Malgorzata E Wilinska; Sibylle Dellweg; Carsten Benesch; Sabine Arnolds; Manuel Holzer; Harald Kojzar; Fiona Campbell; James Yong; Jennifer Pichierri; Peter Hindmarsh; Lutz Heinemann; Mark L Evans; Roman Hovorka Journal: J Diabetes Sci Technol Date: 2017-04-03
Authors: Yirui Xue; Angelika S Thalmayer; Samuel Zeising; Georg Fischer; Maximilian Lübke Journal: Sensors (Basel) Date: 2022-01-06 Impact factor: 3.576